<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fita , E</style></author><author><style face="normal" font="default" size="100%">Damrich, S.</style></author><author><style face="normal" font="default" size="100%">Hamprecht, F A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Algebraic Path Problem for Graph Metrics</style></title><secondary-title><style face="normal" font="default" size="100%">39th International Conference on Machine Learning, PMLR. Proceedings </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><volume><style face="normal" font="default" size="100%">162</style></volume><pages><style face="normal" font="default" size="100%">19178-19204</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Damrich, S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovering Structure without Labels</style></title><secondary-title><style face="normal" font="default" size="100%">Heidelberg University</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garrido, Q</style></author><author><style face="normal" font="default" size="100%">Damrich, S</style></author><author><style face="normal" font="default" size="100%">Jäger, A</style></author><author><style face="normal" font="default" size="100%">Cerletti, D</style></author><author><style face="normal" font="default" size="100%">Claassen, M</style></author><author><style face="normal" font="default" size="100%">Najman, L</style></author><author><style face="normal" font="default" size="100%">Hamprecht, F A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><number><style face="normal" font="default" size="100%"> arXiv:2102.05892</style></number><publisher><style face="normal" font="default" size="100%">arXiv preprint</style></publisher><volume><style face="normal" font="default" size="100%">38 (Suppl 1)</style></volume><pages><style face="normal" font="default" size="100%">i316-i324</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sitenko, D</style></author><author><style face="normal" font="default" size="100%">Boll, B</style></author><author><style face="normal" font="default" size="100%">Schnörr, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assignment Flow For Order-Constrained OCT Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">129</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">3088-3118</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sitenko, D</style></author><author><style face="normal" font="default" size="100%">Boll, B</style></author><author><style face="normal" font="default" size="100%">Schnörr, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assignment Flows and Nonlocal PDEs on Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">GCPR, in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gonzalez-Alvarado, D</style></author><author><style face="normal" font="default" size="100%">Zeilmann, A</style></author><author><style face="normal" font="default" size="100%">Schnörr, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assignment Flows and Nonlocal PDEs on Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">GCPR, in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haußmann, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Neural Networks for Probabilistic Machine Learning</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreas Blattmann</style></author><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Michael Dorkenwald</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Behavior-Driven Synthesis of Human Dynamics</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2103.04677</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Karsten Roth</style></author><author><style face="normal" font="default" size="100%">Samarth Sinha</style></author><author><style face="normal" font="default" size="100%">Ludwig Schmidt</style></author><author><style face="normal" font="default" size="100%">Marzyeh Ghassemi</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2107.09562</style></url></web-urls></urls></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ruiz, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep k-segments: a generalization of k-means</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bailoni, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Learning for Graph-Based Image Instance Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vijayan, A.</style></author><author><style face="normal" font="default" size="100%">Tofanelli, R</style></author><author><style face="normal" font="default" size="100%">Strauss, S</style></author><author><style face="normal" font="default" size="100%">Cerrone, L</style></author><author><style face="normal" font="default" size="100%">Wolny, A</style></author><author><style face="normal" font="default" size="100%">Strohmeier, J</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Smith, R S</style></author><author><style face="normal" font="default" size="100%">Schneitz, K</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule</style></title><secondary-title><style face="normal" font="default" size="100%">eLife</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fita, E</style></author><author><style face="normal" font="default" size="100%">Damrich, S</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Directed Probabilistic Watershed</style></title><secondary-title><style face="normal" font="default" size="100%">NeurIPS. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">34</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>34</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M</style></author><author><style face="normal" font="default" size="100%">Agkül, A</style></author><author><style face="normal" font="default" size="100%">Haußmann, M</style></author><author><style face="normal" font="default" size="100%">Ünal, G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evidential Turing Processes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2106.01216</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">https://arxiv.org/abs/2106.01216</style></number><publisher><style face="normal" font="default" size="100%">arXiv preprint</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jenner, E</style></author><author><style face="normal" font="default" size="100%">Fita, E</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extensions of Karger&#039;s Algorithm: Why They Fail in Theory and How They Are Useful in Practice</style></title><secondary-title><style face="normal" font="default" size="100%">ICCV. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pages><style face="normal" font="default" size="100%">4602-4611</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometry-Free View Synthesis: Transformers and no 3D Priors</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Intl. Conf. on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2104.07652</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Jahn</style></author><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High-Resolution Complex Scene Synthesis with Transformers</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR 2021, AI for Content Creation Workshop</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The use of coarse-grained layouts for controllable synthesis of complex scene images via deep generative models has recently gained popularity. However, results of current approaches still fall short of their promise of high-resolution synthesis. We hypothesize that this is mostly due to the highly engineered nature of these approaches which often rely on auxiliary losses and intermediate steps such as mask generators. In this note, we present an orthogonal approach to this task, where the generative model is based on pure likelihood training without additional objectives. To do so, we first optimize a powerful compression model with adversarial training which learns to reconstruct its inputs via a discrete latent bottleneck and thereby effectively strips the latent representation of high-frequency details such as texture. Subsequently, we train an autoregressive transformer model to learn the distribution of the discrete image representations conditioned on a tokenized version of the layouts. Our experiments show that the resulting system is able to synthesize high-quality images consistent with the given layouts. In particular, we improve the state-of-the-art FID score on COCO-Stuff and on Visual Genome by up to 19% and 53% and demonstrate the synthesis of images up to 512 x 512 px on COCO and Open Images.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Andreas Blattmann</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2108.08827</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanakoyeu, Artsiom</style></author><author><style face="normal" font="default" size="100%">Pingchuan Ma</style></author><author><style face="normal" font="default" size="100%">Tschernezki, V.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving Deep Metric Learning by Divide and Conquer</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2109.04003</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Deep metric learning (DML) is a cornerstone of many computer vision applications. It aims at learning a mapping from the input domain to an embedding space, where semantically similar objects are located nearby and dissimilar objects far from another. The target similarity on the training data is defined by user in form of ground-truth class labels. However, while the embedding space learns to mimic the user-provided similarity on the training data, it should also generalize to novel categories not seen during training. Besides user-provided groundtruth training labels, a lot of additional visual factors (such as viewpoint changes or shape peculiarities) exist and imply different notions of similarity between objects, affecting the generalization on the images unseen during training. However, existing approaches usually directly learn a single embedding space on all available training data, struggling to encode all different types of relationships, and do not generalize well. We propose to build a more expressive representation by jointly splitting the embedding space and the data hierarchically into smaller sub-parts. We successively focus on smaller subsets of the training data, reducing its variance and learning a different embedding subspace for each data subset. Moreover, the subspaces are learned jointly to cover not only the intricacies, but the breadth of the data as well. Only after that, we build the final embedding from the subspaces in the conquering stage. The proposed algorithm acts as a transparent wrapper that can be placed around arbitrary existing DML methods. Our approach significantly improves upon the state-of-the-art on image retrieval, clustering, and re-identification tasks evaluated using CUB200-2011, CARS196, Stanford Online Products, In-shop Clothes, and PKU VehicleID datasets.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schütz, LM</style></author><author><style face="normal" font="default" size="100%">Louveaux, M</style></author><author><style face="normal" font="default" size="100%">Vilches-Barro, A</style></author><author><style face="normal" font="default" size="100%">Bouziri, S</style></author><author><style face="normal" font="default" size="100%">Cerrone, L</style></author><author><style face="normal" font="default" size="100%">Wolny, A</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author><author><style face="normal" font="default" size="100%">Maizel, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana</style></title><secondary-title><style face="normal" font="default" size="100%">Plant and Cell Physiology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">62</style></volume><pages><style face="normal" font="default" size="100%">1269-1279</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">8</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreas Blattmann</style></author><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Michael Dorkenwald</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">iPOKE: Poking a Still Image for Controlled Stochastic Video Synthesis</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Conference on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2107.02790</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andersson, A</style></author><author><style face="normal" font="default" size="100%">Diego, F</style></author><author><style face="normal" font="default" size="100%">Hamprecht, F A</style></author><author><style face="normal" font="default" size="100%">Wählby, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ISTDECO: In Situ Transcriptomics Decoding by Deconvolution</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">bioRxiv</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mahmoud Afifi</style></author><author><style face="normal" font="default" size="100%">Konstantinos G Derpanis</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Michael S Brown</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Multi-Scale Photo Exposure Correction</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2003.11596</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Gerwinn, S</style></author><author><style face="normal" font="default" size="100%">Look, A</style></author><author><style face="normal" font="default" size="100%">Rakitsch, B</style></author><author><style face="normal" font="default" size="100%">Kandemir, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Artificial Intelligence and Statistics </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">PMLR 130</style></volume><pages><style face="normal" font="default" size="100%">478-486</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pape, C</style></author><author><style face="normal" font="default" size="100%">Remme, R</style></author><author><style face="normal" font="default" size="100%">Wolny, A</style></author><author><style face="normal" font="default" size="100%">Olberg, S</style></author><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Cerrone, L</style></author><author><style face="normal" font="default" size="100%">Cortese, M</style></author><author><style face="normal" font="default" size="100%">Klaus, S</style></author><author><style face="normal" font="default" size="100%">Lucic, B</style></author><author><style face="normal" font="default" size="100%">Ullrich, S</style></author><author><style face="normal" font="default" size="100%">Anders-Össwein, M</style></author><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Cerikan, B</style></author><author><style face="normal" font="default" size="100%">Neufeldt, C J</style></author><author><style face="normal" font="default" size="100%">Ganter, M</style></author><author><style face="normal" font="default" size="100%">Schnitzler, P</style></author><author><style face="normal" font="default" size="100%">Merle, U</style></author><author><style face="normal" font="default" size="100%">Lusic, M</style></author><author><style face="normal" font="default" size="100%">Boulant, S</style></author><author><style face="normal" font="default" size="100%">Stanifer, M</style></author><author><style face="normal" font="default" size="100%">Bartenschlager, R</style></author><author><style face="normal" font="default" size="100%">Hamprecht, F A</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A</style></author><author><style face="normal" font="default" size="100%">Tischer, C</style></author><author><style face="normal" font="default" size="100%">Kräusslich, H.-G.</style></author><author><style face="normal" font="default" size="100%">Müller, B</style></author><author><style face="normal" font="default" size="100%">Laketa, V</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera</style></title><secondary-title><style face="normal" font="default" size="100%">BioEssays</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">43</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Walter, F C</style></author><author><style face="normal" font="default" size="100%">Damrich, S</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MultiStar: Instance Segmentation of Overlapping Objects with Star-Convex Polygons</style></title><secondary-title><style face="normal" font="default" size="100%">ISBI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><pages><style face="normal" font="default" size="100%">295-298</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dmytro Kotovenko</style></author><author><style face="normal" font="default" size="100%">Matthias Wright</style></author><author><style face="normal" font="default" size="100%">Arthur Heimbrecht</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://compvis.github.io/brushstroke-parameterized-style-transfer/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">There have been many successful implementations of
neural style transfer in recent years. In most of these works,
the stylization process is confined to the pixel domain. How-
ever, we argue that this representation is unnatural because
paintings usually consist of brushstrokes rather than pixels.
We propose a method to stylize images by optimizing parameterized brushstrokes instead of pixels and further introduce
a simple differentiable rendering mechanism.
Our approach significantly improves visual quality and en-
ables additional control over the stylization process such as
controlling the flow of brushstrokes through user input.
We provide qualitative and quantitative evaluations that
show the efficacy of the proposed parameterized representation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karsten Roth</style></author><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Joseph Paul Cohen</style></author><author><style face="normal" font="default" size="100%">Marzyeh Ghassemi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of International Conference on Machine Learning (ICML)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2009.08348</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pape, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scalable Instance Segmentation for Microscopy</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">PhD Thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>34</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arlt, H</style></author><author><style face="normal" font="default" size="100%">Sui, X</style></author><author><style face="normal" font="default" size="100%">Folger, B</style></author><author><style face="normal" font="default" size="100%">Adams, C</style></author><author><style face="normal" font="default" size="100%">Chen, X</style></author><author><style face="normal" font="default" size="100%">Remme, R</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author><author><style face="normal" font="default" size="100%">DiMaio, F</style></author><author><style face="normal" font="default" size="100%">Liao, M</style></author><author><style face="normal" font="default" size="100%">Goodman, JM</style></author><author><style face="normal" font="default" size="100%">Farese, RV</style></author><author><style face="normal" font="default" size="100%">Walther, TC</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Seipin forms a flexible cage at lipid droplet formation sites</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><publisher><style face="normal" font="default" size="100%">bioRxiv</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Md Amirul Islam</style></author><author><style face="normal" font="default" size="100%">Matthew Kowal</style></author><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Sen Jia</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Konstantinos G Derpanis</style></author><author><style face="normal" font="default" size="100%">Neil Bruce</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Shape or Texture: Understanding Discriminative Features in CNNs</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Learning Representations (ICLR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Dorkenwald</style></author><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Andreas Blattmann</style></author><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Konstantinos G. Derpanis</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stochastic Image-to-Video Synthesis usin cINNs</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Taming Transformers for High-Resolution Image Synthesis</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2012.09841</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transforming Information Into Knowledge: How Computational Methods Reshape Art History</style></title><secondary-title><style face="normal" font="default" size="100%">Digital Humanities Quaterly (DHQ)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">15</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transforming Information Into Knowledge: How Computational Methods Reshape Art History</style></title><secondary-title><style face="normal" font="default" size="100%">Digital Humanities Quaterly (DHQ)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://digitalhumanities.org/dhq/vol/15/3/000560/000560.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">15</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Damrich, S</style></author><author><style face="normal" font="default" size="100%">Hamprecht, F.H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">UMAP does not reproduce high-dimensional similarities due to negative sampling</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><number><style face="normal" font="default" size="100%">arXiv:2103.14608</style></number><publisher><style face="normal" font="default" size="100%">arXiv preprint</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Damrich, S</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On UMAP&#039;s True Loss Function</style></title><secondary-title><style face="normal" font="default" size="100%">NeurIPS. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">34</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>34</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bellagente, M</style></author><author><style face="normal" font="default" size="100%">Haußmann, M</style></author><author><style face="normal" font="default" size="100%">Luchmann, M</style></author><author><style face="normal" font="default" size="100%">Plehn, T</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding Event-Generation Networks via Uncertainties</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2104.04543v1</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">arXiv preprint</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreas Blattmann</style></author><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Michael Dorkenwald</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding Object Dynamics for Interactive Image-to-Video Synthesis</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2106.11303v1</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">What would be the effect of locally poking a static scene? We present an approach that learns naturally-looking global articulations caused by a local manipulation at a pixel level. Training requires only videos of moving objects but no information of the underlying manipulation of the physical scene. Our generative model learns to infer natural object dynamics as a response to user interaction and learns about the interrelations between different object body regions. Given a static image of an object and a local poking of a pixel, the approach then predicts how the object would deform over time. In contrast to existing work on video prediction, we do not synthesize arbitrary realistic videos but enable local interactive control of the deformation. Our model is not restricted to particular object categories and can transfer dynamics onto novel unseen object instances. Extensive experiments on diverse objects demonstrate the effectiveness of our approach compared to common video prediction frameworks.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">Uta Büchler</style></author><author><style face="normal" font="default" size="100%">Michael Dorkenwald</style></author><author><style face="normal" font="default" size="100%">Philipp Reiser</style></author><author><style face="normal" font="default" size="100%">Linard Filli</style></author><author><style face="normal" font="default" size="100%">Fritjof Helmchen</style></author><author><style face="normal" font="default" size="100%">Anna-Sophia Wahl</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised behaviour analysis and magnification (uBAM) using deep learning</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Machine Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://rdcu.be/ch6pL</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Motor behaviour analysis is essential to biomedical research and clinical diagnostics as it provides a non-invasive strategy for identifying motor impairment and its change caused by interventions. State-of-the-art instrumented movement analysis is time- and cost-intensive, because it requires the placement of physical or virtual markers. As well as the effort required for marking the keypoints or annotations necessary for training or fine-tuning a detector, users need to know the interesting behaviour beforehand to provide meaningful keypoints. Here, we introduce unsupervised behaviour analysis and magnification (uBAM), an automatic deep learning algorithm for analysing behaviour by discovering and magnifying deviations. A central aspect is unsupervised learning of posture and behaviour representations to enable an objective comparison of movement. Besides discovering and quantifying deviations in behaviour, we also propose a generative model for visually magnifying subtle behaviour differences directly in a video without requiring a detour via keypoints or annotations. Essential for this magnification of deviations, even across different individuals, is a disentangling of appearance and behaviour. Evaluations on rodents and human patients with neurological diseases demonstrate the wide applicability of our approach. Moreover, combining optogenetic stimulation with our unsupervised behaviour analysis shows its suitability as a non-invasive diagnostic tool correlating function to brain plasticity.</style></abstract><reprint-edition><style face="normal" font="default" size="100%">https://arxiv.org/abs/2012.09237</style></reprint-edition></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolny, A</style></author><author><style face="normal" font="default" size="100%">Cerrone, L</style></author><author><style face="normal" font="default" size="100%">Vijayan, A</style></author><author><style face="normal" font="default" size="100%">Tofanelli, R</style></author><author><style face="normal" font="default" size="100%">Vilches-Barro, A</style></author><author><style face="normal" font="default" size="100%">Louveaux, M</style></author><author><style face="normal" font="default" size="100%">Wenzel, C</style></author><author><style face="normal" font="default" size="100%">Strauss, S</style></author><author><style face="normal" font="default" size="100%">Wilson-Sanchez, D</style></author><author><style face="normal" font="default" size="100%">Lymbouridou, R</style></author><author><style face="normal" font="default" size="100%">Steigleder, SS</style></author><author><style face="normal" font="default" size="100%">Pape, C</style></author><author><style face="normal" font="default" size="100%">Bailoni, A</style></author><author><style face="normal" font="default" size="100%">Duran-Nebreda, S</style></author><author><style face="normal" font="default" size="100%">Bassel, GW</style></author><author><style face="normal" font="default" size="100%">Lohmann, JU</style></author><author><style face="normal" font="default" size="100%">Tsiantis, M</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author><author><style face="normal" font="default" size="100%">Schneitz, K</style></author><author><style face="normal" font="default" size="100%">Maizel, A</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution</style></title><secondary-title><style face="normal" font="default" size="100%">eLife</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">9</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krull, A.</style></author><author><style face="normal" font="default" size="100%">Hirsch, P.</style></author><author><style face="normal" font="default" size="100%">Rother, C.</style></author><author><style face="normal" font="default" size="100%">Schiffrin, A.</style></author><author><style face="normal" font="default" size="100%">Krull, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Artificial-intelligence-driven scanning probe microscopy</style></title><secondary-title><style face="normal" font="default" size="100%">Communications Physics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Scanning probe microscopy (SPM) has revolutionized the fields of materials, nano-science, chemistry, and biology, by enabling mapping of surface properties and surface manipulation with atomic precision. However, these achievements require constant human supervision; fully automated SPM has not been accomplished yet. Here we demonstrate an artificial intelligence framework based on machine learning for autonomous SPM operation (DeepSPM). DeepSPM includes an algorithmic search of good sample regions, a convolutional neural network to assess the quality of acquired images, and a deep reinforcement learning agent to reliably condition the state of the probe. DeepSPM is able to acquire and classify data continuously in multi-day scanning tunneling microscopy experiments, managing the probe quality in response to varying experimental conditions. Our approach paves the way for advanced methods hardly feasible by human operation (e.g., large dataset acquisition and SPM-based nanolithography). DeepSPM can be generalized to most SPM techniques, with the source code publicly available.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Grohs, P.</style></author><author><style face="normal" font="default" size="100%">Holler, M.</style></author><author><style face="normal" font="default" size="100%">Weinmann, A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Assignment Flows</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Variational Methods for Nonlinear Geometric Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.springer.com/gp/book/9783030313500</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">235—260</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zern, A.</style></author><author><style face="normal" font="default" size="100%">Zeilmann, A.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Assignment Flows for Data Labeling on Graphs: Convergence and Stability</style></title><secondary-title><style face="normal" font="default" size="100%">preprint: arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2002.11571</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Radev, Stefan T.</style></author><author><style face="normal" font="default" size="100%">Mertens, Ulf K.</style></author><author><style face="normal" font="default" size="100%">Voss, Andreass</style></author><author><style face="normal" font="default" size="100%">Lynton Ardizzone</style></author><author><style face="normal" font="default" size="100%">Köthe, Ullrich</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BayesFlow: Learning complex stochastic models with invertible neural networks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/2003.06281</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood function is not available. With this work, we propose a novel method for globally amortized Bayesian inference based on invertible neural networks which we call BayesFlow. The method uses simulation to learn a global estimator for the probabilistic mapping from observed data to underlying model parameters. A neural network pre-trained in this way can then, without additional training or optimization, infer full posteriors on arbitrary many real data sets involving the same model family. In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics. Learning summary statistics from data makes the method applicable to modeling scenarios where standard inference techniques with hand-crafted summary statistics fail. We demonstrate the utility of BayesFlow on challenging intractable models from population dynamics, epidemiology, cognitive science and ecology. We argue that BayesFlow provides a general framework for building reusable Bayesian parameter estimation machines for any process model from which data can be simulated.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Gerwinn, S</style></author><author><style face="normal" font="default" size="100%">Kandemir, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Evidential Deep Learning with PAC Regularization </style></title><secondary-title><style face="normal" font="default" size="100%">3rd Symposium on Advances in Approximate Bayesian Inference </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamann, Christoph</style></author><author><style face="normal" font="default" size="100%">Rother, Carsten</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Benchmarking the Robustness of Semantic Segmentation Models</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR 2020</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1908.05005</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">When designing a semantic segmentation module for a practical application, such as autonomous driving, it is crucial to understand the robustness of the module with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on the state-of-the-art model DeepLabv3\$+\$. To increase the realism of our study, we utilize almost 200,000 images generated from Cityscapes and PASCAL VOC 2012, and we furthermore present a realistic noise model, imitating HDR camera noise. Based on the benchmark study we gain several new insights. Firstly, model robustness increases with model performance, in most cases. Secondly, some architecture properties affect robustness significantly, such as a Dense Prediction Cell which was designed to maximize performance on clean data only. Thirdly, to achieve good generalization with respect to various types of image noise, it is recommended to train DeepLabv3+ with our realistic noise model.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kluger, Florian</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Ackermann, Hanno</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Ying</style></author><author><style face="normal" font="default" size="100%">Rosenhahn, Bodo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR 2020</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/2001.02643</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scenes, fitting planes to architectural imagery, or estimating multiple rigid motions within the same sequence. In contrast to previous works, which resorted to hand-crafted search strategies for multiple model detection, we learn the search strategy from data. A neural network conditioned on previously detected models guides a RANSAC estimator to different subsets of all measurements, thereby finding model instances one after another. We train our method supervised as well as self-supervised. For supervised training of the search strategy, we contribute a new dataset for vanishing point estimation. Leveraging this dataset, the proposed algorithm is superior with respect to other robust estimators as well as to designated vanishing point estimation algorithms. For self-supervised learning of the search, we evaluate the proposed algorithm on multi-homography estimation and demonstrate an accuracy that is superior to state-of-the-art methods.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Das Objekt jenseits der Digitalisierung</style></title><secondary-title><style face="normal" font="default" size="100%">Das digitale Objekt</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.deutsches-museum.de/fileadmin/Content/010_DM/060_Verlag/studies-7.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><isbn><style face="normal" font="default" size="100%">978-3-948808-00-6</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Der technische Fortschritt der letzten Jahrzehnte hat disruptive Veränderungen für Gesellschaft, Wirtschaft und Wissenschaft gebracht: Die Digitalisierung ist ein Resultat dessen und beeinflusst, wie wir auf Daten zugreifen, diese verarbeiten, analysieren und Ergebnisse verbreiten. Obwohl dadurch bereits ein Wandel eingeleitet worden ist, kann das Digitalisieren von Textdokumenten oder Bildern nicht das endgültige Ziel sein. Der Fokus aktueller Bestrebungen sollte vielmehr auf der Möglichkeit der Weiterverarbeitung von Digitalisaten liegen – dies schließt eine intelligente Informationsverarbeitung ein. Der Wert der Digitalisierung besteht nicht in der bloßen Anhäufung digitaler Sammlun- gen, sondern in der Tatsache, dass sie weitaus mehr ermöglicht als das Analoge und dafür die notwendigen Grundvoraussetzungen schafft.
Die Problematik besteht nun darin, dass die meisten Verarbeitungs- und Analyse- methoden für digitale Daten noch analog oder diesen nachempfunden sind: So werden digitale Sammlungen und darin enthaltene Bilder häufig noch mit den eigenen Augen, in traditionell komparativer Weise betrachtet und evaluiert. Dass dies aufgrund der Fülle an Daten nicht effizient ist, muss an dieser Stelle nicht betont werden. Obwohl das ana- loge und das digitale Bild den gleichen Inhalt zeigen können, haben beide doch ganz unterschiedliche Substrate. Ein Unterschied besteht zum Beispiel darin, dass digitale Bilder im Gegensatz zu analogen einfach manipuliert und dupliziert werden können. Das Digitale ist nicht das Analoge in neuer Form, und so bedarf es genuin digitaler Methoden für die Verarbeitung digitaler Daten. Durch die Entwicklung computerge- stützter Verfahren entstehen neue Möglichkeiten, Inhalte zu erschließen: Dazu gehören Ansätze zur Objektsuche oder das Gruppieren und Sortieren der Daten entsprechend benutzerdefinierter Dimensionen; dies schließt übergeordnete Kategorien wie Stil oder Genre, aber auch nuancierte Begriffe wie Alter oder Gewichtung der Bildkomposition ein. Doch das Digitale und entsprechende Verfahren können noch weitaus mehr leisten: Generative Verfahren, wie die Bildsynthese und Stilisierung eines Bildes, ermöglichen eine Blickänderung auf das Artefakt und schließlich die Modifizierung des Objekts selbst. Wie hätte ein Künstler eine uns sichtbare Szene gemalt und dargestellt? Und wie sieht ein Mensch in der Pose eines anderen aus? Dies sind Fragen, die durch die Anwendung com- putergestützter Methoden beantwortet werden können. Für das Museum haben diese Ansätze eine besondere Relevanz, da sie neue Arten des Betrachtens und Vermittelns von Kunstwerken oder zum Beispiel die Rekonstruktion verlorener Artefakte erlauben. In Zusammenarbeit von Mensch und Maschine entstehen so neue effektive Verfahren, die Inhalte erschließen, Verbindungen etablieren und neues Wissen generieren.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tobias Dencker</style></author><author><style face="normal" font="default" size="100%">Pablo Klinkisch</style></author><author><style face="normal" font="default" size="100%">Stefan M Maul</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep learning of cuneiform sign detection with weak supervision using transliteration alignment</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cuneiform script</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">sign detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Weakly supervised learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://hci.iwr.uni-heidelberg.de/compvis/projects/cuneiform</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">15</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The cuneiform script provides a glimpse into our ancient history. However, reading age-old clay tablets is time-consuming and requires years of training. To simplify this process, we propose a deep-learning based sign detector that locates and classifies cuneiform signs in images of clay tablets. Deep learning requires large amounts of training data in the form of bounding boxes around cuneiform signs, which are not readily available and costly to obtain in the case of cuneiform script. To tackle this problem, we make use of existing transliterations, a sign-by-sign representation of the tablet content in Latin script. Since these do not provide sign localization, we propose a weakly supervised approach: We align tablet images with their corresponding transliterations to localize the transliterated signs in the tablet image, before using these localized signs in place of annotations to re-train the sign detector. A better sign detector in turn boosts the quality of the alignments. We combine these steps in an iterative process that enables training a cuneiform sign detector from transliterations only. While our method works weakly supervised, a small number of annotations further boost the performance of the cuneiform sign detector which we evaluate on a large collection of clay tablets from the Neo-Assyrian period. To enable experts to directly apply the sign detector in their study of cuneiform texts, we additionally provide a web application for the analysis of clay tablets with a trained cuneiform sign detector.</style></abstract><issue><style face="normal" font="default" size="100%">12</style></issue><work-type><style face="normal" font="default" size="100%">Journal</style></work-type><section><style face="normal" font="default" size="100%">1-21</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bollweg, S</style></author><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Kasieczka, G</style></author><author><style face="normal" font="default" size="100%">Luchmann, M</style></author><author><style face="normal" font="default" size="100%">Plehn, T</style></author><author><style face="normal" font="default" size="100%">Thompson, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep-Learning Jets with Uncertainties and More</style></title><secondary-title><style face="normal" font="default" size="100%">SciPost Phys</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://scipost.org/10.21468/SciPostPhys.8.1.006</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirschbaum, E</style></author><author><style face="normal" font="default" size="100%">Bailoni, A</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><pages><style face="normal" font="default" size="100%">151-162</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sorrenson, Peter</style></author><author><style face="normal" font="default" size="100%">Rother, Carsten</style></author><author><style face="normal" font="default" size="100%">Köthe, Ullrich</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)</style></title><secondary-title><style face="normal" font="default" size="100%">Intl. Conf. Learning Representations (ICLR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/2001.04872</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A central question of representation learning asks under which conditions it is possible to reconstruct the true latent variables of an arbitrarily complex generative process. Recent breakthrough work by Khemakhem et al. (2019) on nonlinear ICA has answered this question for a broad class of conditional generative processes. We extend this important result in a direction relevant for application to real-world data. First, we generalize the theory to the case of unknown intrinsic problem dimension and prove that in some special (but not very restrictive) cases, informative latent variables will be automatically separated from noise by an estimating model. Furthermore, the recovered informative latent variables will be in one-to-one correspondence with the true latent variables of the generating process, up to a trivial component-wise transformation. Second, we introduce a modification of the RealNVP invertible neural network architecture (Dinh et al. (2016)) which is particularly suitable for this type of problem: the General Incompressible-flow Network (GIN). Experiments on artificial data and EMNIST demonstrate that theoretical predictions are indeed verified in practice. In particular, we provide a detailed set of exactly 22 informative latent variables extracted from EMNIST.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Disentangling Invertible Interpretation Network for Explaining Latent Representations</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://compvis.github.io/iin/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Karsten Roth</style></author><author><style face="normal" font="default" size="100%">Homanga Bharadhwaj</style></author><author><style face="normal" font="default" size="100%">Samarth Sinha</style></author><author><style face="normal" font="default" size="100%">Yoshua Bengio</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Joseph Paul Cohen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE European Conference on Computer Vision (ECCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2004.13458</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hehn, TM</style></author><author><style face="normal" font="default" size="100%">Kooij, J F P</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">End-to-End Learning of Decision Trees and Forests</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">128</style></volume><pages><style face="normal" font="default" size="100%">997-1011</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lynton Ardizzone</style></author><author><style face="normal" font="default" size="100%">Mackowiak, Radek</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">jan</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/2001.06448</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Generative models are more informative about underlying phenomena than discriminative ones and offer superior uncertainty quantification and out-of-distribution robustness. However, these advantages often come at the expense of reduced classification accuracy. The Information Bottleneck objective (IB) formulates this trade-off in a clean information-theoretic way, but its practical application is hampered by a lack of accurate high-dimensional estimators of mutual information (MI), its main constituent. To overcome this limitation, we develop the theory and methodology of IB-INNs, which optimize the IB objective by means of Invertible Neural Networks (INNs), without the need for approximations of MI. Our experiments show that IB-INNs allow for a precise adjustment of the generative/discriminative trade-off: They learn accurate models of the class conditional likelihoods, generalize well to unseen data and reliably detect out-of-distribution examples, while at the same time exhibiting classification accuracy close to purely discriminative feed-forward networks.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zeilmann, A.</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometric Numerical Integration of the Assignment Flow</style></title><secondary-title><style face="normal" font="default" size="100%">Inverse Problems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">034004 (33pp)</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, S.</style></author><author><style face="normal" font="default" size="100%">Hamprecht, F A</style></author><author><style face="normal" font="default" size="100%">Funke, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inpainting Networks Learn to Separate Cells in Microscopy Images</style></title><secondary-title><style face="normal" font="default" size="100%">BMCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author><author><style face="normal" font="default" size="100%">Funke, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Instance Separation Emerges from Inpainting</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:2003.00891</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Friman, Sonja</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Laboratory investigations of concentration and wind profiles close to the wind-driven wavy water surface</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine Learning for Instance Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">PhD Thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Making Sense of CNNs: Interpreting Deep Representations &amp; Their Invariances with INNs</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE European Conference on Computer Vision (ECCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://compvis.github.io/invariances/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">To tackle increasingly complex tasks, it has become an essential ability of neural networks to learn abstract representations. These task-specific representations and, particularly, the invariances they capture turn neural networks into black box models that lack interpretability. To open such a black box, it is, therefore, crucial to uncover the different semantic concepts a model has learned as well as those that it has learned to be invariant to. We present an approach based on INNs that (i) recovers the task-specific, learned invariances by disentangling the remaining factor of variation in the data and that (ii) invertibly transforms these recovered invariances combined with the model representation into an equally expressive one with accessible semantic concepts. As a consequence, neural network representations become understandable by providing the means to (i) expose their semantic meaning, (ii) semantically modify a representation, and (iii) visualize individual learned semantic concepts and invariances. Our invertible approach significantly extends the abilities to understand black box models by enabling post-hoc interpretations of state-of-the-art networks without compromising their performance.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. Schilling</style></author><author><style face="normal" font="default" size="100%">M. Gutsche</style></author><author><style face="normal" font="default" size="100%">A. Brock</style></author><author><style face="normal" font="default" size="100%">D. Späth</style></author><author><style face="normal" font="default" size="100%">C. Rother</style></author><author><style face="normal" font="default" size="100%">K. Krispin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mind the Gap – A Benchmark for Dense Depth Prediction beyond Lidar</style></title><secondary-title><style face="normal" font="default" size="100%">2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, Steffen</style></author><author><style face="normal" font="default" size="100%">Bailoni, Alberto</style></author><author><style face="normal" font="default" size="100%">Pape, Constantin</style></author><author><style face="normal" font="default" size="100%">Rahaman, Nasim</style></author><author><style face="normal" font="default" size="100%">Kreshuk, Anna</style></author><author><style face="normal" font="default" size="100%">Köthe, Ullrich</style></author><author><style face="normal" font="default" size="100%">Hamprecht, Fred A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">3724-3738</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we propose a greedy algorithm for signed graph partitioning, the &quot;Mutex Watershed&quot;. Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. We also prove that this simple algorithm solves to global optimality an objective function that is intimately related to the multicut / correlation clustering integer linear programming formulation. The algorithm is deterministic, very simple to implement, and has empirically linearithmic complexity. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives the best results currently known for the competitive ISBI 2012 EM segmentation benchmark.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Network Fusion for Content Creation with Conditional INNs</style></title><secondary-title><style face="normal" font="default" size="100%">CVPRW 2020 (AI for Content Creation)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://compvis.github.io/network-fusion/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Artificial Intelligence for Content Creation has the potential to reduce the amount of manual content creation work significantly. While automation of laborious work is welcome, it is only useful if it allows users to control aspects of the creative process when desired. Furthermore, widespread adoption of semi-automatic content creation depends on low barriers regarding the expertise, computational budget and time required to obtain results and experiment with new techniques. With state-of-the-art approaches relying on task-specific models, multi-GPU setups and weeks of training time, we must find ways to reuse and recombine them to meet these requirements. Instead of designing and training methods for controllable content creation from scratch, we thus present a method to repurpose powerful, existing models for new tasks, even though they have never been designed for them. We formulate this problem as a translation between expert models, which includes common content creation scenarios, such as text-to-image and image-to-image translation, as a special case. As this translation is ambiguous, we learn a generative model of hidden representations of one expert conditioned on hidden representations of the other expert. Working on the level of hidden representations makes optimal use of the computational effort that went into the training of the expert model to produce these efficient, low-dimensional representations. Experiments demonstrate that our approach can translate from BERT, a state-of-the-art expert for text, to BigGAN, a state-of-the-art expert for images, to enable text-to-image generation, which neither of the experts can perform on its own. Additional experiments show the wide applicability of our approach across different conditional image synthesis tasks and improvements over existing methods for image modifications.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Network-to-Network Translation with Conditional Invertible Neural Networks</style></title><secondary-title><style face="normal" font="default" size="100%"> Neural Information Processing Systems (NeurIPS) (Oral)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://compvis.github.io/net2net/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Combining stimuli from diverse modalities into a coherent perception is a striking feat of intelligence of evolved brains. This work seeks its analogy in deep learning models and aims to establish relations between existing networks by faithfully combining the representations of these different domains. Therefore, we seek a model that can relate between different existing representations by learning a conditionally invertible mapping between them. The network demonstrates this capability by (i) providing generic transfer between diverse domains, (ii) enabling controlled content synthesis by allowing modification in other domains, and (iii) facilitating diagnosis of existing representations by translating them into an easily accessible domain. Our domain transfer network can translate between fixed representations without having to learn or finetune them. This allows users to utilize various existing domain-specific expert models from the literature that had been trained with extensive computational resources. Experiments on diverse conditional image synthesis tasks, competitive image modification results and experiments on image-to-image and text-to-image generation demonstrate the generic applicability of our approach. In particular, we translate between BERT and BigGAN, state-of-the-art text and image models to provide text-to-image generation, which neither of both experts can perform on their own.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Robin Rombach</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Note on Data Biases in Generative Models</style></title><secondary-title><style face="normal" font="default" size="100%">NeurIPS 2020 Workshop on Machine Learning for Creativity and Design</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2012.02516</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">It is tempting to think that machines are less prone to unfairness and prejudice. However, machine learning approaches compute their outputs based on data. While biases can enter at any stage of the development pipeline, models are particularly receptive to mirror biases of the datasets they are trained on and therefore do not necessarily reflect truths about the world but, primarily, truths about the data. To raise awareness about the relationship between modern algorithms and the data that shape them, we use a conditional invertible neural network to disentangle the dataset-specific information from the information which is shared across different datasets. In this way, we can project the same image onto different datasets, thereby revealing their inherent biases. We use this methodology to (i) investigate the impact of dataset quality on the performance of generative models, (ii) show how societal biases of datasets are replicated by generative models, and (iii) present creative applications through unpaired transfer between diverse datasets such as photographs, oil portraits, and animes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nikolai Ufer</style></author><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Object Retrieval and Localization in Large Art Collections Using Deep Multi-style Feature Fusion and Iterative Voting</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE European Conference on Computer Vision (ECCV), VISART Workshop </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08-2020</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The search for specific objects or motifs is essential to art history as both assist in decoding the meaning of artworks. Digitization has produced large art collections, but manual methods prove to be insufficient to analyze them. In the following, we introduce an algorithm that allows users to search for image regions containing specific motifs or objects and find similar regions in an extensive dataset, helping art historians to analyze large digitized art collections. Computer vision has presented efficient methods for visual instance retrieval across photographs. However, applied to art collections, they reveal severe deficiencies because of diverse motifs and massive domain shifts induced by differences in techniques, materials, and styles. In this paper, we present a multi-style feature fusion approach that successfully reduces the domain gap and improves retrieval results without labelled data or curated image collections. Our region-based voting with GPU-accelerated approximate nearest-neighbour search allows us to find and localize even small motifs within an extensive dataset in a few seconds. We obtain state-of-the-art results on the Brueghel dataset and demonstrate its generalization to inhomogeneous collections with a large number of distractors.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Karsten Roth</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PADS: Policy-Adapted Sampling for Visual Similarity Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2003.11113</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">1</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefan Haller</style></author><author><style face="normal" font="default" size="100%">Prakash, Mangal</style></author><author><style face="normal" font="default" size="100%">Hutschenreiter, Lisa</style></author><author><style face="normal" font="default" size="100%">Pietzsch, Tobias</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Jug, Florian</style></author><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Primal-Dual Solver for Large-Scale Tracking-by-Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">AISTATS 2020</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a fast approximate solver for the combinatorial problem known as tracking-by-assignment, which we apply to cell tracking. The latter plays a key role in discovery in many life sciences, especially in cell and developmental biology. So far, in the most general setting this problem was addressed by off-the-shelf solvers like Gurobi, whose run time and memory requirements rapidly grow with the size of the input. In contrast, for our method this growth is nearly linear. Our contribution consists of a new (1) de-composable compact representation of the problem; (2) dual block-coordinate ascent method for optimizing the decomposition-based dual; and (3) primal heuristics that reconstructs a feasible integer solution based on the dual information. Compared to solving the problem with Gurobi, we observe an up to 60 times speed-up, while reducing the memory footprint significantly. We demonstrate the efficacy of our method on real-world tracking problems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bailoni, A</style></author><author><style face="normal" font="default" size="100%">Pape, C</style></author><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A.</style></author><author><style face="normal" font="default" size="100%">Hamprecht, F A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks</style></title><secondary-title><style face="normal" font="default" size="100%">GCPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><edition><style face="normal" font="default" size="100%">LNCS</style></edition><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">12544</style></volume><pages><style face="normal" font="default" size="100%">331-344</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bhowmik, Aritra</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Rother, Carsten</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR 2020 (oral)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1912.00623</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We address a core problem of computer vision: Detection and description of 2D feature points for image matching. For a long time, hand-crafted designs, like the seminal SIFT algorithm, were unsurpassed in accuracy and efficiency. Recently, learned feature detectors emerged that implement detection and description using neural networks. Training these networks usually resorts to optimizing low-level matching scores, often pre-defining sets of image patches which should or should not match, or which should or should not contain key points. Unfortunately, increased accuracy for these low-level matching scores does not necessarily translate to better performance in high-level vision tasks. We propose a new training methodology which embeds the feature detector in a complete vision pipeline, and where the learnable parameters are trained in an end-to-end fashion. We overcome the discrete nature of key point selection and descriptor matching using principles from reinforcement learning. As an example, we address the task of relative pose estimation between a pair of images. We demonstrate that the accuracy of a state-of-the-art learning-based feature detector can be increased when trained for the task it is supposed to solve at test time. Our training methodology poses little restrictions on the task to learn, and works for any architecture which predicts key point heat maps, and descriptors for key point locations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karsten Roth</style></author><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Samarth Sinha</style></author><author><style face="normal" font="default" size="100%">Prateek Gupta</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Joseph Paul Cohen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Revisiting Training Strategies and Generalization Performance in Deep Metric Learning</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Machine Learning (ICML)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/pdf/2002.08473.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S.K. Mustikovela</style></author><author><style face="normal" font="default" size="100%">V. Jampani</style></author><author><style face="normal" font="default" size="100%">S. De Mello</style></author><author><style face="normal" font="default" size="100%">S. Liu</style></author><author><style face="normal" font="default" size="100%">U. Iqbal</style></author><author><style face="normal" font="default" size="100%">C. Rother</style></author><author><style face="normal" font="default" size="100%">J. Kautz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-Supervised Viewpoint Learning From Image Collections</style></title><secondary-title><style face="normal" font="default" size="100%">CONSAC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://github.com/NVlabs/SSV</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Training deep neural networks to estimate the viewpoint of objects requires large labeled training datasets. However, manually labeling viewpoints is notoriously hard, error-prone, and time-consuming. On the other hand, it is relatively easy to mine many unlabelled images of an object category from the internet, e.g., of cars or faces. We seek to answer the research question of whether such un-labeled collections of in-the-wild images can be successfully utilized to train viewpoint estimation networks for general object categories purely via self-supervision. Self-supervision here refers to the fact that the only true supervisory signal that the network has is the input image itself. We propose a novel learning framework which incorporates an analysis-by-synthesis paradigm to reconstruct images in a viewpoint aware manner with a generative network, along with symmetry and adversarial constraints to successfully supervise our viewpoint estimation network. We show that our approach performs competitively to fully-supervised approaches for several object categories like human faces, cars, buses, and trains. Our work opens up further research in self-supervised viewpoint learning and serves as a robust baseline for it. We open-source our code at https://github.com/NVlabs/SSV .</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Li, Y</style></author><author><style face="normal" font="default" size="100%">Pape, C</style></author><author><style face="normal" font="default" size="100%">Bailoni, A</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A</style></author><author><style face="normal" font="default" size="100%">Hamprecht, F A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><pages><style face="normal" font="default" size="100%">208-224</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Karsten Roth</style></author><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sharing Matters for Generalization in Deep Metric Learning</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/2004.05582</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Joachim Funke</style></author><author><style face="normal" font="default" size="100%">Michael Wink</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Struktur und Chaos: Kleinskalige Austauschprozesse zwischen Atmosphäre und Meer</style></title><secondary-title><style face="normal" font="default" size="100%">Heidelberger Jahrbücher Online, Entwicklung – Wie aus Prozessen Strukturen werden</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Heidelberger Jahrbücher Online</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">133–150</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Desana, M.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sum-Product Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Learning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">135–173</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Censor, Y.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case</style></title><secondary-title><style face="normal" font="default" size="100%">J. Appl. Numer. Optimization (in press; arXiv:1911.05498)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://jano.biemdas.com/archives/1060</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">15-62</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tourani, Siddharth</style></author><author><style face="normal" font="default" size="100%">Shekhovtsov, Alexander</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization</style></title><secondary-title><style face="normal" font="default" size="100%">AISTATS 2020</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://gitlab.com/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the maximum-a-posteriori inference problem in discrete graphical models and study solvers based on the dual block-coordinate ascent rule. We map all existing solvers in a single framework, allowing for a better understanding of their design principles. We theoretically show that some block-optimizing updates are sub-optimal and how to strictly improve them. On a wide range of problem instances of varying graph connec-tivity, we study the performance of existing solvers as well as new variants that can be obtained within the framework. As a result of this exploration we build a new state-of-the art solver, performing uniformly better on the whole range of test instances.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zern, A.</style></author><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Mathematical Imaging and Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1007/s10851-019-00935-7</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Dorkenwald</style></author><author><style face="normal" font="default" size="100%">Uta Büchler</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Magnification of Posture Deviations Across Subjects</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sandro Braun</style></author><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Part Discovery by Unsupervised Disentanglement</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the German Conference on Pattern Recognition (GCPR) (Oral)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://compvis.github.io/unsupervised-part-segmentation/</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Tübingen</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We address the problem of discovering part segmentations of articulated objects without supervision. In contrast to keypoints, part segmentations provide information about part localizations on the level of individual pixels. Capturing both locations and semantics, they are an attractive target for supervised learning approaches. However, large annotation costs limit the scalability of supervised algorithms to other object categories than humans. Unsupervised approaches potentially allow to use much more data at a lower cost. Most existing unsupervised approaches focus on learning abstract representations to be refined with supervision into the final representation. Our approach leverages a generative model consisting of two disentangled representations for an object&#039;s shape and appearance and a latent variable for the part segmentation. From a single image, the trained model infers a semantic part segmentation map. In experiments, we compare our approach to previous state-of-the-art approaches and observe significant gains in segmentation accuracy and shape consistency. Our work demonstrates the feasibility to discover semantic part segmentations without supervision.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Omair Ghori</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Representation Learning by Discovering Reliable Image Relations</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June 2020</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1911.07808</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">102</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">P. Vlahos</style></author><author><style face="normal" font="default" size="100%">E. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">What controls air-sea gas exchange at extreme wind speeds? Evidence from laboratory experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Recent Advances in the Study of Oceanic Whitecaps</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">133–150</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J. Kirk Cochran</style></author><author><style face="normal" font="default" size="100%">Henry J. Bokuniewicz</style></author><author><style face="normal" font="default" size="100%">Patricia L. Yager</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Air-Sea Gas Exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Encyclopedia of Ocean Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><edition><style face="normal" font="default" size="100%">3</style></edition><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">1–13</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerstin E. Krall</style></author><author><style face="normal" font="default" size="100%">Andrew W. Smith</style></author><author><style face="normal" font="default" size="100%">Naohisa Takagaki</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Air–sea gas exchange at wind speeds up to 85 m/s</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">1783-–1799</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Grohs, P.</style></author><author><style face="normal" font="default" size="100%">Holler, M.</style></author><author><style face="normal" font="default" size="100%">Weinmann, A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Assignment Flows</style></title><secondary-title><style face="normal" font="default" size="100%">Variational Methods for Nonlinear Geometric Data and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">in press</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Gerwinn, S</style></author><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Prior Networks with PAC Training</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1906.00816</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kruse, Jakob</style></author><author><style face="normal" font="default" size="100%">Lynton Ardizzone</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Benchmarking Invertible Architectures on Inverse Problems</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><number><style face="normal" font="default" size="100%">i</style></number><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Recent work demonstrated that flow-based invert-ible neural networks are promising tools for solving ambiguous inverse problems. Following up on this, we investigate how ten invertible archi-tectures and related models fare on two intuitive, low-dimensional benchmark problems, obtaining the best results with coupling layers and simple autoencoders. We hope that our initial efforts inspire other researchers to evaluate their invertible architectures in the same setting and put forth additional benchmarks, so our evaluation may eventually grow into an official community challenge.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kamann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Benchmarking the Robustness of Semantic Segmentation Models</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1908.05005</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">When designing a semantic segmentation module for a practical application, such as autonomous driving, it is crucial to understand the robustness of the module with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on the state-of-the-art model DeepLabv3\$+\$. To increase the realism of our study, we utilize almost 200,000 images generated from Cityscapes and PASCAL VOC 2012, and we furthermore present a realistic noise model, imitating HDR camera noise. Based on the benchmark study we gain several new insights. Firstly, model robustness increases with model performance, in most cases. Secondly, some architecture properties affect robustness significantly, such as a Dense Prediction Cell which was designed to maximize performance on clean data only. Thirdly, to achieve good generalization with respect to various types of image noise, it is recommended to train DeepLabv3+ with our realistic noise model.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bendinger, AL</style></author><author><style face="normal" font="default" size="100%">Debus, C</style></author><author><style face="normal" font="default" size="100%">Glowa, C</style></author><author><style face="normal" font="default" size="100%">Karger, CP</style></author><author><style face="normal" font="default" size="100%">Peter, J</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press</style></title><secondary-title><style face="normal" font="default" size="100%">Physics in Medicine and Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">64</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kleesiek, Jens</style></author><author><style face="normal" font="default" size="100%">Morshuis, Jan Nikolas</style></author><author><style face="normal" font="default" size="100%">Isensee, Fabian</style></author><author><style face="normal" font="default" size="100%">Deike-Hofmann, Katerina</style></author><author><style face="normal" font="default" size="100%">Paech, Daniel</style></author><author><style face="normal" font="default" size="100%">Kickingereder, Philipp</style></author><author><style face="normal" font="default" size="100%">Köthe, Ullrich</style></author><author><style face="normal" font="default" size="100%">Rother, Carsten</style></author><author><style face="normal" font="default" size="100%">Forsting, Michael</style></author><author><style face="normal" font="default" size="100%">Wick, Wolfgang</style></author><author><style face="normal" font="default" size="100%">Bendszus, Martin</style></author><author><style face="normal" font="default" size="100%">Schlemmer, Heinz Peter</style></author><author><style face="normal" font="default" size="100%">Radbruch, Alexander</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study</style></title><secondary-title><style face="normal" font="default" size="100%">Investigative Radiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Bayesian uncertainty</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">gadolinium-based contrast agents</style></keyword><keyword><style  face="normal" font="default" size="100%">glioma</style></keyword><keyword><style  face="normal" font="default" size="100%">multiparametric MRI</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">54</style></volume><pages><style face="normal" font="default" size="100%">653–660</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Objectives Gadolinium-based contrast agents (GBCAs) have become an integral part in daily clinical decision making in the last 3 decades. However, there is a broad consensus that GBCAs should be exclusively used if no contrast-free magnetic resonance imaging (MRI) technique is available to reduce the amount of applied GBCAs in patients. In the current study, we investigate the possibility of predicting contrast enhancement from noncontrast multiparametric brain MRI scans using a deep-learning (DL) architecture. Materials and Methods A Bayesian DL architecture for the prediction of virtual contrast enhancement was developed using 10-channel multiparametric MRI data acquired before GBCA application. The model was quantitatively and qualitatively evaluated on 116 data sets from glioma patients and healthy subjects by comparing the virtual contrast enhancement maps to the ground truth contrast-enhanced T1-weighted imaging. Subjects were split in 3 different groups: Enhancing tumors (n = 47), nonenhancing tumors (n = 39), and patients without pathologic changes (n = 30). The tumor regions were segmented for a detailed analysis of subregions. The influence of the different MRI sequences was determined. Results Quantitative results of the virtual contrast enhancement yielded a sensitivity of 91.8% and a specificity of 91.2%. T2-weighted imaging, followed by diffusion-weighted imaging, was the most influential sequence for the prediction of virtual contrast enhancement. Analysis of the whole brain showed a mean area under the curve of 0.969 ± 0.019, a peak signal-to-noise ratio of 22.967 ± 1.162 dB, and a structural similarity index of 0.872 ± 0.031. Enhancing and nonenhancing tumor subregions performed worse (except for the peak signal-to-noise ratio of the nonenhancing tumors). The qualitative evaluation by 2 raters using a 4-point Likert scale showed good to excellent (3-4) results for 91.5% of the enhancing and 92.3% of the nonenhancing gliomas. However, despite the good scores and ratings, there were visual deviations between the virtual contrast maps and the ground truth, including a more blurry, less nodular-like ring enhancement, few low-contrast false-positive enhancements of nonenhancing gliomas, and a tendency to omit smaller vessels. These &quot;features&quot; were also exploited by 2 trained radiologists when performing a Turing test, allowing them to discriminate between real and virtual contrast-enhanced images in 80% and 90% of the cases, respectively. Conclusions The introduced model for virtual gadolinium enhancement demonstrates a very good quantitative and qualitative performance. Future systematic studies in larger patient collectives with varying neurological disorders need to evaluate if the introduced virtual contrast enhancement might reduce GBCA exposure in clinical practice.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mackowiak, Radek</style></author><author><style face="normal" font="default" size="100%">Lenz, Philip</style></author><author><style face="normal" font="default" size="100%">Ghori, Omair</style></author><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Lange, Oliver</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">British Machine Vision Conference 2018, BMVC 2018</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">State of the art methods for semantic image segmentation are trained in a supervised fashion using a large corpus of fully labeled training images. However, gathering such a corpus is expensive, due to human annotation effort, in contrast to gathering unlabeled data. We propose an active learning-based strategy, called CEREALS, in which a human only has to hand-label a few, automatically selected, regions within an unlabeled image corpus. This minimizes human annotation effort while maximizing the performance of a semantic image segmentation method. The automatic selection procedure is achieved by: a) using a suitable information measure combined with an estimate about human annotation effort, which is inferred from a learned cost model, and b) exploiting the spatial coherency of an image. The performance of CEREALS is demonstrated on Cityscapes, where we are able to reduce the annotation effort to 17%, while keeping 95% of the mean Intersection over Union (mIoU) of a model that was trained with the fully annotated training set of Cityscapes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dmytro Kotovenko</style></author><author><style face="normal" font="default" size="100%">Sanakoyeu, Artsiom</style></author><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Content and Style Disentanglement for Artistic Style Transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Intl. Conf. on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Continuous-Domain Assignment Flows</style></title><secondary-title><style face="normal" font="default" size="100%">preprint: arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1910.07287</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Guan-hung Lu</style></author><author><style face="normal" font="default" size="100%">Wu-ting Tsai</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Decomposing infrared images of wind waves for quantitative separation into characteristic flow processes</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Geoscience and Remote Sensing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">57</style></volume><pages><style face="normal" font="default" size="100%">8304–8316</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Active Learning with Adaptive Acquisition</style></title><secondary-title><style face="normal" font="default" size="100%">IJCAI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><pages><style face="normal" font="default" size="100%">2470-2476</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weihao Li</style></author><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Object Co-segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">11363 LNCS</style></volume><pages><style face="normal" font="default" size="100%">638–653</style></pages><isbn><style face="normal" font="default" size="100%">9783030208929</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on common objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Papst</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of a method for quantitative imaging of air-water gas exchange</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><volume><style face="normal" font="default" size="100%">Bachelors thesis</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">mastersMaster&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discrete Graphical Models — An Optimization Perspective</style></title><secondary-title><style face="normal" font="default" size="100%">Foundations and Trends® in Computer Graphics and Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><number><style face="normal" font="default" size="100%">3-4</style></number><publisher><style face="normal" font="default" size="100%">Now Publishers</style></publisher><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">160–429</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This monograph is about combinatorial optimization. More precisely, about a special class of combinatorial problems known as energy minimization or maximum a posteriori (MAP) inference in graphical models, closely related to weighted and valued constraint satisfaction problems and having tight connections to Markov random fields and quadratic pseudo-boolean optimization. What distinguishes this monograph from a number of other monographs on graphical models is its focus: It considers graphical models, or, more precisely, MAP-inference for graphical models, purely as a combinatorial optimization problem. Modeling, applications, probabilistic interpretations and many other aspects are either ignored here or find their place in examples and remarks only. Combinatorial optimization as a field is largely based on five fundamental topics: (i) integer linear programming and polyhedral optimization; (ii) totally unimodular matrices and the class of min-cost-flow problems; (iii) Lagrange decompositions and relaxations; (iv) dynamic programming and (v) submodularity, matroids and greedy algorithms. Each of these topics found its place in this monograph, although to a variable extent. The covering of each respective topic reflects its importance for the considered MAP-inference problem. Since optimization is the primary topic of this monograph, we mostly stick to the terminology widely used in optimization and where it was possible we tried to avoid the graphical models community-specific technical terms. The latter differ from sub-community to sub-community and, in our view, significantly complicate the information exchange between them. The same holds also for the presentation of material in this monograph. If there is a choice when introducing mathematical constructs or proving statements, we prefer more general mathematical tools applicable in the whole field of operations research rather than to stick to graphical modelspecific constructions. We additionally provide the graphical model-specific constructions if it turns out to be easier than the more general one. This way of presentation has two advantages. A reader familiar with a more general technique can grasp the new material faster. On the other hand, the monograph may serve as an introduction to combinatorial optimization for readers unfamiliar with this subject. To make the monograph even more suitable for both categories of readers we explicitly separate the mathematical optimization background chapters from those specific to graphical models. We believe, therefore, that the monograph can be useful for undergraduate and graduate students studying optimization or graphical models, as well as for experts in optimization who want to have a look into graphical models. Moreover, we believe that even experts in graphical models can find new views on the known facts as well as a novel presentation of less known results in the monograph. These are for instance (i) a simple and general proof of equivalence of different acyclic Lagrange decompositions of a graphical model; (ii) a general scheme for analyzing convergence of dual block-coordinate descent methods; (iii) a short and self-contained analysis of a linear programming relaxation for binary graphical models, its persistency properties and its relation to quadratic pseudo-boolean optimization. The present monograph is based on lectures given to undergraduate students of Technical University of Dresden and Heidelberg University. The selection of material is done in a way that it may serve as a basis for a semester course.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanakoyeu, A.</style></author><author><style face="normal" font="default" size="100%">Tschernezki, V.</style></author><author><style face="normal" font="default" size="100%">Uta Büchler</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Divide and Conquer the Embedding Space for Metric Learning</style></title><secondary-title><style face="normal" font="default" size="100%"> Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">metric learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://github.com/CompVis/metric-learning-divide-and-conquer</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kiefer, L</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Weinmann, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An efficient algorithm for the piecewise affine-linear Mumford-Shah model based on a Taylor jet splitting</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">29</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">921-933</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cerrone, L</style></author><author><style face="normal" font="default" size="100%">Zeilmann, A</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">End-to-End Learned Random Walker for Seeded Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><pages><style face="normal" font="default" size="100%">12559-12568</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Imle, A</style></author><author><style face="normal" font="default" size="100%">Kumberger, P</style></author><author><style face="normal" font="default" size="100%">Schnellbächer, ND</style></author><author><style face="normal" font="default" size="100%">Fehr, J</style></author><author><style face="normal" font="default" size="100%">Carillo-Bustamente, P</style></author><author><style face="normal" font="default" size="100%">Ales, J</style></author><author><style face="normal" font="default" size="100%">Schmidt, P</style></author><author><style face="normal" font="default" size="100%">Ritter, C</style></author><author><style face="normal" font="default" size="100%">Godinez, WJ</style></author><author><style face="normal" font="default" size="100%">Müller, B</style></author><author><style face="normal" font="default" size="100%">Rohr, K</style></author><author><style face="normal" font="default" size="100%">Hamprecht, FA</style></author><author><style face="normal" font="default" size="100%">Schwarz, US</style></author><author><style face="normal" font="default" size="100%">Graw, F</style></author><author><style face="normal" font="default" size="100%">Fackler, OT</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Communications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">13;10(1)</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expert sample consensus applied to camera re-localization</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">aug</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1908.02484</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2019-Octob</style></volume><pages><style face="normal" font="default" size="100%">7524–7533</style></pages><isbn><style face="normal" font="default" size="100%">9781728148038</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Fitting model parameters to a set of noisy data points is a common problem in computer vision. In this work, we fit the 6D camera pose to a set of noisy correspondences between the 2D input image and a known 3D environment. We estimate these correspondences from the image using a neural network. Since the correspondences often contain outliers, we utilize a robust estimator such as Random Sample Consensus (RANSAC) or Differentiable RANSAC (DSAC) to fit the pose parameters. When the problem domain, e.g. the space of all 2D-3D correspondences, is large or ambiguous, a single network does not cover the domain well. Mixture of Experts (MoE) is a popular strategy to divide a problem domain among an ensemble of specialized networks, so called experts, where a gating network decides which expert is responsible for a given input. In this work, we introduce Expert Sample Consensus (ESAC), which integrates DSAC in a MoE. Our main technical contribution is an efficient method to train ESAC jointly and end-to-end. We demonstrate experimentally that ESAC handles two real-world problems better than competing methods, i.e. scalability and ambiguity. We apply ESAC to fitting simple geometric models to synthetic images, and to camera re-localization for difficult, real datasets.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel M. Kirchhöfer</style></author><author><style face="normal" font="default" size="100%">Gerhard A. Holst</style></author><author><style face="normal" font="default" size="100%">Fred S. Wouters</style></author><author><style face="normal" font="default" size="100%">Stephan Hock</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extended noise equalisation for image compression in microscopical applications</style></title><secondary-title><style face="normal" font="default" size="100%">tm - Technisches Messen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">86</style></volume><pages><style face="normal" font="default" size="100%">422–432</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, F</style></author><author><style face="normal" font="default" size="100%">Schnörr, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast Multivariate Log-Concave Density Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Comp. Statistics &amp; Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">140</style></volume><pages><style face="normal" font="default" size="100%">41-58</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast Multivariate Log-Concave Density Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Comp. Statistics &amp; Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">140</style></volume><pages><style face="normal" font="default" size="100%">41–58</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Angelika Klein</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Fetch Dependency of Small-Scale Air-Sea Interaction Processes at Low to Moderate Wind Speeds</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Mustikovela, Siva Karthik</style></author><author><style face="normal" font="default" size="100%">Geiger, Andreas</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometric Image Synthesis</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://youtu.be/W2tFCz9xJoU</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11366 LNCS</style></volume><pages><style face="normal" font="default" size="100%">85–100</style></pages><isbn><style face="normal" font="default" size="100%">9783030208752</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The task of generating natural images from 3D scenes has been a long standing goal in computer graphics. On the other hand, recent developments in deep neural networks allow for trainable models that can produce natural-looking images with little or no knowledge about the scene structure. While the generated images often consist of realistic looking local patterns, the overall structure of the generated images is often inconsistent. In this work we propose a trainable, geometry-aware image generation method that leverages various types of scene information, including geometry and segmentation, to create realistic looking natural images that match the desired scene structure. Our geometrically-consistent image synthesis method is a deep neural network, called Geometry to Image Synthesis (GIS) framework, which retains the advantages of a trainable method, e.g., differentiability and adaptiveness, but, at the same time, makes a step towards the generalizability, control and quality output of modern graphics rendering engines. We utilize the GIS framework to insert vehicles in outdoor driving scenes, as well as to generate novel views of objects from the Linemod dataset. We qualitatively show that our network is able to generalize beyond the training set to novel scene geometries, object shapes and segmentations. Furthermore, we quantitatively show that the GIS framework can be used to synthesize large amounts of training data which proves beneficial for training instance segmentation models.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zeilmann, A.</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometric Numerical Integration of the Assignment Flow</style></title><secondary-title><style face="normal" font="default" size="100%">Inverse Problems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1088/1361-6420/ab2772</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kostrykin, L.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Rohr, K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Image Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.media.2019.101536</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lynton Ardizzone</style></author><author><style face="normal" font="default" size="100%">Carsten Lüth</style></author><author><style face="normal" font="default" size="100%">Kruse, Jakob</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Guided Image Generation with Conditional Invertible Neural Networks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1907.02392</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lynton Ardizzone</style></author><author><style face="normal" font="default" size="100%">Carsten Lüth</style></author><author><style face="normal" font="default" size="100%">Kruse, Jakob</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Guided Image Generation with Conditional Invertible Neural Networks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1907.02392</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stuart Berg</style></author><author><style face="normal" font="default" size="100%">Kutra, D</style></author><author><style face="normal" font="default" size="100%">Kroeger, T</style></author><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Bernhard X. Kausler</style></author><author><style face="normal" font="default" size="100%">Haubold, C.</style></author><author><style face="normal" font="default" size="100%">Schiegg, M</style></author><author><style face="normal" font="default" size="100%">Ales, J</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Rudy, M</style></author><author><style face="normal" font="default" size="100%">Eren, K</style></author><author><style face="normal" font="default" size="100%">Cervantes, JI</style></author><author><style face="normal" font="default" size="100%">Xu, B</style></author><author><style face="normal" font="default" size="100%">Beuttenmüller, F</style></author><author><style face="normal" font="default" size="100%">Wolny, A</style></author><author><style face="normal" font="default" size="100%">Zhang, C</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ilastik: interactive machine learning for (bio)image analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Methods</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">1226-1232</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Remme, R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Instance Segmentation via Associative Pixel Embeddings</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Friman, Sonja I.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigating SO2 transfer across the air–water interface via LIF</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">60</style></volume><pages><style face="normal" font="default" size="100%">65</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Mustikovela, Siva Karthik</style></author><author><style face="normal" font="default" size="100%">Pertsch, Karl</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">11363 LNCS</style></volume><pages><style face="normal" font="default" size="100%">477–492</style></pages><isbn><style face="normal" font="default" size="100%">9783030208929</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded. Recent RGB-D-based methods are robust to moderate degrees of occlusion. For RGB inputs, no previous method works well for partly occluded objects. Our main contribution is to present the first deep learning-based system that estimates accurate poses for partly occluded objects from RGB-D and RGB input. We achieve this with a new instance-aware pipeline that decomposes 6D object pose estimation into a sequence of simpler steps, where each step removes specific aspects of the problem. The first step localizes all known objects in the image using an instance segmentation network, and hence eliminates surrounding clutter and occluders. The second step densely maps pixels to 3D object surface positions, so called object coordinates, using an encoder-decoder network, and hence eliminates object appearance. The third, and final, step predicts the 6D pose using geometric optimization. We demonstrate that we significantly outperform the state-of-the-art for pose estimation of partly occluded objects for both RGB and RGB-D input.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanslovsky, P</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Isotropic Reconstruction of Neural Morphology from Large Non-Isotropic 3D Electron MIcroscopy</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hühnerbein, R.</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Adaptive Regularization for Image Labeling Using Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">preprint: arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1910.09976</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hühnerbein, R.</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Adaptive Regularization for Image Labeling Using Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. SSVM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Titus Leistner</style></author><author><style face="normal" font="default" size="100%">Schilling, Hendrik</style></author><author><style face="normal" font="default" size="100%">Mackowiak, Radek</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 2019 International Conference on 3D Vision, 3DV 2019</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">depth estimation</style></keyword><keyword><style  face="normal" font="default" size="100%">light fields</style></keyword><keyword><style  face="normal" font="default" size="100%">Stereo</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">sep</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">249–257</style></pages><isbn><style face="normal" font="default" size="100%">9781728131313</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a method for depth estimation from light field data, based on a fully convolutional neural network architecture. Our goal is to design a pipeline which achieves highly accurate results for small-and wide-baseline light fields. Since light field training data is scarce, all learning-based approaches use a small receptive field and operate on small disparity ranges. In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range. In this way, our approach &#039;learns to think outside the box of the receptive field&quot;. Our network performs joint classification of integer disparities and regression of disparity-offsets. A U-Net component provides excellent long-range smoothing. EPI-Shift considerably outperforms the state-of-the-art learning-based approaches and is on par with hand-crafted methods. We demonstrate this on a publicly available, synthetic, small-baseline benchmark and on large-baseline real-world recordings.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirschbaum, E.</style></author><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Sonntag, H</style></author><author><style face="normal" font="default" size="100%">Schneider, J</style></author><author><style face="normal" font="default" size="100%">Elzoheiry, S</style></author><author><style face="normal" font="default" size="100%">Kann, O</style></author><author><style face="normal" font="default" size="100%">Durstewitz, D</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos</style></title><secondary-title><style face="normal" font="default" size="100%">ICLR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weihao Li</style></author><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Localizing Common Objects Using Common Component Activation Map</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work, we propose an approach to localize common objects from novel object categories in a set of images. We solve this problem using a new common component activation map (CCAM) in which we treat the class-specific activation maps (CAM) as components to discover the common components in the image set. We show that our approach can generalize on novel object categories in our experiments .</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter, S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine learning under test-time budget constraints</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leila Nagel</style></author><author><style face="normal" font="default" size="100%">Kerstin E. Krall</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurement of air-sea gas transfer velocities in the Baltic Sea</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">235–247</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bengio, Y</style></author><author><style face="normal" font="default" size="100%">Deleu, T</style></author><author><style face="normal" font="default" size="100%">Rahaman, N</style></author><author><style face="normal" font="default" size="100%">Ke, R</style></author><author><style face="normal" font="default" size="100%">Lachapelle, S</style></author><author><style face="normal" font="default" size="100%">Bilaniuk, O</style></author><author><style face="normal" font="default" size="100%">Goyal, A</style></author><author><style face="normal" font="default" size="100%">Pal, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1901.10912</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">Karsten Roth</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MIC: Mining Interclass Characteristics for Improved Metric Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Intl. Conf. on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural-guided RANSAC: Learning where to sample model hypotheses</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">may</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1905.04132</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2019-Octob</style></volume><pages><style face="normal" font="default" size="100%">4321–4330</style></pages><isbn><style face="normal" font="default" size="100%">9781728148038</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present Neural-Guided RANSAC (NG-RANSAC), an extension to the classic RANSAC algorithm from robust optimization. NG-RANSAC uses prior information to improve model hypothesis search, increasing the chance of finding outlier-free minimal sets. Previous works use heuristic side-information like hand-crafted descriptor distance to guide hypothesis search. In contrast, we learn hypothesis search in a principled fashion that lets us optimize an arbitrary task loss during training, leading to large improvements on classic computer vision tasks. We present two further extensions to NG-RANSAC. Firstly, using the inlier count itself as training signal allows us to train neural guidance in a self-supervised fashion. Secondly, we combine neural guidance with differentiable RANSAC to build neural networks which focus on certain parts of the input data and make the output predictions as good as possible. We evaluate NG-RANSAC on a wide array of computer vision tasks, namely estimation of epipolar geometry, horizon line estimation and camera re-localization. We achieve superior or competitive results compared to state-of-the-art robust estimators, including very recent, learned ones.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ravindran, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel Deep Learning-based Instance Segmentation Using Mutex Watershed for Microscopy Cell Images</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirschbaum, E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel Machine Learning Approaches for Neurophysiological Data Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Adler, Tim J</style></author><author><style face="normal" font="default" size="100%">Ayala, Leonardo</style></author><author><style face="normal" font="default" size="100%">Lynton Ardizzone</style></author><author><style face="normal" font="default" size="100%">Kenngott, Hannes G</style></author><author><style face="normal" font="default" size="100%">Vemuri, Anant</style></author><author><style face="normal" font="default" size="100%">Müller-Stich, Beat P</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Maier-Hein, Lena</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Out of Distribution Detection for Intra-operative Functional Imaging</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI UNSURE Workshop 2019</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">11840 LNCS</style></volume><pages><style face="normal" font="default" size="100%">75–82</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Multispectral optical imaging is becoming a key tool in the operating room. Recent research has shown that machine learning algorithms can be used to convert pixel-wise reflectance measurements to tissue parameters, such as oxygenation. However, the accuracy of these algorithms can only be guaranteed if the spectra acquired during surgery match the ones seen during training. It is therefore of great interest to detect so-called out of distribution (OoD) spectra to prevent the algorithm from presenting spurious results. In this paper we present an information theory based approach to OoD detection based on the widely applicable information criterion (WAIC). Our work builds upon recent methodology related to invertible neural networks (INN). Specifically, we make use of an ensemble of INNs as we need their tractable Jacobians in order to compute the WAIC. Comprehensive experiments with in silico, and in vivo multispectral imaging data indicate that our approach is well-suited for OoD detection. Our method could thus be an important step towards reliable functional imaging in the operating room.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Snajder, R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pipeline für die automatisierte Objektsegmentierung von 3D Lightshet Mikroskopiebildern</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fita Sanmartin, E</style></author><author><style face="normal" font="default" size="100%">Damrich, Sebastian</style></author><author><style face="normal" font="default" size="100%">Hamprecht, Fred A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bhowmik, Aritra</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">dec</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1912.00623</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We address a core problem of computer vision: Detection and description of 2D feature points for image matching. For a long time, hand-crafted designs, like the seminal SIFT algorithm, were unsurpassed in accuracy and efficiency. Recently, learned feature detectors emerged that implement detection and description using neural networks. Training these networks usually resorts to optimizing low-level matching scores, often pre-defining sets of image patches which should or should not match, or which should or should not contain key points. Unfortunately, increased accuracy for these low-level matching scores does not necessarily translate to better performance in high-level vision tasks. We propose a new training methodology which embeds the feature detector in a complete vision pipeline, and where the learnable parameters are trained in an end-to-end fashion. We overcome the discrete nature of key point selection and descriptor matching using principles from reinforcement learning. As an example, we address the task of relative pose estimation between a pair of images. We demonstrate that the accuracy of a state-of-the-art learning-based feature detector can be increased when trained for the task it is supposed to solve at test time. Our training methodology poses little restrictions on the task to learn, and works for any architecture which predicts key point heat maps, and descriptors for key point locations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Li, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust Single Object Tracking via Fully Convolutional Siamese Networks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Master Thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation</style></title><secondary-title><style face="normal" font="default" size="100%">UAI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><pages><style face="normal" font="default" size="100%">563-573</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Zern, A.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-Assignment Flows for Unsupervised Data Labeling on Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">preprint: arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1911.03472</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style 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name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Großkinsky, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Synaptic Cleft Prediction on Electron Microsope Images</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Esposito, M</style></author><author><style face="normal" font="default" size="100%">Hennersperger, C</style></author><author><style face="normal" font="default" size="100%">Göbl, R</style></author><author><style face="normal" font="default" size="100%">Demaret, L</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Navab, N</style></author><author><style face="normal" font="default" size="100%">Baust, M</style></author><author><style face="normal" font="default" size="100%">Weinmann, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Total variation regularization of pose signals with an application to 3D freehand ultrasound</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Medical Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">38(10)</style></volume><pages><style face="normal" font="default" size="100%">2245-2258</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Xiao, S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tracking Dividing Cells Using Spatio-Temporal Embeddings</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zern, A.</style></author><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">preprint: arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1904.10863</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Zern, A.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. SSVM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dominik Lorenz</style></author><author><style face="normal" font="default" size="100%">Leonard Bereska</style></author><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Part-Based Disentangling of Object Shape and Appearance</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Oral + Best paper finalist: top 45 / 5160 submissions)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><custom1><style face="normal" font="default" size="100%">arXiv preprint: &lt;a href=&quot;https://arxiv.org/abs/1903.06946&quot;&gt;download&lt;/a&gt;
Source Code: &lt;a href=&quot;https://github.com/CompVis/unsupervised-disentangling&quot;&gt;download&lt;/a&gt;
CVPR Oral: &lt;a href=&quot;https://youtu.be/fNlMGWm7bbk?t=5382&quot;&gt;download&lt;/a&gt;
Oral slides: &lt;a href=&quot;#&quot;&gt;download&lt;/a&gt;</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Johannes Haux</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Robust Disentangling of Latent Characteristics for Image Synthesis</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Intl. Conf. on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://compvis.github.io/robust-disentangling/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Deep generative models come with the promise to learn an explainable representation for visual objects that allows image sampling, synthesis, and selective modification. The main challenge is to learn to properly model the independent latent characteristics of an object, especially its appearance and pose. We present a novel approach that learns disentangled representations of these characteristics and explains them individually. Training requires only pairs of images depicting the same object appearance, but no pose annotations. We propose an additional classifier that estimates the minimal amount of regularization required to enforce disentanglement. Thus both representations together can completely explain an image while being independent of each other. Previous methods based on adversarial approaches fail to enforce this independence, while methods based on variational approaches lead to uninformative representations. In experiments on diverse object categories, the approach successfully recombines pose and appearance to reconstruct and retarget novel synthesized images. We achieve significant improvements over state-of-the-art methods which utilize the same level of supervision, and reach performances comparable to those of pose-supervised approaches. However, we can handle the vast body of articulated object classes for which no pose models/annotations are available.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dmytro Kotovenko</style></author><author><style face="normal" font="default" size="100%">Sanakoyeu, A.</style></author><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Ma, P.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using a Transformation Content Block For Image Style Transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Variational Perspective on the Assignment Flow</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. SSVM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nikolai Ufer</style></author><author><style face="normal" font="default" size="100%">Kam To Lui</style></author><author><style face="normal" font="default" size="100%">Katja Schwarz</style></author><author><style face="normal" font="default" size="100%">Paul Warkentin</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weakly Supervised Learning of Dense SemanticCorrespondences and Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">German Conference on Pattern Recognition (GCPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pandey, N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weakly Supervised Semantic Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bopp, Maximilian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Air-Flow and Stress Partitioning over Wind Waves in a Linear Wind-Wave Facility</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Attesting Similarity: Supporting the Organization and Study of Art Image Collections with Computer Vision</style></title><secondary-title><style face="normal" font="default" size="100%">Digital Scholarship in the Humanities, Oxford University Press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">845-856</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Mustikovela, Siva Karthik</style></author><author><style face="normal" font="default" size="100%">Mescheder, Lars</style></author><author><style face="normal" font="default" size="100%">Geiger, Andreas</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Augmented Reality Meets Computer Vision</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">autonomous driving</style></keyword><keyword><style  face="normal" font="default" size="100%">data augmenta-</style></keyword><keyword><style  face="normal" font="default" size="100%">instance segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic training data</style></keyword><keyword><style  face="normal" font="default" size="100%">tion</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">In press</style></volume><pages><style face="normal" font="default" size="100%">1–13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The success of deep learning in computer vision is based on availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment real images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance and a large number of complex object arrangements. In contrast to modeling complete 3D environments, our augmentation approach requires only a few user interactions in combination with 3D shapes of the target object. Through extensive experimentation, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of our approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenes. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on Cityscapes dataset. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amount of annotated real data.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Mustikovela, S K</style></author><author><style face="normal" font="default" size="100%">Mescheder, A</style></author><author><style face="normal" font="default" size="100%">Geiger, C</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes</style></title><secondary-title><style face="normal" font="default" size="100%">IJCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><pages><style face="normal" font="default" size="100%">1-12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Mustikovela, Siva Karthik</style></author><author><style face="normal" font="default" size="100%">Mescheder, Lars</style></author><author><style face="normal" font="default" size="100%">Geiger, Andreas</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">autonomous driving</style></keyword><keyword><style  face="normal" font="default" size="100%">Data augmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">instance segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Object detection</style></keyword><keyword><style  face="normal" font="default" size="100%">synthetic training data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">aug</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1708.01566</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">126</style></volume><pages><style face="normal" font="default" size="100%">961–972</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D models of the target object category. Leveraging our approach, we introduce a novel dataset of augmented urban driving scenes with 360 degree images that are used as environment maps to create realistic lighting and reflections on rendered objects. We analyze the significance of realistic object placement by comparing manual placement by humans to automatic methods based on semantic scene analysis. This allows us to create composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. Through an extensive set of experiments, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of the proposed approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenarios. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on the Cityscapes dataset. Our experiments demonstrate that the models trained on augmented imagery generalize better than those trained on fully synthetic data or models trained on limited amounts of annotated real data.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hodaň, Tomáš</style></author><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Kehl, Wadim</style></author><author><style face="normal" font="default" size="100%">Buch, Anders Glent</style></author><author><style face="normal" font="default" size="100%">Kraft, Dirk</style></author><author><style face="normal" font="default" size="100%">Drost, Bertram</style></author><author><style face="normal" font="default" size="100%">Vidal, Joel</style></author><author><style face="normal" font="default" size="100%">Ihrke, Stephan</style></author><author><style face="normal" font="default" size="100%">Zabulis, Xenophon</style></author><author><style face="normal" font="default" size="100%">Sahin, Caner</style></author><author><style face="normal" font="default" size="100%">Manhardt, Fabian</style></author><author><style face="normal" font="default" size="100%">Tombari, Federico</style></author><author><style face="normal" font="default" size="100%">Kim, Tae Kyun</style></author><author><style face="normal" font="default" size="100%">Matas, Jiří</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BOP: Benchmark for 6D object pose estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">aug</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1808.08319</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11214 LNCS</style></volume><pages><style face="normal" font="default" size="100%">19–35</style></pages><isbn><style face="normal" font="default" size="100%">9783030012489</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: (i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, (ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, (iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and (iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at bop.felk.cvut.cz.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bell, P.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer Vision und Kunstgeschichte — Dialog zweier Bildwissenschaften</style></title><secondary-title><style face="normal" font="default" size="100%">Computing Art Reader: Einführung in die digitale Kunstgeschichte, P. Kuroczyński et al. (ed.)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arnab, Anurag</style></author><author><style face="normal" font="default" size="100%">Zheng, Shuai</style></author><author><style face="normal" font="default" size="100%">Jayasumana, Sadeep</style></author><author><style face="normal" font="default" size="100%">Romera-paredes, Bernardino</style></author><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kahl, Fredrik</style></author><author><style face="normal" font="default" size="100%">Torr, Philip</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Cvpr</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">conditional random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">seman-</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889&amp;rep=rep1&amp;type=pdf%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Xx</style></number><volume><style face="normal" font="default" size="100%">XX</style></volume><pages><style face="normal" font="default" size="100%">1–15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">—Semantic Segmentation is the task of labelling every pixel in an image with a pre-defined object category. It has numer-ous applications in scenarios where the detailed understanding of an image is required, such as in autonomous vehicles and medical diagnosis. This problem has traditionally been solved with probabilistic models known as Conditional Random Fields (CRFs) due to their ability to model the relationships between the pixels being predicted. However, Deep Neural Networks (DNNs) have recently been shown to excel at a wide range of computer vision problems due to their ability to learn rich feature representations automatically from data, as opposed to traditional hand-crafted features. The idea of combining CRFs and DNNs have achieved state-of-the-art results in a number of domains. We review the literature on combining the modelling power of CRFs with the representation-learning ability of DNNs, ranging from early work that combines these two techniques as independent stages of a common pipeline to recent approaches that embed inference of probabilistic models directly in the neural network itself. Finally, we summarise future research directions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sayed, N.</style></author><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cross and Learn: Cross-Modal Self-Supervision</style></title><secondary-title><style face="normal" font="default" size="100%">German Conference on Pattern Recognition (GCPR) (Oral)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">action recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">cross-modal</style></keyword><keyword><style  face="normal" font="default" size="100%">image understanding</style></keyword><keyword><style  face="normal" font="default" size="100%">unsupervised learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1811.03879v1</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Stuttgart, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we present a self-supervised method to learn feature representations for different modalities. Based on the observation that cross-modal information has a high semantic meaning we propose a method to effectively exploit this signal. For our method we utilize video data since it is available on a large scale and provides easily accessible modalities given by RGB and optical flow. We demonstrate state-of-the-art performance on highly contested action recognition datasets in the context of self-supervised learning. We also show the transferability of our feature representations and conduct extensive ablation studies to validate our core contributions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cerrone, L</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanakoyeu, A.</style></author><author><style face="normal" font="default" size="100%">Miguel Bautista</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Unsupervised Learning of Visual Similarities</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://authors.elsevier.com/a/1WXUt77nKSb25 </style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">78</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.</style></abstract><section><style face="normal" font="default" size="100%">331</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weilbach, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dictionary Learning with Bayesian GANs for Few-Shot Classification</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna-Sophia Wahl</style></author><author><style face="normal" font="default" size="100%">Erlebach, E.</style></author><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">Uta Büchler</style></author><author><style face="normal" font="default" size="100%">Kaiser, J.</style></author><author><style face="normal" font="default" size="100%">Ineichen, V. B.</style></author><author><style face="normal" font="default" size="100%">Alice C. Mosberger</style></author><author><style face="normal" font="default" size="100%">Schneeberger, S.</style></author><author><style face="normal" font="default" size="100%">Imobersteg, S.</style></author><author><style face="normal" font="default" size="100%">Wieckhorst, M.</style></author><author><style face="normal" font="default" size="100%">Stirn, M.</style></author><author><style face="normal" font="default" size="100%">Schroeter, A.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">M. E. Schwab</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats</style></title><secondary-title><style face="normal" font="default" size="100%">Sage Journals</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/18</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%"> Journal of Cerebral Blood Flow &amp; Metabolism</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The majority of stroke patients develop post-stroke fatigue, a symptom which impairs motivation and diminishes the success of rehabilitative interventions. We show that large cortical strokes acutely reduce activity levels in rats for 1–2 weeks as a physiological response paralleled by signs of systemic inflammation. Rats were exposed early (1–2 weeks) or late (3–4 weeks after stroke) to an individually monitored enriched environment to stimulate self-controlled high-intensity sensorimotor training. A group of animals received Anti-Nogo antibodies for the first two weeks after stroke, a neuronal growth promoting immunotherapy already in clinical trials. Early exposure to the enriched environment resulted in poor outcome: Training intensity was correlated to enhanced systemic inflammation and functional impairment. In contrast, animals starting intense sensorimotor training two weeks after stroke preceded by the immunotherapy revealed better recovery with functional outcome positively correlated to the training intensity and the extent of re-innervation of the stroke denervated cervical hemi-cord. Our results suggest stroke-induced fatigue as a biological purposeful reaction of the organism during neuronal remodeling, enabling new circuit formation which will then be stabilized or pruned in the subsequent rehabilitative training phase. However, intense training too early may lead to wrong connections and is thus less effective.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hehn, T</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">End-to-end Learning of Deterministic Decision Trees</style></title><secondary-title><style face="normal" font="default" size="100%">German Conference on Pattern Recognition. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 11269</style></volume><pages><style face="normal" font="default" size="100%">612-627</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Draxler, F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Energy Landscape of Deep Neural Networks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Draxler, F</style></author><author><style face="normal" font="default" size="100%">Veschgini, K</style></author><author><style face="normal" font="default" size="100%">Salmhofer, M</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Essentially No Barriers in Neural Network Energy Landscape</style></title><secondary-title><style face="normal" font="default" size="100%">ICML. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">80</style></volume><pages><style face="normal" font="default" size="100%">1308--1317</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefan Haller</style></author><author><style face="normal" font="default" size="100%">Paul Swoboda</style></author><author><style face="normal" font="default" size="100%">Bogdan Savchynskyy</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Sheila A. McIlraith</style></author><author><style face="normal" font="default" size="100%">Kilian Q. 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5624</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">11</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zern, A.</style></author><author><style face="normal" font="default" size="100%">Rohr, K.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometric Image Labeling with Global Convex Labeling Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">EMMCVPR</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">10746</style></volume><pages><style face="normal" font="default" size="100%">533–547</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Mustikovela, S K</style></author><author><style face="normal" font="default" size="100%">Geiger, A</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometric Image Synthesis</style></title><secondary-title><style face="normal" font="default" size="100%">ACCV. Proceedings, in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zeilmann, A.</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometric Numerical Integration of the Assignment Flow</style></title><secondary-title><style face="normal" font="default" size="100%">preprint: arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1810.06970</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hühnerbein, R</style></author><author><style face="normal" font="default" size="100%">Savarino, F</style></author><author><style face="normal" font="default" size="100%">Freddie Aström</style></author><author><style face="normal" font="default" size="100%">Schnörr, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal on Imaging Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">1317-1362</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hühnerbein, R.</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM J. Imaging Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://epubs.siam.org/doi/abs/10.1137/17M1150669</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">1317–1362</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uta Büchler</style></author><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the European Conference on Computer Vision (ECCV)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">action recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">deep reinforcement learning</style></keyword><keyword><style  face="normal" font="default" size="100%">image understanding</style></keyword><keyword><style  face="normal" font="default" size="100%">self-supervision</style></keyword><keyword><style  face="normal" font="default" size="100%">shuffling</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">(UB and BB  contributed equally)</style></publisher><pub-location><style face="normal" font="default" size="100%">Munich, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Self-supervised learning of convolutional neural networks can harness large amounts of cheap unlabeled data to train powerful feature representations. As surrogate task, we jointly address ordering of visual data in the spatial and temporal domain. The permutations of training samples, which are at the core of self-supervision by ordering, have so far been sampled randomly from a fixed preselected set. Based on deep reinforcement learning we propose a sampling policy that adapts to the state of the network, which is being trained. Therefore, new permutations are sampled according to their expected utility for updating the convolutional feature representation. Experimental evaluation on unsupervised and transfer learning tasks demonstrates competitive performance on standard benchmarks for image and video classification and nearest neighbor retrieval.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jakob Kunz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigating small scale air-sea exchange processes via thermography</style></title><secondary-title><style face="normal" font="default" size="100%">Front. Mech. Eng.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">26</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Mustikovela, S K</style></author><author><style face="normal" font="default" size="100%">Pertsch, K</style></author><author><style face="normal" font="default" size="100%">E Brachmann</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects</style></title><secondary-title><style face="normal" font="default" size="100%">ACCV. Proceedings, in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schimmel, F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learnability of Approximated Graph Cut Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Less is More - 6D Camera Localization via 3D Surface Regression</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">nov</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1711.10228</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">4654–4662</style></pages><isbn><style face="normal" font="default" size="100%">9781538664209</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Popular research areas like autonomous driving and augmented reality have renewed the interest in image-based camera localization. In this work, we address the task of predicting the 6D camera pose from a single RGB image in a given 3D environment. With the advent of neural networks, previous works have either learned the entire camera localization process, or multiple components of a camera localization pipeline. Our key contribution is to demonstrate and explain that learning a single component of this pipeline is sufficient. This component is a fully convolutional neural network for densely regressing so-called scene coordinates, defining the correspondence between the input image and the 3D scene space. The neural network is prepended to a new end-to-end trainable pipeline. Our system is efficient, highly accurate, robust in training, and exhibits outstanding generalization capabilities. It exceeds state-of-the-art consistently on indoor and outdoor datasets. Interestingly, our approach surpasses existing techniques even without utilizing a 3D model of the scene during training, since the network is able to discover 3D scene geometry automatically, solely from single-view constraints.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maurice Weiler</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Steerable Filters for Rotation Equivariant CNNs</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><pages><style face="normal" font="default" size="100%">849-858</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Omar Ghori</style></author><author><style face="normal" font="default" size="100%">Radek Mackowiak</style></author><author><style face="normal" font="default" size="100%">Miguel Bautista</style></author><author><style face="normal" font="default" size="100%">Niklas Beuter</style></author><author><style face="normal" font="default" size="100%">Lucas Drumond</style></author><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning to Forecast Pedestrian Intention from Pose Dynamics</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligent Vehicles, IEEE, 2018</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Erb, Wolfgang</style></author><author><style face="normal" font="default" size="100%">Andreas Weinmann</style></author><author><style face="normal" font="default" size="100%">Ahlborg, Mandy</style></author><author><style face="normal" font="default" size="100%">Christina Brandt</style></author><author><style face="normal" font="default" size="100%">Bringout, Gael</style></author><author><style face="normal" font="default" size="100%">Buzug, Thorsten M</style></author><author><style face="normal" font="default" size="100%">Frikel, Jürgen</style></author><author><style face="normal" font="default" size="100%">Kaethner, Christian</style></author><author><style face="normal" font="default" size="100%">Tobias Knopp</style></author><author><style face="normal" font="default" size="100%">März, Thomas</style></author><author><style face="normal" font="default" size="100%">Möddel, Martin</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Alexander Weber</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging</style></title><secondary-title><style face="normal" font="default" size="100%">Inverse Problems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">34</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">5</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shekhovtsov, Alexander</style></author><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Maximum Persistency via Iterative Relaxed Inference in Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">discrete optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">energy minimization</style></keyword><keyword><style  face="normal" font="default" size="100%">graphical models</style></keyword><keyword><style  face="normal" font="default" size="100%">LP relaxation</style></keyword><keyword><style  face="normal" font="default" size="100%">partial optimality</style></keyword><keyword><style  face="normal" font="default" size="100%">persistency</style></keyword><keyword><style  face="normal" font="default" size="100%">WCSP</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.icg.tugraz.at/</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">1668–1682</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Kiechle</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Andreas Weinmann</style></author><author><style face="normal" font="default" size="100%">Martin Kleinsteuber</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-based learning of local image features for unsupervised texture segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">27</style></volume><pages><style face="normal" font="default" size="100%">1994-2007</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">4</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tourani, Siddharth</style></author><author><style face="normal" font="default" size="100%">Shekhovtsov, Alexander</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Block-Coordinate-Ascent</style></keyword><keyword><style  face="normal" font="default" size="100%">graphical models</style></keyword><keyword><style  face="normal" font="default" size="100%">Message passing algorithms</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">11208 LNCS</style></volume><pages><style face="normal" font="default" size="100%">264–281</style></pages><isbn><style face="normal" font="default" size="100%">9783030012243</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Dense, discrete Graphical Models with pairwise potentials are a powerful class of models which are employed in state-of-the-art computer vision and bio-imaging applications. This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle. Surprisingly, by making a small change to a low-performing solver, the Max Product Linear Programming (MPLP) algorithm [7], we derive the new solver MPLP++ that significantly outperforms all existing solvers by a large margin, including the state-of-the-art solver Tree-Reweighted Sequential (TRW-S) message-passing algorithm [17]. Additionally, our solver is highly parallel, in contrast to TRW-S, which gives a further boost in performance with the proposed GPU and multi-thread CPU implementations. We verify the superiority of our algorithm on dense problems from publicly available benchmarks as well as a new benchmark for 6D Object Pose estimation. We also provide an ablation study with respect to graph density.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multicut Algorithms for Neurite Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Pape, C</style></author><author><style face="normal" font="default" size="100%">Bailoni, A</style></author><author><style face="normal" font="default" size="100%">Rahaman, N</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Mutex Watershed: Efficient, Parameter-Free Image   Partitioning</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">571-587</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, Steffen</style></author><author><style face="normal" font="default" size="100%">Pape, Constantin</style></author><author><style face="normal" font="default" size="100%">Bailoni, Alberto</style></author><author><style face="normal" font="default" size="100%">Rahaman, Nasim</style></author><author><style face="normal" font="default" size="100%">Kreshuk, Anna</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Mutex Watershed: Efficient, Parameter-Free Image Partitioning</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">apr</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1904.12654</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11208 LNCS</style></volume><pages><style face="normal" font="default" size="100%">571–587</style></pages><isbn><style face="normal" font="default" size="100%">9783030012243</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments; or equivalently, the task of detecting closed contours in an image. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as an NP-hard signed graph partitioning problem. Here, we propose an algorithm with empirically linearithmic complexity. Unlike seeded watershed, the algorithm can accommodate not only attractive but also repulsive cues, allowing it to find a previously unspecified number of segments without the need for explicit seeds or a tunable threshold. The algorithm itself, which we dub “Mutex Watershed”, is closely related to a minimal spanning tree computation. It is deterministic and easy to implement. When presented with short-range attractive and long-range repulsive cues from a deep neural network, the Mutex Watershed gives results that currently define the state-of-the-art in the competitive ISBI 2012 EM segmentation benchmark. These results are also better than those obtained from other recently proposed clustering strategies operating on the very same network outputs.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reconstructing Histories: Analyzing Exhibition Photographs with Computational Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Arts, Computational Aesthetics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2018</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">7, 64</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">64</style></issue><section><style face="normal" font="default" size="100%">1-21</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reflecting on How Artworks Are Processed and Analyzed by Computer Vision</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Computer Vision (ECCV - VISART)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alessandro Vianello</style></author><author><style face="normal" font="default" size="100%">Jens Ackermann</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust Hough transform based 3D reconstruction from circular light fields</style></title><secondary-title><style face="normal" font="default" size="100%">Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kostrykin, L.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Rohr, K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. ISBI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kawetzki, D</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Segmentation of Urban Scenes Using Deep Learning</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rahaman, N</style></author><author><style face="normal" font="default" size="100%">Arpit, D</style></author><author><style face="normal" font="default" size="100%">Baratin, A</style></author><author><style face="normal" font="default" size="100%">Draxler, F</style></author><author><style face="normal" font="default" size="100%">Lin, M</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bengio, Y</style></author><author><style face="normal" font="default" size="100%">Courville, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the spectral bias of deep neural networks</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv preprint arXiv:1806.08734</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sanakoyeu, A.</style></author><author><style face="normal" font="default" size="100%">Dmytro Kotovenko</style></author><author><style face="normal" font="default" size="100%">Sabine Lang</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Style-Aware Content Loss for Real-time HD Style Transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the European Conference on Computer Vision (ECCV) (Oral)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">generative network</style></keyword><keyword><style  face="normal" font="default" size="100%">Style transfer</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an artist, but previous work is limited to only a single instance of a style or shows no benefit from more images. Moreover, previous work has relied on a direct comparison of art in the domain of RGB images or on CNNs pre-trained on ImageNet, which requires millions of labeled object bounding boxes and can introduce an extra bias, since it has been assembled without artistic consideration. To circumvent these issues, we propose a style-aware content loss, which is trained jointly with a deep encoder-decoder network for real-time, high-resolution stylization of images and videos. We propose a quantitative measure for evaluating the quality of a stylized image and also have art historians rank patches from our approach against those from previous work. These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.
</style></abstract><custom1><style face="normal" font="default" size="100%">Oral slides: &lt;a href=&quot;https://drive.google.com/file/d/1X5WeUxPxKG5PiOaZTXDI_ZVTVAjedmqo/view?usp=sharing&quot;&gt;download&lt;/a&gt;
Supplementary material: &lt;a href=&quot;https://hcicloud.iwr.uni-heidelberg.de/index.php/s/kF81jw5XN2MGHqL&quot;&gt;download&lt;/a&gt;</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">A Supplementary Material CEREALS-Cost-Effective REgion-based Active Learning for Semantic Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A.1 Implementation Details Instead of cropping the annotated regions out of the images, while taking into account their receptive field in input space, we instead mask out all currently unlabeled data in output space, making sure that no loss is computed on unlabeled data when learning the semantic segmentation model nor when learning the cost model. We then perform an image-based training, from unprocessed input images to spatial label maps. However, our practical implementation of CEREALS, which will be made publicly available is supporting both options. For training the utilized models we use Adam as our optimizer with learning rate, alpha and beta set to 0.0001, 0.99 and 0.999 respectively. Furthermore, we claim convergence whenever a model hasn&#039;t improved regarding the application loss for at least 10 epochs. We train with the mini-batch size set to 1, such that a gradient step is always being applied w.r.t. one full resolution image of Cityscapes. Semantic Segmentation Model We do not train the employed model in stages, but directly optimize for FCN8s. Regarding the training performed on the full training set of Cityscapes, we report a mean intersection over union (mIoU) of 0.605 which is, as all other results, computed on the full validation dataset of Cityscapes. Note, that the original model achieves a mIoU of 0.65 and that we are able to reproduce this result when the width multiplier is set to 1.0, despite all other changes. Though we utilized this particular model, CEREALS can use any model producing semantic segmentation masks as long as it provides probability distributions regarding it&#039;s posterior outcome. The cost model however, would need to be adapted or made independent of the semantic segmentation model in such a case. Cost Model The only change we made to the original model&#039;s architecture is to replace it&#039;s softmax activation with a linear activation layer. We trained the model towards minimizing the mean squared error of predicted and ground truth clicks. Since we observed some pixels to have unrealistically many clicks in the ground truth data, we clipped the values to be in [0, 10] range allowing a maximum of 10 ground truth clicks per pixel. As the semantic segmentation model, the cost model doesn&#039;t have any upsampling layer at the end, in order to allow for faster trainings. We instead downscale the provided click data by a factor of 8.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kristian Bredies</style></author><author><style face="normal" font="default" size="100%">Martin Holler</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Andreas Weinmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Total Generalized Variation for Manifold-valued Data</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal on Imaging Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">1785 - 1848</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Johannes Haux</style></author><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Learning a Realistic Rendering of Human Behavior</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Computer Vision (ECCV - HBUGEN)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Realistic rendering of human behavior is of great interest for applications such as video animations, virtual reality and more generally, gaming engines. Commonly animations of persons performing actions are rendered by articulating explicit 3D models based on sequences of coarse body shape representations simulating a certain behavior. While the simulation of natural behavior can be efficiently learned from common video data, the corresponding 3D models are typically designed in manual, laborious processes or reconstructed from costly (multi-)sensor data. In this work, we present an approach towards a holistic learning framework for rendering human behavior in which all components are learned from easily available data. We utilize motion capture data to generate realistic generations which can be controlled by a user and learn to render characters using only RGB camera data. Our experiments show that we can further improve data efficiency by training on multiple characters at the same time. Overall our approach shows a completely new path towards easily available, personalized avatar creation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hendrik Schilling</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Trust your Model: Light Field Depth Estimation with inline Occlusion Handling</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schilling, Hendrik</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><pages><style face="normal" font="default" size="100%">4530–4538</style></pages><isbn><style face="normal" font="default" size="100%">9781538664209</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We address the problem of depth estimation from light-field images. Our main contribution is a new way to handle occlusions which improves general accuracy and quality of object borders. In contrast to all prior work we work with a model which directly incorporates both depth and occlusion, using a local optimization scheme based on the PatchMatch algorithm. The key benefit of this joint approach is that we utilize all available data, and not erroneously discard valuable information in pre-processing steps. We see the benefit of our approach not only at improved object boundaries, but also at smooth surface reconstruction, where we outperform even methods which focus on good surface regularization. We have evaluated our method on a public light-field dataset, where we achieve state-of-the-art results in nine out of twelve error metrics, with a close tie for the remaining three.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zern, A</style></author><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Freddie Aström</style></author><author><style face="normal" font="default" size="100%">Petra, S</style></author><author><style face="normal" font="default" size="100%">Schnörr, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">GCPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><pages><style face="normal" font="default" size="100%">698-713</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zern, A.</style></author><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">GCPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Esser</style></author><author><style face="normal" font="default" size="100%">Ekaterina Sutter</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Variational U-Net for Conditional Appearance and Shape Generation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (short Oral)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://compvis.github.io/vunet/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intricate interplay of their inherent shape and appearance. We present a conditional U-Net for shape-guided image generation, conditioned on the output of a variational autoencoder for appearance. The approach is trained end-to-end on images, without requiring samples of the same object with varying pose or appearance. Experiments show that the model enables conditional image generation and transfer. Therefore, either shape or appearance can be retained from a query image, while freely altering the other. Moreover, appearance can be sampled due to its stochastic latent representation, while preserving shape. In quantitative and qualitative experiments on COCO, DeepFashion, shoes, Market-1501 and handbags, the approach demonstrates significant improvements over the state-of-the-art.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roth, Nicolas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualization of Near-Surface Flow Patterns for Air-Water Gas Transfer</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">mastersMaster&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Blum, O.</style></author><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">X-GAN: Improving Generative Adversarial Networks with ConveX Combinations</style></title><secondary-title><style face="normal" font="default" size="100%">German Conference on Pattern Recognition (GCPR) (Oral)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">deep learning</style></keyword><keyword><style  face="normal" font="default" size="100%">generative adversarial network</style></keyword><keyword><style  face="normal" font="default" size="100%">generative model</style></keyword><keyword><style  face="normal" font="default" size="100%">variational auto-encoder</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><pub-location><style face="normal" font="default" size="100%">Stuttgart, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Even though recent neural architectures for image generation are capable of producing photo-realistic results, the overall distributions of real and faked images still differ a lot. While the lack of a structured latent representation for GANs often results in mode collapse, VAEs enforce a prior to the latent space that leads to an unnatural representation of the underlying real distribution. We introduce a method that preserves the natural structure of the latent manifold. By utilizing neighboring relations within the set of discrete real samples, we reproduce the full continuous latent manifold. We propose a novel image generation network X-GAN that creates latent input vectors from random convex combinations of adjacent real samples. This way we ensure a structured and natural latent space by not requiring prior assumptions. In our experiments, we show that our model outperforms recent approaches in terms of the missing mode problem while maintaining a high image quality.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alessandro Vianello</style></author><author><style face="normal" font="default" size="100%">Giulio Manfredi</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D reconstruction by a combined structure tensor and Hough transform light field approach</style></title><secondary-title><style face="normal" font="default" size="100%">tm - Technisches Messen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Disparity estimation using the structure tensor is a local approach to determine orientation in Epipolar Plane Images. A global extension would lead to more precise and robust estimations. In this work, a novel algorithm for 3D reconstruction from linear light fields is proposed. This method uses a modified version of the Progressive Probabilistic Hough Transform to extract orientations from Epipolar Plane Images, allowing to achieve high quality disparity maps. To this aim, the structure tensor estimates are used to speed up computation and improve the disparity estimation near occlusion boundaries. The new algorithm is evaluated on both synthetic and real light field datasets, and compared with classical local disparity estimation techniques based on the structure tensor.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Wojek, C.</style></author><author><style face="normal" font="default" size="100%">Schmidt, U.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Active machine learning for training an event classification</style></title><secondary-title><style face="normal" font="default" size="100%">Patent, Patent Number WO2017032775 A1</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jakob Kunz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Active Thermography as a Tool for the Estimation of Air-Water Transfer Velocities</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phd</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Groth, Oliver</style></author><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Ying</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analyzing modular CNN architectures for joint depth prediction and semantic segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - IEEE International Conference on Robotics and Automation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">feb</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1702.08009 http://dx.doi.org/10.1109/ICRA.2017.7989537</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">4620–4627</style></pages><isbn><style face="normal" font="default" size="100%">9781509046331</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper addresses the task of designing a modular neural network architecture that jointly solves different tasks. As an example we use the tasks of depth estimation and semantic segmentation given a single RGB image. The main focus of this work is to analyze the cross-modality influence between depth and semantic prediction maps on their joint refinement. While most of the previous works solely focus on measuring improvements in accuracy, we propose a way to quantify the cross-modality influence. We show that there is a relationship between final accuracy and cross-modality influence, although not a simple linear one. Hence a larger cross-modality influence does not necessarily translate into an improved accuracy. We find that a beneficial balance between the cross-modality influences can be achieved by network architecture and conjecture that this relationship can be utilized to understand different network design choices. Towards this end we propose a Convolutional Neural Network (CNN) architecture that fuses the state-of-the-art results for depth estimation and semantic labeling. By balancing the cross-modality influences between depth and semantic prediction, we achieve improved results for both tasks using the NYU-Depth v2 benchmark.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leonard Gerhard Holtmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufbau eines aktiven Thermographiesystems zur Messung des Geschwindigkeitsgradienten in der windgetriebenen wasserseitigen viskosen Grenzschicht</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">mastersBachelor&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Mustikovela, Siva Karthik</style></author><author><style face="normal" font="default" size="100%">Mescheder, Lars</style></author><author><style face="normal" font="default" size="100%">Geiger, Andreas</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Augmented reality meets deep learning for car instance segmentation in urban scenes</style></title><secondary-title><style face="normal" font="default" size="100%">British Machine Vision Conference 2017, BMVC 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><isbn><style face="normal" font="default" size="100%">190172560X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D shapes of the target object category. We demonstrate the utility of the proposed approach for training a state-of-the-art high-capacity deep model for semantic instance segmentation. In particular, we consider the task of segmenting car instances on the KITTI dataset which we have annotated with pixel-accurate ground truth. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amounts of annotated real data.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Behl, Aseem</style></author><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Mustikovela, Siva Karthik</style></author><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Geiger, Andreas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">2017-Octob</style></volume><pages><style face="normal" font="default" size="100%">2593–2602</style></pages><isbn><style face="normal" font="default" size="100%">9781538610329</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Behl, Aseem</style></author><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Mustikovela, Siva Karthik</style></author><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Geiger, Andreas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">2017-Octob</style></volume><pages><style face="normal" font="default" size="100%">2593–2602</style></pages><isbn><style face="normal" font="default" size="100%">9781538610329</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leonie Flothow</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bubble Characteristics from Breaking Waves in Fresh Water and Simulated Seawater</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">mastersMaster&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brosowsky, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cluster Resolving for Animal Tracking: Multi Hypotheses Tracking with Part Based Model for Object Hypotheses Generation and Pose Estimation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dalitz, R.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Compressed Motion Sensing</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. SSVM</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">10302</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krause, G</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Correlation of Performance and Entropy in Active Learning with Convolutional Neural Networks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter, S.</style></author><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Nadler, B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cost-efficient Gradient Boosting</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS, poster</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schlesinger, Dmitrij</style></author><author><style face="normal" font="default" size="100%">Jug, Florian</style></author><author><style face="normal" font="default" size="100%">Myers, Gene</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kainmueller, Dagmar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Crowd sourcing image segmentation with iaSTAPLE</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - International Symposium on Biomedical Imaging</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Crowdsourcing</style></keyword><keyword><style  face="normal" font="default" size="100%">Epithelial cell segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">IaSTAPLE</style></keyword><keyword><style  face="normal" font="default" size="100%">Markovian Random Fields</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><pages><style face="normal" font="default" size="100%">401–405</style></pages><isbn><style face="normal" font="default" size="100%">9781509011711</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a novel label fusion technique as well as a crowdsourcing protocol to efficiently obtain accurate epithelial cell segmentations from non-expert crowd workers. Our label fusion technique simultaneously estimates the true segmentation, the performance levels of individual crowd workers, and an image segmentation model in the form of a pairwise Markov random field. We term our approach image-aware STAPLE (iaSTAPLE) since our image segmentation model seamlessly integrates into the well-known and widely used STAPLE approach. In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations. These results show that iaSTAPLE is a highly useful tool for crowd sourcing image segmentation.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nikolai Ufer</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Semantic Feature Matching</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miguel Bautista</style></author><author><style face="normal" font="default" size="100%">Sanakoyeu, A.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Unsupervised Similarity Learning using Partially Ordered Sets</style></title><secondary-title><style face="normal" font="default" size="100%">The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hendrik Schilling</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Marcel Gutsche</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the design of a fractal calibration pattern for improved camera calibration</style></title><secondary-title><style face="normal" font="default" size="100%">tm - Technisches Messen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">84</style></volume><pages><style face="normal" font="default" size="100%">440–451</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ramos, Sebastian</style></author><author><style face="normal" font="default" size="100%">Gehrig, Stefan</style></author><author><style face="normal" font="default" size="100%">Pinggera, Peter</style></author><author><style face="normal" font="default" size="100%">Franke, Uwe</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Intelligent Vehicles Symposium, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">dec</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1612.06573</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1025–1032</style></pages><isbn><style face="normal" font="default" size="100%">9781509048045</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is proposed to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-The-Art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-The-Art, with a performance gain of 27.4%. In particular, we achieve a detection rate of over 90% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haubold, C.</style></author><author><style face="normal" font="default" size="100%">Uhlmann, V</style></author><author><style face="normal" font="default" size="100%">Michael Unser</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Diverse M-best Solutions by Dynamic Programming</style></title><secondary-title><style face="normal" font="default" size="100%">GCPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 10496</style></volume><pages><style face="normal" font="default" size="100%">255-267</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uhlmann, V</style></author><author><style face="normal" font="default" size="100%">Haubold, C.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Michael Unser</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Diverse Shortest Paths for Bioimage Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><pages><style face="normal" font="default" size="100%">1-3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Shotton, Jamie</style></author><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DSAC - Differentiable RANSAC for camera localization</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">nov</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1611.05705</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2017-Janua</style></volume><pages><style face="normal" font="default" size="100%">2492–2500</style></pages><isbn><style face="normal" font="default" size="100%">9781538604571</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">RANSAC is an important algorithm in robust optimization and a central building block for many computer vision applications. In recent years, traditionally hand-crafted pipelines have been replaced by deep learning pipelines, which can be trained in an end-to-end fashion. However, RANSAC has so far not been used as part of such deep learning pipelines, because its hypothesis selection procedure is non-differentiable. In this work, we present two different ways to overcome this limitation. The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w.r.t. to all learnable parameters. We call this approach DSAC, the differentiable counterpart of RANSAC. We apply DSAC to the problem of camera localization, where deep learning has so far failed to improve on traditional approaches. We demonstrate that by directly minimizing the expected loss of the output camera poses, robustly estimated by RANSAC, we achieve an increase in accuracy. In the future, any deep learning pipeline can use DSAC as a robust optimization component.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Christina Brandt</style></author><author><style face="normal" font="default" size="100%">Martin Hofmann</style></author><author><style face="normal" font="default" size="100%">Tobias Knopp</style></author><author><style face="normal" font="default" size="100%">Johannes Salamon</style></author><author><style face="normal" font="default" size="100%">Alexander Weber</style></author><author><style face="normal" font="default" size="100%">Andreas Weinmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Edge preserving and noise reducing reconstruction for magnetic particle imaging</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Medical Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">74 - 85</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Dennis Rickert</style></author><author><style face="normal" font="default" size="100%">Michael Unser</style></author><author><style face="normal" font="default" size="100%">Andreas Weinmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast segmentation from blurred data in 3D fluorescence microscopy</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2017</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">26</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">10</style></issue><section><style face="normal" font="default" size="100%"> 4856 - 4870</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Geometric Approach for Color Image Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Comp. Vision Image Understanding</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.cviu.2017.10.013</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">165</style></volume><pages><style face="normal" font="default" size="100%">43–59</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zern, A.</style></author><author><style face="normal" font="default" size="100%">Rohr, K.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometric Image Labeling with Global Convex Labeling Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. EMMCVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">to appear</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global hypothesis generation for 6D object pose estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">dec</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1612.02287</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2017-Janua</style></volume><pages><style face="normal" font="default" size="100%">115–124</style></pages><isbn><style face="normal" font="default" size="100%">9781538604571</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper addresses the task of estimating the 6D pose of a known 3D object from a single RGB-D image. Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool. This work focuses on the second step. While all existing approaches generate the hypotheses pool via local reasoning, e.g. RANSAC or Hough-voting, we are the first to show that global reasoning is beneficial at this stage. In particular, we formulate a novel fully-connected Conditional Random Field (CRF) that outputs a very small number of pose-hypotheses. Despite the potential functions of the CRF being non-Gaussian, we give a new and efficient two-step optimization procedure, with some guarantees for optimality. We utilize our global hypotheses generation procedure to produce results that exceed state-of-the-art for the challenging &quot;Occluded Object Dataset&quot;.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gradient Flows on a Riemannian Submanifold for Discrete Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. GCPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hühnerbein, R.</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1710.01493</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">arXiv, preprint</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schmitzer, B.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Labeling by Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">J. Math. Imag. Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Papers/Astroem2017.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">58</style></volume><pages><style face="normal" font="default" size="100%">211–238</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Reconstruction by Multilabel Propagation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. SSVM</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">10302</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hennies, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improvement and Validation of Neural EM Volume Image Segmentation by High-Level Information</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Levinkov, Evgeny</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">InstanceCut: From edges to instances with MultiCut</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">2017-Janua</style></volume><pages><style face="normal" font="default" size="100%">7322–7331</style></pages><isbn><style face="normal" font="default" size="100%">9781538604571</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work addresses the task of instance-aware semantic segmentation. Our key motivation is to design a simple method with a new modelling-paradigm, which therefore has a different trade-off between advantages and disadvantages compared to known approaches.Our approach, we term InstanceCut, represents the problem by two output modalities: (i) an instance-agnostic semantic segmentation and (ii) all instance-boundaries. The former is computed from a standard convolutional neural network for semantic segmentation, and the latter is derived from a new instanceaware edge detection model. To reason globally about the optimal partitioning of an image into instances, we combine these two modalities into a novel MultiCut formulation. We evaluate our approach on the challenging CityScapes dataset. Despite the conceptual simplicity of our approach, we achieve the best result among all published methods, and perform particularly well for rare object classes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haller, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interactive Watershed Based Segmentation for Biological Images</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Levinkov, Evgeny</style></author><author><style face="normal" font="default" size="100%">Uhrig, Jonas</style></author><author><style face="normal" font="default" size="100%">Tang, Siyu</style></author><author><style face="normal" font="default" size="100%">Omran, Mohamed</style></author><author><style face="normal" font="default" size="100%">Insafutdinov, Eldar</style></author><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Brox, Thomas</style></author><author><style face="normal" font="default" size="100%">Schiele, Bernt</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Joint graph decomposition &amp; node labeling: Problem, algorithms, applications</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">2017-Janua</style></volume><pages><style face="normal" font="default" size="100%">1904–1912</style></pages><isbn><style face="normal" font="default" size="100%">9781538604571</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We state a combinatorial optimization problem whose feasible solutions define both a decomposition and a node labeling of a given graph. This problem offers a common mathematical abstraction of seemingly unrelated computer vision tasks, including instance-separating semantic segmentation, articulated human body pose estimation and multiple object tracking. Conceptually, the problem we state generalizes the unconstrained integer quadratic program and the minimum cost lifted multicut problem, both of which are NP-hard. In order to find feasible solutions efficiently, we define two local search algorithms that converge monotonously to a local optimum, offering a feasible solution at any time. To demonstrate their effectiveness in tackling computer vision tasks, we apply these algorithms to instances of the problem that we construct from published data, using published algorithms. We report state-of-the-art application-specific accuracy for the three above-mentioned applications.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirillov, A</style></author><author><style face="normal" font="default" size="100%">Schlesinger, D</style></author><author><style face="normal" font="default" size="100%">Zheng, S</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B</style></author><author><style face="normal" font="default" size="100%">Torr, P. H.S.</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Joint training of generic CNN-CRF models with stochastic optimization</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://host.robots.ox.ac.uk:8080/leaderboard</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">10112 LNCS</style></volume><pages><style face="normal" font="default" size="100%">221–236</style></pages><isbn><style face="normal" font="default" size="100%">9783319541839</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e. it applies to arbitrary CNN and CRF architectures and potential functions; (ii) scalable, i.e. it has a low memory footprint and straightforwardly parallelizes on GPUs; (iii) easy in implementation. Additionally, the unified CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Andreas Weinmann</style></author><author><style face="normal" font="default" size="100%">Michael Unser</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Jump-penalized least absolute values estimation of scalar or circle-valued signals</style></title><secondary-title><style face="normal" font="default" size="100%">Information and Inference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">225–245</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">3</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schott, L</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learned Watershed Algorithm: End-to-End Learning of Seeded Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Schott, L</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learned Watershed: End-to-End Learning of Seeded Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">ICCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><pages><style face="normal" font="default" size="100%">2030-2038</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weiler, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Steerable Filters for Rotation Equivariant Convolutional Neural Networks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kruse, Jakob</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Schmidt, Uwe</style></author><author><style face="normal" font="default" size="100%">Dresden, T U</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning to Push the Limits of Efficient FFT-based Image Deconvolution - Supplemental Material</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">1. Details about boundary adjustment comparison Section 4.3 of the main paper compares our proposed boundary adjustment (BA) strategy (Our BA, cf. Eq. 17 and Fig. 2 of the main paper) to the traditional edgetapering method (ET once, cf. Eq. 11 of the main paper) and the BA approach (ET each) of CSF [3]; these BA strategies are compared within our FDN model, the CSF model, and a standard Wiener filter [5]. Specifically, we use the publicly available code to train different variants of the CSF model on a dataset of the same size as ours, and only adjust the BA strategy. Furthermore, we apply the Wiener filter as defined in Eq. 2 of the main paper, which we can use iteratively with our BA approach by replacing y with ϕ t (y, k, x t); we estimate the expected image spectrum n from 3000 clean image patches. While our BA comparison is depicted visually in Fig. 5 of the main paper, Table 1 also provides the numeric results and additionally includes stages 6-10 of our FDN model. As compared to the BA approach of CSF (ET each), the results suggest that CSF would also benefit from further stages if used with our BA strategy (cf. 6 th column). Remarkably, the performance of the Wiener filter is not even fully saturated after 50 iterations (Wiener 50) when applied with our BA approach (cf. 3 rd column, only every 5 th step shown after iteration 10). Fig. 1 shows an example where our proposed BA strategy yields a substantial improvement in image quality compared to standard edgetapering (ET once).</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kruse, Jakob</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Schmidt, Uwe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning to Push the Limits of Efficient FFT-Based Image Deconvolution</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><volume><style face="normal" font="default" size="100%">2017-Octob</style></volume><pages><style face="normal" font="default" size="100%">4596–4604</style></pages><isbn><style face="normal" font="default" size="100%">9781538610329</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work addresses the task of non-blind image deconvolution. Motivated to keep up with the constant increase in image size, with megapixel images becoming the norm, we aim at pushing the limits of efficient FFT-based techniques. Based on an analysis of traditional and more recent learning-based methods, we generalize existing discriminative approaches by using more powerful regularization, based on convolutional neural networks. Additionally, we propose a simple, yet effective, boundary adjustment method that alleviates the problematic circular convolution assumption, which is necessary for FFT-based deconvolution. We evaluate our approach on two common non-blind deconvolution benchmarks and achieve state-of-the-art results even when including methods which are computationally considerably more expensive.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miguel Bautista</style></author><author><style face="normal" font="default" size="100%">Fuchs, P.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Where to Drive by Watching Others</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the German Conference Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Basel</style></pub-location><volume><style face="normal" font="default" size="100%">1</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ecaterina Bodnariuc</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Voorneveld, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. GCPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, F</style></author><author><style face="normal" font="default" size="100%">Desana, M</style></author><author><style face="normal" font="default" size="100%">Schnörr, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><pages><style face="normal" font="default" size="100%">177-184</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, F.</style></author><author><style face="normal" font="default" size="100%">Desana, M.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. MICCAI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">Uta Büchler</style></author><author><style face="normal" font="default" size="100%">Anna-Sophia Wahl</style></author><author><style face="normal" font="default" size="100%">M. E. Schwab</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">LSTM Self-Supervision for Detailed Behavior Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">(BB and UB contributed equally)</style></publisher><abstract><style face="normal" font="default" size="100%">Behavior analysis provides a crucial non-invasive and
easily accessible diagnostic tool for biomedical research.
A detailed analysis of posture changes during skilled mo-
tor tasks can reveal distinct functional deficits and their
restoration during recovery. Our specific scenario is based
on a neuroscientific study of rodents recovering from a large
sensorimotor cortex stroke and skilled forelimb grasping is
being recorded. Given large amounts of unlabeled videos
that are recorded during such long-term studies, we seek
an approach that captures fine-grained details of posture
and its change during rehabilitation without costly manual
supervision. Therefore, we utilize self-supervision to au-
tomatically learn accurate posture and behavior represen-
tations for analyzing motor function. Learning our model
depends on the following fundamental elements: (i) limb
detection based on a fully convolutional network is ini-
tialized solely using motion information, (ii) a novel self-
supervised training of LSTMs using only temporal permu-
tation yields a detailed representation of behavior, and (iii)
back-propagation of this sequence representation also im-
proves the description of individual postures. We establish a
novel test dataset with expert annotations for evaluation of
fine-grained behavior analysis. Moreover, we demonstrate
the generality of our approach by successfully applying it to
self-supervised learning of human posture on two standard
benchmark datasets.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Hühnerbein, R.</style></author><author><style face="normal" font="default" size="100%">Savarino, F.</style></author><author><style face="normal" font="default" size="100%">Recknagel, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MAP Image Labeling Using Wasserstein Messages and Geometric Assignment</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. SSVM</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LCNS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">10302</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Wojek, C</style></author><author><style face="normal" font="default" size="100%">Schmidt, U.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Maschinelles Lernen</style></title><secondary-title><style face="normal" font="default" size="100%">Patent, Patent Number WO2017032775A1</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Balluff, B</style></author><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author><author><style face="normal" font="default" size="100%">Heeren, R. M. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mass spectrometry imaging for the investigation of intratumor heterogeneity</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Cancer Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><volume><style face="normal" font="default" size="100%">134</style></volume><pages><style face="normal" font="default" size="100%">201-230</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Clemens Haltebourg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling of Heat Exchange Across the Ocean Surface as Measured by Active Thermography</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Pape, C</style></author><author><style face="normal" font="default" size="100%">Rahaman, N</style></author><author><style face="normal" font="default" size="100%">Prange, T</style></author><author><style face="normal" font="default" size="100%">Stuart Berg</style></author><author><style face="normal" font="default" size="100%">Bock, D</style></author><author><style face="normal" font="default" size="100%">A. Cardona</style></author><author><style face="normal" font="default" size="100%">G. W. Knott</style></author><author><style face="normal" font="default" size="100%">Plaza, S M</style></author><author><style face="normal" font="default" size="100%">Scheffer, L K</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A</style></author><author><style face="normal" font="default" size="100%">Fred A. 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M.</style></author><author><style face="normal" font="default" size="100%">Dzyubachyk, O.</style></author><author><style face="normal" font="default" size="100%">Lelieveldt, B.</style></author><author><style face="normal" font="default" size="100%">Xiao, P.</style></author><author><style face="normal" font="default" size="100%">Li, Y.</style></author><author><style face="normal" font="default" size="100%">Cho, S-Y.</style></author><author><style face="normal" font="default" size="100%">Dufour, A.</style></author><author><style face="normal" font="default" size="100%">Olivo-Marin, J. C.</style></author><author><style face="normal" font="default" size="100%">Reyes-Aldasoro, C. C.</style></author><author><style face="normal" font="default" size="100%">Solis-Lemus, J. 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Brändli</style></author><author><style face="normal" font="default" size="100%">Biagio Brattoli</style></author><author><style face="normal" font="default" size="100%">S. Musall</style></author><author><style face="normal" font="default" size="100%">H. Kasper</style></author><author><style face="normal" font="default" size="100%">B.V. Ineichen</style></author><author><style face="normal" font="default" size="100%">F. Helmchen</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">M. E. Schwab</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Communications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.nature.com/articles/s41467-017-01090-6</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">(ASW &amp; UB contributed equally; BO and MES contributed equally)</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Shotton, Jamie</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">dec</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1612.03779</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2017-Janua</style></volume><pages><style face="normal" font="default" size="100%">2566–2574</style></pages><isbn><style face="normal" font="default" size="100%">9781538604571</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">State-of-the-art computer vision algorithms often achieve efficiency by making discrete choices about which hypotheses to explore next. This allows allocation of computational resources to promising candidates, however, such decisions are non-differentiable. As a result, these algorithms are hard to train in an end-to-end fashion. In this work we propose to learn an efficient algorithm for the task of 6D object pose estimation. Our system optimizes the parameters of an existing state-of-the art pose estimation system using reinforcement learning, where the pose estimation system now becomes the stochastic policy, parametrized by a CNN. Additionally, we present an efficient training algorithm that dramatically reduces computation time. We show empirically that our learned pose estimation procedure makes better use of limited resources and improves upon the state-of-the-art on a challenging dataset. Our approach enables differentiable end-to-end training of complex algorithmic pipelines and learns to make optimal use of a given computational budget.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hehn, T</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A probabilistic approach to learn complex differentiable split functions in decision trees using gradient ascent</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Massiceti, Daniela</style></author><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Torr, Philip H S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Random Forests versus Neural Networks − What&#039;s best for camera location</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work addresses the task of camera localiza-tion in a known 3D scene given a single input RGB image. State-of-the-art approaches accomplish this in two steps: firstly, regressing for every pixel in the image its 3D scene coordinate and subsequently, using these coordinates to estimate the final 6D camera pose via RANSAC. To solve the first step, Random Forests (RFs) are typically used. On the other hand, Neural Networks (NNs) reign in many dense regression tasks, but are not test-time efficient. We ask the question: which of the two is best for camera localization? To address this, we make two method contributions: (1) a test-time efficient NN architecture which we term a ForestNet that is derived and initialized from a RF, and (2) a new fully-differentiable robust averaging technique for regression ensembles which can be trained end-to-end with a NN. Our experimental findings show that for scene coordinate regression, traditional NN architectures are superior to test-time efficient RFs and ForestNets, however, this does not translate to final 6D camera pose accuracy where RFs and ForestNets perform slightly better. To summarize, our best method, a ForestNet with a robust average, which has an equivalent fast and lightweight RF, improves over the state-of-the-art for camera localization on the 7-Scenes dataset [1]. While this work focuses on scene coordinate regression for camera localization, our innovations may also be applied to other continuous regression tasks.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alessandro Vianello</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust 3D Surface Reconstruction from Light Fields</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haubold, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scalable Inference for Multi-Target Tracking on Proliferating Cells</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johannes Berger</style></author><author><style face="normal" font="default" size="100%">Lenzen, F.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Neufeld, A.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations</style></title><secondary-title><style face="normal" font="default" size="100%">J. Math. Imag. Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">58</style></volume><pages><style face="normal" font="default" size="100%">102–129</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markowsky, P.</style></author><author><style face="normal" font="default" size="100%">Reith, S.</style></author><author><style face="normal" font="default" size="100%">Zuber, T.E.</style></author><author><style face="normal" font="default" size="100%">König, R.</style></author><author><style face="normal" font="default" size="100%">Rohr, K.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Segmentation of cell structure using model-based set covering with iterative reweighting</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. ISBI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Neigel, P</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-Similarity Based Detection of Temporal Motifs in Multivariate Signals</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ömer Sümer</style></author><author><style face="normal" font="default" size="100%">Tobias Dencker</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self-supervised Learning of Pose Embeddings from Spatiotemporal Relations in Videos</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Human pose analysis is presently dominated by deep convolutional networks trained with extensive manual annotations of joint locations and beyond. To avoid the need for expensive labeling, we exploit spatiotemporal relations in training videos for self-supervised learning of pose embeddings. The key idea is to combine temporal ordering and spatial placement estimation as auxiliary tasks for learning pose similarities in a Siamese convolutional network. Since the self-supervised sampling of both tasks from natural videos can result in ambiguous and incorrect training labels, our method employs a curriculum learning idea that starts training with the most reliable data samples and gradually increases the difficulty. To further refine the training process we mine repetitive poses in individual videos which provide reliable labels while removing inconsistencies. Our pose embeddings capture visual characteristics of human pose that can boost existing supervised representations in human pose estimation and retrieval. We report quantitative and qualitative results on these tasks in Olympic Sports, Leeds Pose Sports and MPII Human Pose datasets.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hullin, M</style></author><author><style face="normal" font="default" size="100%">Klein, R</style></author><author><style face="normal" font="default" size="100%">Schultz, T</style></author><author><style face="normal" font="default" size="100%">Yao, A</style></author><author><style face="normal" font="default" size="100%">Weihao Li</style></author><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic-Aware Image Smoothing</style></title><secondary-title><style face="normal" font="default" size="100%">Vision, Modeling, and Visualization</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Enhancement— Smoothing</style></keyword><keyword><style  face="normal" font="default" size="100%">I43 [Image Processing and Computer Vision]</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Structure-preserving image smoothing aims to extract semantically meaningful image structure from texture, which is one of the fundamental problems in computer vision and graphics. However, it is still not clear how to define this concept. On the other hand, semantic image labeling has achieved significant progress recently and has been widely used in many computer vision tasks. In this paper, we present an interesting observation, i.e. high-level semantic image labeling information can provide a meaningful structure prior naturally. Based on this observation, we propose a simple and yet effective method, which we term semantic smoothing, by exploiting the semantic information to accomplish semantically structure-preserving image smoothing. We show that our approach outperforms the state-of-the-art approaches in texture removal by considering the semantic infor-mation for structure preservation. Also, we apply our approach to three applications: detail enhancement, edge detection, and image segmentation, and we demonstrate the effectiveness of our semantic smoothing method on these problems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pape, C</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Li, P</style></author><author><style face="normal" font="default" size="100%">Jain, V</style></author><author><style face="normal" font="default" size="100%">Brock, D. D.</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Solving Large Multicut Problems for Connectomics via Domain Decomposition</style></title><secondary-title><style face="normal" font="default" size="100%">Bioimage Computing Workshop. ICCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><pages><style face="normal" font="default" size="100%">1-10</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter, S.</style></author><author><style face="normal" font="default" size="100%">Kirschbaum, E.</style></author><author><style face="normal" font="default" size="100%">M. Both</style></author><author><style face="normal" font="default" size="100%">Campbell, L. A.</style></author><author><style face="normal" font="default" size="100%">Harvey, B. K.</style></author><author><style face="normal" font="default" size="100%">Heins, C.</style></author><author><style face="normal" font="default" size="100%">Durstewitz, D.</style></author><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sparse convolutional coding for neuronal assembly detection</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS, poster</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreas Rennebaum</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-Temporal Properties of the initial Wave Formation Phase at the Aeolotron</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">mastersMaster&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Timo Milbich</style></author><author><style face="normal" font="default" size="100%">Miguel Bautista</style></author><author><style face="normal" font="default" size="100%">Ekaterina Sutter</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Video Understanding by Reconciliation of Posture Similarities</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><custom1><style face="normal" font="default" size="100%">https://hciweb.iwr.uni-heidelberg.de/compvis/research/tmilbich_iccv17
</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Kandemir, M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Bayesian Multiple Instance Learning with Gaussian Processes</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><pages><style face="normal" font="default" size="100%">6570-6579</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Najy von Schmude</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visual Localization with Lines</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phd</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alessandro Vianello</style></author><author><style face="normal" font="default" size="100%">Giulio Manfredi</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D Reconstruction by a Combined Structure Tensor and Hough Transform Light-Field Approach</style></title><secondary-title><style face="normal" font="default" size="100%">Forum Bildverarbeitung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.5445/KSP/1000059899</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">978-3-7315-0587-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Disparity estimation using the structure tensor is a local approach to determine orientation in Epipolar Plane Images. A global extension would lead to more precise and robust estimations. In this work, a novel algorithm for 3D reconstruction from linear light fields is porposed. It uses a modified Progressive Probabilistic Hough Transform, in combination with the structure tensor, to extract orientations from Epipolar Plane Images edge maps, allowing to achieve high quality disparity maps.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jakob Kunz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Active thermography as a tool to investigate heat and gas transfer across the air-water interface</style></title><secondary-title><style face="normal" font="default" size="100%">13th Quantitative Infrared Thermographie Conference (QIRT 2016), Gdansk 4–8 July 2016</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Proß, Christin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of the Fetch Dependency of the Slope of Wind-Water Waves</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this thesis mean square slope has been calculated from slope images which were recorded by the Imaging Slope Gauge (ISG) at the annular wind-wave tank Aeolotron in Heidelberg. The calculations have been realized using three different methods, which are, (i) calculation of the variance, (ii) integration of the slope power spectrum and (iii) fitting the probability distribution function of slope with a model function. The resulting values have been compared to each other and to the existing live evaluation of the ISG for a wide range of wind and fetch conditions. Also the fetch dependence of mean square slope has been analyzed, which obtains information about the evolution of a wave field. Additionally the slope images have been separated with the use of band pass filters into slope images of gravity waves and capillary waves. By separating gravity from capillary waves it was possible to analyze their slope probability distribution functions individually.</style></abstract><work-type><style face="normal" font="default" size="100%">mastersBachelor&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schmitzer, B.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Assignment Manifold: A Smooth Model for Image Labeling</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML&#039;16; oral presentation; Grenander best paper award)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krasowski, N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pape, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic Segmentation of Neurites from Anisotropic EM-Imaging</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prange, T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic Segmentation of Neurons in Electron Microscopy Data with Membrane Defects</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mustikovela, Siva Karthik</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Ying</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Can ground truth label propagation from video help semantic segmentation?</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">9915 LNCS</style></volume><pages><style face="normal" font="default" size="100%">804–820</style></pages><isbn><style face="normal" font="default" size="100%">9783319494081</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">For state-of-the-art semantic segmentation task, training convolutional neural networks (CNNs) requires dense pixelwise ground truth (GT) labeling, which is expensive and involves extensive human effort. In this work, we study the possibility of using auxiliary ground truth, so-called pseudo ground truth (PGT) to improve the performance. The PGT is obtained by propagating the labels of a GT frame to its subsequent frames in the video using a simple CRF-based, cue integration framework. Our main contribution is to demonstrate the use of noisy PGT along with GT to improve the performance of a CNN. We perform a systematic analysis to find the right kind of PGT that needs to be added along with the GT for training a CNN. In this regard, we explore three aspects of PGT which influence the learning of a CNN: (i) the PGT labeling has to be of good quality; (ii) the PGT images have to be different compared to the GT images; (iii) the PGT has to be trusted differently than GT. We conclude that PGT which is diverse from GT images and has good quality of labeling can indeed help improve the performance of a CNN. Also, when PGT is multiple folds larger than GT, weighing down the trust on PGT helps in improving the accuracy. Finally, We show that using PGT along with GT, the performance of Fully Convolutional Network (FCN) on Camvid data is increased by 2.7% on IoU accuracy. We believe such an approach can be used to train CNNs for semantic video segmentation where sequentially labeled image frames are needed. To this end, we provide recommendations for using PGT strategically for semantic segmentation and hence bypass the need for extensive human efforts in labeling.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cell Tracking With Graphical Model Using Higher Order Features On Track Segments</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miguel Bautista</style></author><author><style face="normal" font="default" size="100%">Sanakoyeu, A.</style></author><author><style face="normal" font="default" size="100%">Sutter, E.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CliqueCNN: Deep Unsupervised Exemplar Learning</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Conference on Advances in Neural Information Processing Systems (NIPS)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1608.08792</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Barcelona</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Exemplar learning is a powerful paradigm for discovering visual similarities in
an unsupervised manner.  In this context, however, the recent breakthrough in
deep learning could not yet unfold its full potential.  With only a single positive
sample, a great imbalance between one positive and many negatives, and unreliable
relationships between most samples, training of Convolutional Neural networks is
impaired. Given weak estimates of local distance we propose a single optimization
problem to extract batches of samples with mutually consistent relations. Conflict-
ing relations are distributed over different batches and similar samples are grouped
into compact cliques. Learning exemplar similarities is framed as a sequence of
clique categorization tasks. The CNN then consolidates transitivity relations within
and between cliques and learns a single representation for all samples without
the need for labels. The proposed unsupervised approach has shown competitive
performance on detailed posture analysis and object classification.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Baust</style></author><author><style face="normal" font="default" size="100%">Andreas Weinmann</style></author><author><style face="normal" font="default" size="100%">Matthias Wieczorek</style></author><author><style face="normal" font="default" size="100%">Tobias Lasser</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Nassir Navab</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined Tensor Fitting and TV Regularization in Diffusion Tensor Imaging based on a Riemannian Manifold Approach</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Medical Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">1972–1989</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">8</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Royer, Loic A.</style></author><author><style face="normal" font="default" size="100%">Richmond, David L.</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Kainmueller, Dagmar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Convexity shape constraints for image segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">sep</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1509.02122</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2016-Decem</style></volume><pages><style face="normal" font="default" size="100%">402–410</style></pages><isbn><style face="normal" font="default" size="100%">9781467388504</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Segmenting an image into multiple components is a central task in computer vision. In many practical scenarios, prior knowledge about plausible components is available. Incorporating such prior knowledge into models and algorithms for image segmentation is highly desirable, yet can be non-trivial. In this work, we introduce a new approach that allows, for the first time, to constrain some or all components of a segmentation to have convex shapes. Specifically, we extend the Minimum Cost Multicut Problem by a class of constraints that enforce convexity. To solve instances of this NP-hard integer linear program to optimality, we separate the proposed constraints in the branch-and-cut loop of a state-of-the-art ILP solver. Results on photographs and micrographs demonstrate the effectiveness of the approach as well as its advantages over the state-of-the-art heuristic.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Güssefeld, Burkhard</style></author><author><style face="normal" font="default" size="100%">Katrin Honauer</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Optical flow ground truth generated by computer graphics has many advantages. For example, we can systematically vary scene parameters to understand algorithm sensitivities. But is synthetic ground truth realistic enough? Appropriate material models have been established as one of the major challenges for the creation of synthetic datasets: previous research has shown that highly sophisticated reflectance field acquisition methods yield results, which various optical flow methods cannot distinguish from real scenes. However, such methods are costly both in acquisition and rendering time and thus infeasible for large datasets. In this paper we find the simplest reflectance models (RM) for different groups of materials which still provide sufficient accuracy for optical flow performance analysis. It turns out that a spatially varying Phong RM is sufficient for simple materials. Normal estimation combined with Anisotropic RM can handle even very complex materials.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katrin Honauer</style></author><author><style face="normal" font="default" size="100%">Ole Johannsen</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lai, Shang-Hong</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><isbn><style face="normal" font="default" size="100%">978-3-319-54186-0</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In computer vision communities such as stereo, optical flow, or visual tracking, commonly accepted and widely used benchmarks have enabled objective comparison and boosted scientific progress. In  the  emergent  light  field  community,  a  comparable  benchmark  and evaluation methodology is still missing. The performance of newly proposed methods is often demonstrated qualitatively on a handful of images, making quantitative comparison and targeted progress very difficult. To overcome these difficulties, we propose a novel light field benchmark. We provide 24 carefully designed synthetic, densely sampled 4D light fields with highly accurate disparity ground truth. We thoroughly evaluate four state-of-the-art light field algorithms and one multi-view stereo algorithm using existing and novel error measures. This consolidated state-of-the art  may serve as a baseline to stimulate and guide further scientific progress. We publish the benchmark website http://www.lightfield-analysis.net , an evaluation toolkit, and our rendering setup to encourage submissions of both algorithms and further datasets.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, P</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Learning for Bioimage Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Balles, L</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep Learning for Diabetic Retinopathy Diagnostics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kleesiek, J</style></author><author><style face="normal" font="default" size="100%">Urban, G</style></author><author><style face="normal" font="default" size="100%">Hubert, A</style></author><author><style face="normal" font="default" size="100%">Schwarz, D</style></author><author><style face="normal" font="default" size="100%">Maier-Hein, K</style></author><author><style face="normal" font="default" size="100%">M. Bendszus</style></author><author><style face="normal" font="default" size="100%">A. Biller</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.</style></title><secondary-title><style face="normal" font="default" size="100%">NeuroImage</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">129</style></volume><pages><style face="normal" font="default" size="100%">460-469</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bell, P.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digital Connoisseur? How Computer Vision Supports Art History</style></title><secondary-title><style face="normal" font="default" size="100%">Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro &amp; S. Albl (ed.)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Artemide</style></publisher><pub-location><style face="normal" font="default" size="100%">Rome</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, Cl.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. GCPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Double-Opponent Vectorial Total Variation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. ECCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Swoboda, P.</style></author><author><style face="normal" font="default" size="100%">Kuske, J.</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv, preprint</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/pdf/1612.05460.pdf</style></url></web-urls></urls></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 9906</style></volume><pages><style face="normal" font="default" size="100%">715-730</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Desana, M.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1604.07243</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">ArXiv</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hendrik Schilling</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Marcel Gutsche</style></author><author><style face="normal" font="default" size="100%">Hamza Aziz-Ahmad</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A fractal calibration pattern for improved camera calibration</style></title><secondary-title><style face="normal" font="default" size="100%">Forum Bildverarbeitung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.5445/KSP/1000059899</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Camera calibration, crucial for computer vision tasks, often relies on planar calibration targets to calibrate the camera parameters. This work explores a planar, fractal, self-identifying calibration pattern, which provides a high density of calibration points for a large range of magnification factors. An evaluation on ground truth data shows the target provides very high accuracy over a wide range of conditions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. von Borstel</style></author><author><style face="normal" font="default" size="100%">Kandemir, M</style></author><author><style face="normal" font="default" size="100%">Schmidt, P</style></author><author><style face="normal" font="default" size="100%">Rao, M</style></author><author><style face="normal" font="default" size="100%">Rajamani, K</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gaussian process density counting from weak supervision</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 9905</style></volume><pages><style face="normal" font="default" size="100%">365-380 </style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haubold, C.</style></author><author><style face="normal" font="default" size="100%">Ales, J</style></author><author><style face="normal" font="default" size="100%">Wolf, S</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Generalized Successive Shortest Paths Solver for Tracking Dividing Targets</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 9911</style></volume><pages><style face="normal" font="default" size="100%">566-582</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Geometric Approach to Color Image Regularization</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1605.05977</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">ArXiv, preprint</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schmitzer, B.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Geometric Approach to Image Labeling</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. ECCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Nair, Rahul</style></author><author><style face="normal" font="default" size="100%">Katrin Honauer</style></author><author><style face="normal" font="default" size="100%">Karsten Krispin</style></author><author><style face="normal" font="default" size="100%">Jonas Andrulis</style></author><author><style face="normal" font="default" size="100%">Alexander Brock</style></author><author><style face="normal" font="default" size="100%">Güssefeld, Burkhard</style></author><author><style face="normal" font="default" size="100%">Mohsen Rahimimoghaddam</style></author><author><style face="normal" font="default" size="100%">Sabine Hofmann</style></author><author><style face="normal" font="default" size="100%">Brenner, Claus</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving</style></title><secondary-title><style face="normal" font="default" size="100%">The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%"> Recent advances in autonomous driving require more and more highly realistic reference data, even for difficult situations such as low light and bad weather. We present a new stereo and optical flow dataset to complement existing benchmarks. It was specifically designed to be representative for urban autonomous driving, including realistic, systematically varied radiometric and geometric challenges which were previously unavailable. The accuracy of the ground truth is evaluated based on Monte Carlo simulations yielding full, per-pixel distributions. Interquartile ranges are used as uncertainty measure to create binary masks for arbitrary accuracy thresholds and show that we achieved uncertainties better than those reported for comparable outdoor benchmarks. Binary masks for all dynamically moving regions are supplied with estimated stereo and flow values. An initial public benchmark dataset of 55 manually selected sequences between 19 and 100 frames long are made available in a dedicated website featuring interactive tools for database search, visualization, comparison and benchmarking.
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Alexander Gatto</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Heterogeneous Light Fields</style></title><secondary-title><style face="normal" font="default" size="100%">2016 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1109/CVPR.2016.193</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In contrast to traditional binocular or multi-view stereo approaches, the adequately sampled space of observations in light-field imaging allows, to obtain dense and high quality depth maps. It also extends capabilities beyond those of traditional methods. Previously, constant intensity has been assumed for estimating disparity of orientation in most approaches to analyze epipolar plane images (EPIs). Here, we introduce a modified structure tensor approach which improves depth estimation. This extension also includes a model of non-constant intensity on EPI manifolds. We derive an approach to estimate high quality depth maps in luminance-gradient light fields, as well as in color-filtered light fields. Color-filtered light fields pose particular challenges due to the fact that structures can change significantly in appearance with wavelength and can completely vanish at some wavelength. We demonstrate solutions to this challenge and obtain a dense sRGB image reconstruction in addition to dense depth maps.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, J.</style></author><author><style face="normal" font="default" size="100%">Speth, M.</style></author><author><style face="normal" font="default" size="100%">Reinelt, G.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Higher-order Segmentation via Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">Comp. Vision Image Understanding</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">143</style></volume><pages><style face="normal" font="default" size="100%">104–119</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Aström, F.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schmitzer, B.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Labeling by Assignment</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">March</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1603.05285</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">ArXiv, preprint</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meijering, E</style></author><author><style face="normal" font="default" size="100%">Carpenter, A E</style></author><author><style face="normal" font="default" size="100%">Peng, Hanchuan</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Olivo-Marin, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Imagining the future of bioimage analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Biotechnology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">1250-1255</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">12</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Censor, Y.</style></author><author><style face="normal" font="default" size="100%">Gibali, A.</style></author><author><style face="normal" font="default" size="100%">Lenzen, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising</style></title><secondary-title><style face="normal" font="default" size="100%">J. Comp. Math.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">608-623</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Biller</style></author><author><style face="normal" font="default" size="100%">Badde, S</style></author><author><style face="normal" font="default" size="100%">Nagel, A</style></author><author><style face="normal" font="default" size="100%">Neumann, JO</style></author><author><style face="normal" font="default" size="100%">Wick, W</style></author><author><style face="normal" font="default" size="100%">Hertenstein, A</style></author><author><style face="normal" font="default" size="100%">M. Bendszus</style></author><author><style face="normal" font="default" size="100%">Sahm, F</style></author><author><style face="normal" font="default" size="100%">Benkhedah, N</style></author><author><style face="normal" font="default" size="100%">Kleesiek, J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression</style></title><secondary-title><style face="normal" font="default" size="100%">American Journal of Neuroradiology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">37 </style></volume><pages><style face="normal" font="default" size="100%">66-73</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mund, Johannes</style></author><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Dieke-Meier, Franziska</style></author><author><style face="normal" font="default" size="100%">Fricke, Hartmut</style></author><author><style face="normal" font="default" size="100%">Meyer, Lothar</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations</style></title><secondary-title><style face="normal" font="default" size="100%">International Symposium on Enhanced Solutions for Aircraft and Vehicle Surveillance Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">- lidar</style></keyword><keyword><style  face="normal" font="default" size="100%">3d point cloud</style></keyword><keyword><style  face="normal" font="default" size="100%">aircraft</style></keyword><keyword><style  face="normal" font="default" size="100%">airport ground surveillance</style></keyword><keyword><style  face="normal" font="default" size="100%">apron control</style></keyword><keyword><style  face="normal" font="default" size="100%">apron control rely on</style></keyword><keyword><style  face="normal" font="default" size="100%">classification</style></keyword><keyword><style  face="normal" font="default" size="100%">current legacy procedures for</style></keyword><keyword><style  face="normal" font="default" size="100%">laser scanning</style></keyword><keyword><style  face="normal" font="default" size="100%">pose estimation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://goo.gl/28Yoqh</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Current procedures for conventional and remote airport ground control still rely on the direct (camera-) view. Despite further support by different Radar applications occasional shortcomings in the awareness of the responsible controllers may occur, particularly under adverse weather conditions, giving rise to capacity backlogs, incidents and accidents. As Laser scanners and computer vision algorithms have reached new performance levels in recent years, we proposed a novel concept for complete and independent airport apron surveillance based on LiDAR 3D point data. In this paper we extend our object detection/segmentation technique by addressing object classification in LiDAR 3D scans. We hereby enable LiDAR`s unique capability to classify non-cooperative objects by means of a single sensor and learned model knowledge. Our technique was able to classify and to estimate the poses of an Airbus A319-100 and a Boeing B737-700 parked on the airport apron. In the future we will enhance our classification technique to a wider range of objects including moving ground vehicles and pedestrians.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johannes Berger</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Joint Recursive Monocular Filtering of Camera Motion and Disparity Map</style></title><secondary-title><style face="normal" font="default" size="100%">38th German Conference on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://arxiv.org/abs/1606.02092</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Hannover</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom2><style face="normal" font="default" size="100%">in press</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johannes Berger</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Joint Recursive Monocular Filtering of Camera Motion and Disparity Map</style></title><secondary-title><style face="normal" font="default" size="100%">38th German Conference on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">in press</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anca Stefanoiu</style></author><author><style face="normal" font="default" size="100%">Andreas Weinmann</style></author><author><style face="normal" font="default" size="100%">Martin Storath</style></author><author><style face="normal" font="default" size="100%">Nassir Navab</style></author><author><style face="normal" font="default" size="100%">Maximilian Baust</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Joint Segmentation and Shape Regularization with a Generalized Forward Backward Algorithm</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Image Processing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Active contours</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Manifolds</style></keyword><keyword><style  face="normal" font="default" size="100%">Shape</style></keyword><keyword><style  face="normal" font="default" size="100%">Three-dimensional displays</style></keyword><keyword><style  face="normal" font="default" size="100%">TV</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">3384 - 3394</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">7</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schiegg, M.</style></author><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Diverse Models: The Coulomb Structured Support Vector Machine</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 9907</style></volume><pages><style face="normal" font="default" size="100%">585-599</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. von Borstel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning to Count from Weak Supervision</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Light-Field Imaging and Heterogeneous Light Fields</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phd</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pinggera, Peter</style></author><author><style face="normal" font="default" size="100%">Ramos, Sebastian</style></author><author><style face="normal" font="default" size="100%">Gehrig, Stefan</style></author><author><style face="normal" font="default" size="100%">Franke, Uwe</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Mester, Rudolf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Lost and found: Detecting small road hazards for self-driving vehicles</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Intelligent Robots and Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.6d-vision.com/lostandfounddataset</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2016-Novem</style></volume><pages><style face="normal" font="default" size="100%">1099–1106</style></pages><isbn><style face="normal" font="default" size="100%">9781509037629</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Detecting small obstacles on the road ahead is a critical part of the driving task which has to be mastered by fully autonomous cars. In this paper, we present a method based on stereo vision to reliably detect such obstacles from a moving vehicle. The proposed algorithm performs statistical hypothesis tests in disparity space directly on stereo image data, assessing freespace and obstacle hypotheses on independent local patches. This detection approach does not depend on a global road model and handles both static and moving obstacles. For evaluation, we employ a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic4. The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery. Small obstacles down to the height of 5 cm can successfully be detected at 20 m distance at low false positive rates.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richmond, David L.</style></author><author><style face="normal" font="default" size="100%">Kainmueller, Dagmar</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Y.</style></author><author><style face="normal" font="default" size="100%">Myers, Eugene W.</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping auto-context decision forests to deep convnets for semantic segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">British Machine Vision Conference 2016, BMVC 2016</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">jul</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1507.07583</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2016-Septe</style></volume><pages><style face="normal" font="default" size="100%">144.1–144.12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richmond, David L</style></author><author><style face="normal" font="default" size="100%">Kainmueller, Dagmar</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Y</style></author><author><style face="normal" font="default" size="100%">Myers, Eugene W</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping auto-context decision forests to deep convnets for semantic segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">British Machine Vision Conference 2016, BMVC 2016</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://github.com/BVLC/caffe/wiki/Model-Zoo\#fcn</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2016-Septe</style></volume><pages><style face="normal" font="default" size="100%">144.1–144.12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richmond, David L</style></author><author><style face="normal" font="default" size="100%">Kainmueller, Dagmar</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Y</style></author><author><style face="normal" font="default" size="100%">Myers, Eugene W</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping auto-context decision forests to deep convnets for semantic segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">British Machine Vision Conference 2016, BMVC 2016</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">2016-Septe</style></volume><pages><style face="normal" font="default" size="100%">144.1–144.12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the task of pixel-wise semantic segmentation given a small set of labeled training images. Among two of the most popular techniques to address this task are Random Forests (RF) and Neural Networks (NN). In this work, we explore the relationship between two special forms of these techniques: stacked RFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our main contribution is to show that Auto-context can be mapped to a deep ConvNet with novel architecture, and thereby trained end-to-end. This mapping can be viewed as an intelligent initialization of a deep ConvNet, enabling training even in the face of very limited amounts of training data. We also demonstrate an approximate mapping back from the refined ConvNet to a second stacked RF, with improved performance over the original. We experimentally verify that these mappings outperform stacked RFs for two different applications in computer vision and biology: Kinect-based body part labeling from depth images, and somite segmentation in microscopy images of developing zebrafish.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Strouse, Thomas M.D</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Marijuana&#039;s Public Health Pros and Cons | For Better | US News</style></title><secondary-title><style face="normal" font="default" size="100%">U.S. News and World Report</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://health.usnews.com/health-news/patient-advice/articles/2016-10-12/marijuanas-public-health-pros-and-cons</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">978-1-4244-4420-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">User-provided object bounding box is a simple and popular interaction paradigm considered by many existing interactive image segmentation frameworks. However, these frameworks tend to exploit the provided bounding box merely to exclude its exterior from consideration and sometimes to initialize the energy minimization. In this paper, we discuss how the bounding box can be further used to impose a powerful topological prior, which prevents the solution from excessive shrinking and ensures that the user-provided box bounds the segmentation in a sufficiently tight way. The prior is expressed using hard constraints incorporated into the global energy minimization framework leading to an NP-hard integer program. We then investigate the possible optimization strategies including linear relaxation as well as a new graph cut algorithm called pinpointing. The latter can be used either as a rounding method for the fractional LP solution, which is provably better than thresholding-based rounding, or as a fast standalone heuristic. We evaluate the proposed algorithms on a publicly available dataset, and demonstrate the practical benefits of the new prior both qualitatively and quantitatively.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephan Lenor</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based Estimation of Meteorological Visibility in the Context of Automotive Camera Systems</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phd</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, Jorg Hendrik</style></author><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Hazan, Tamir</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multicuts and Perturb &amp; MAP for Probabilistic Graph Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Mathematical Imaging and Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Correlation clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">graphical models</style></keyword><keyword><style  face="normal" font="default" size="100%">Multicut</style></keyword><keyword><style  face="normal" font="default" size="100%">Perturb and MAP</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">jan</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1601.02088</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">56</style></volume><pages><style face="normal" font="default" size="100%">221–237</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a probabilistic graphical model formulation for the graph clustering problem. This enables us to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. We exploit recent progress on globally optimal MAP inference by integer programming and on perturbation-based approximations of the log-partition function, in order to sample clusterings and to estimate marginal distributions of node-pairs both more accurately and more efficiently than state-of-the-art methods. Our approach works for any graphically represented problem instance. This is demonstrated for image segmentation and social network cluster analysis. Our mathematical ansatz should be relevant also for other combinatorial problems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, J.H.</style></author><author><style face="normal" font="default" size="100%">Swoboda, P.</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Hazan, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multicuts and Perturb &amp; MAP for Probabilistic Graph Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">J. Math. Imag. Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">56</style></volume><pages><style face="normal" font="default" size="100%">221–237</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Martin Schwarzbauer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Noise equalisation and quasi loss-less image data compression – or how many bits needs an image sensor?</style></title><secondary-title><style face="normal" font="default" size="100%">tm – Technisches Messen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">83</style></volume><pages><style face="normal" font="default" size="100%">16–24</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Zisler</style></author><author><style face="normal" font="default" size="100%">Kappes, J.H.</style></author><author><style face="normal" font="default" size="100%">Schnörr, Cl.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-Binary Discrete Tomography by Continuous Non-Convex Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Comp. Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">335-347</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ecaterina Bodnariuc</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Poelma, C.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parametric Dictionary-Based Velocimetry for Echo PIV</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. CGPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Shekhovtsov, Alexander</style></author><author><style face="normal" font="default" size="100%">Kappes, Jorg Hendrik</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Partial Optimality by Pruning for MAP-Inference with General Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">energy minimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Local polytope</style></keyword><keyword><style  face="normal" font="default" size="100%">MAP-inference</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">partial optimality</style></keyword><keyword><style  face="normal" font="default" size="100%">persistency</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">jul</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7</style></number><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">1370–1382</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. The method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method&#039;s runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Swoboda, P.</style></author><author><style face="normal" font="default" size="100%">Shekhovtsov, A.</style></author><author><style face="normal" font="default" size="100%">Kappes, J.H.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Partial Optimality by Pruning for MAP-Inference with General Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans. Patt. Anal. Mach. Intell.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">1370–1382</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ecaterina Bodnariuc</style></author><author><style face="normal" font="default" size="100%">Martin F. Schiffner</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Plane Wave Acoustic Superposition for Fast Ultrasound Imaging</style></title><secondary-title><style face="normal" font="default" size="100%">International Ultrasonics Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Omid Hosseini Jafari</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Ying</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Real-time RGB-D based template matching pedestrian detection</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - IEEE International Conference on Robotics and Automation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">2016-June</style></volume><pages><style face="normal" font="default" size="100%">5520–5527</style></pages><isbn><style face="normal" font="default" size="100%">9781467380263</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Pedestrian detection is one of the most popular topics in computer vision and robotics. Considering challenging issues in multiple pedestrian detection, we present a real-time depth-based template matching people detector. In this paper, we propose different approaches for training the depth-based template. We train multiple templates for handling issues due to various upper-body orientations of the pedestrians and different levels of detail in depth-map of the pedestrians with various distances from the camera. And, we take into account the degree of reliability for different regions of sliding window by proposing the weighted template approach. Furthermore, we combine the depth-detector with an appearance based detector as a verifier to take advantage of the appearance cues for dealing with the limitations of depth data. We evaluate our method on the challenging ETH dataset sequence. We show that our method outperforms the state-of-the-art approaches.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Naja von Schmude</style></author><author><style face="normal" font="default" size="100%">Pierre Lothe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Relative Pose Estimation from Straight Lines using Parallel Line Clustering and its Application to Monocular Visual Odometry</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><isbn><style face="normal" font="default" size="100%">978-989-758-175-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper tackles the problem of relative pose estimation between two monocular camera images in textureless scenes. Due to a lack of point matches, point-based approaches such as the 5-point algorithm often fail when used in these scenarios. Therefore we investigate relative pose estimation from line observations. We propose a new approach in which the relative pose estimation from lines is extended by a 3D line direction estimation step. The estimated line directions serve to improve the robustness and the efficiency of all processing phases: they enable us to guide the matching of line features and allow an efficient calculation of the relative pose. First, we describe in detail the novel 3D line direction estimation from a single image by clustering of parallel lines in the world. Secondly, we propose an innovative guided matching in which only clusters of lines with corresponding 3D line directions are considered. Thirdly, we introduce the new relative pose estimation based on 3 D line directions. Finally, we combine all steps to a visual odometry system. We evaluate the different steps on synthetic and real sequences and demonstrate that in the targeted scenarios we outperform the state-of-the-art in both accuracy and computation time.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haubold, C.</style></author><author><style face="normal" font="default" size="100%">Schiegg, M.</style></author><author><style face="normal" font="default" size="100%">Kreshuk, A.</style></author><author><style face="normal" font="default" size="100%">Stuart Berg</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Segmenting and Tracking Multiple Dividing Targets Using ilastik</style></title><secondary-title><style face="normal" font="default" size="100%">Focus on Bio-Image Informatics</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Advances in Anatomy, Embryology and Cell Biology</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">219</style></volume><pages><style face="normal" font="default" size="100%">199-229</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathore, D</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic Segmentation Using Deep Learning</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katja Schwarz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-Temporal Measurements of Water-Wave Height and Slope using Laser-Induced Fluorescence and Splines</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">mastersBachelor&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sellent, Anita</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stereo video deblurring</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Object motion blur</style></keyword><keyword><style  face="normal" font="default" size="100%">Scene flow</style></keyword><keyword><style  face="normal" font="default" size="100%">Spatially-variant deblurring</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">9906 LNCS</style></volume><pages><style face="normal" font="default" size="100%">558–575</style></pages><isbn><style face="normal" font="default" size="100%">9783319464749</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Videos acquired in low-light conditions often exhibit motion blur, which depends on the motion of the objects relative to the camera. This is not only visually unpleasing, but can hamper further processing. With this paper we are the first to show how the availability of stereo video can aid the challenging video deblurring task.We leverage 3D scene flow, which can be estimated robustly even under adverse conditions. We go beyond simply determining the object motion in two ways: First, we show how a piecewise rigid 3D scene flow representation allows to induce accurate blur kernels via local homographies. Second, we exploit the estimated motion boundaries of the 3D scene flow to mitigate ringing artifacts using an iterative weighting scheme. Being aware of 3D object motion, our approach can deal robustly with an arbitrary number of independently moving objects. We demonstrate its benefit over state-ofthe- art video deblurring using quantitative and qualitative experiments on rendered scenes and real videos.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sellent, Anita</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stereo Video Deblurring-Supplemental Material</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">One of the key contributions in our stereo video deblurring is to employ 3D scene flow to induce blur kernels based on homographies. As the difference to inducing blur kernels from an optical flow field may seem subtle but increases deblurring performance considerably, we schematically illustrate the difference of the two ways of generating blur kernels in Fig. 10. (a) 3D motion (b) Image plane projection: Blur kernels from linear displacements (c) Image plane projection: Blur kernels from homographies Fig. 10. Inducing blur matrices: (a) Assume that 3D point P moves with a constant rigid body motion in 3D, e. g. with a yaw motion. The projection of this motion to the image plane (blue) is a circular trajectory. The corresponding 2D ground truth displacement (yellow), however, is a vector in the image plane that connects start and end point of the motion during a time interval. (b) Using optical flow, the blur kernel at a location z is approximated by identifying all pixels that, according to their spatially-variant displacement, pass through z during the time interval. The image content at point x is correctly identified as passing through z. The image content at point y is not identified correctly as its 2D displacement passes z at a distance. Instead, the image content at pointˆypointˆ pointˆy is erroneously identified as passing through z, even thoughˆythoughˆ thoughˆy has a different distance to the rotation center than z. This results in the distorted kernels shown in Fig. 3b. (c) Assuming 3D points in the vicinity of P to form a plane, we employ 3D homographies to generate blur kernels. The blur kernel at location z is thus formed by the image points whose trajectory, according to the homography, passes through z during the time interval. Consequently, y is correctly identified as passing through z, leading to the kernels shaped as circular arcs shown in Fig. 3a</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kiem, A</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Structured Learning on Calcium Imaging Data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Master Theses</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Structured Regression Gradient Boosting</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><pages><style face="normal" font="default" size="100%">1459-1467</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Silvestri, F.</style></author><author><style face="normal" font="default" size="100%">Reinelt, G.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Symmetry-free SDP Relaxations for Affine Subspace Clustering</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">July</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1607.07387</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">ArXiv, preprint</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Ying</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">2016-Decem</style></volume><pages><style face="normal" font="default" size="100%">3364–3372</style></pages><isbn><style face="normal" font="default" size="100%">9781467388504</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Ying</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">2016-Decem</style></volume><pages><style face="normal" font="default" size="100%">3364–3372</style></pages><isbn><style face="normal" font="default" size="100%">9781467388504</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In recent years, the task of estimating the 6D pose of object instances and complete scenes, i.e. camera localization, from a single input image has received considerable attention. Consumer RGB-D cameras have made this feasible, even for difficult, texture-less objects and scenes. In this work, we show that a single RGB image is sufficient to achieve visually convincing results. Our key concept is to model and exploit the uncertainty of the system at all stages of the processing pipeline. The uncertainty comes in the form of continuous distributions over 3D object coordinates and discrete distributions over object labels. We give three technical contributions. Firstly, we develop a regularized, auto-context regression framework which iteratively reduces uncertainty in object coordinate and object label predictions. Secondly, we introduce an efficient way to marginalize object coordinate distributions over depth. This is necessary to deal with missing depth information. Thirdly, we utilize the distributions over object labels to detect multiple objects simultaneously with a fixed budget of RANSAC hypotheses. We tested our system for object pose estimation and camera localization on commonly used data sets. We see a major improvement over competing systems.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M</style></author><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Rajamani, K</style></author><author><style face="normal" font="default" size="100%">van der Laak, J</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational weakly-supervised Gaussian processes</style></title><secondary-title><style face="normal" font="default" size="100%">BMVC. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kleesiek, J.</style></author><author><style face="normal" font="default" size="100%">Petersen, J.</style></author><author><style face="normal" font="default" size="100%">Döring, M.</style></author><author><style face="normal" font="default" size="100%">Maier-Hein, K.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Wick, W.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">M. Bendszus</style></author><author><style face="normal" font="default" size="100%">A. Biller</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Virtual Raters for Reproducible and Objective Assessments in Radiology</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Scientific Reports</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><volume><style face="normal" font="default" size="100%">6</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manuel Haußmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weakly Supervised Detection with Gaussian Processes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jose Esparza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D Reconstruction for Optimal Representation of Surroundings in Automotive HMIs, Based on Fisheye Multi-camera Systems</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Esparza, Jose</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D Reconstruction for Optimal Representation of Surroundings in Automotive HMIs, Based on Fisheye Multi-camera Systems</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Biesdorf, A.</style></author><author><style face="normal" font="default" size="100%">Wörz, S.</style></author><author><style face="normal" font="default" size="100%">von Tengg-Kobligk, H.</style></author><author><style face="normal" font="default" size="100%">Karl Rohr</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D Segmentation of Vessels by Incremental Implicit Polynomial Fitting and Convex Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.~ISBI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ecaterina Bodnariuc</style></author><author><style face="normal" font="default" size="100%">Gurung, A.</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.~EMMCVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8932</style></volume><pages><style face="normal" font="default" size="100%">378--391</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ecaterina Bodnariuc</style></author><author><style face="normal" font="default" size="100%">Gurung, A.</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV</style></title><secondary-title><style face="normal" font="default" size="100%">EMMCVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nils Krah</style></author><author><style face="normal" font="default" size="100%">M Testa</style></author><author><style face="normal" font="default" size="100%">S Brons</style></author><author><style face="normal" font="default" size="100%">O Jäkel</style></author><author><style face="normal" font="default" size="100%">K Parodi</style></author><author><style face="normal" font="default" size="100%">Björn Voss</style></author><author><style face="normal" font="default" size="100%">I Rinaldi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An advanced image processing method to improve the spatial resolution of ion radiographies</style></title><secondary-title><style face="normal" font="default" size="100%">Physics in Medicine and Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://stacks.iop.org/0031-9155/60/i=21/a=8525</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">60</style></volume><pages><style face="normal" font="default" size="100%">8525</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present an optimization method to improve the spatial resolution and the water equivalent thickness (WET) accuracy of ion radiographies. The method is designed for imaging systems measuring for each actively scanned beam spot the lateral position of the pencil beam and at the same time the Bragg curve (behind the target) in discrete steps without relying on tracker detectors to determine the ion trajectory before and after the irradiated volume. Specifically, the method was used for an imaging set-up consisting of a stack of 61 parallel-plate ionization chambers (PPIC) interleaved with absorber plates of polymethyl methacrylate (PMMA) working as a range telescope. The method uses not only the Bragg peak position, but approximates the entire measured Bragg curve as a superposition of differently shifted Bragg curves. Their relative weights allow to reconstruct the distribution of thickness around each scan spot of a heterogeneous phantom. The approach also allows merging the ion radiography with the geometric information of a co-registered x-ray radiography in order to increase its spatial resolution. The method was tested using Monte Carlo simulated and experimental proton radiographies of a PMMA step phantom and an anthropomorphic head phantom. For the step phantom, the effective spatial resolution was found to be 6 and 4 times higher than the nominal resolution for the simulated and experimental radiographies, respectively. For the head phantom, a gamma index was calculated to quantify the conformity of the simulated proton radiographies with a digitally reconstructed radiography (DRR) obtained from an x-ray CT and properly converted into WET. For a distance-to-agreement (DTA) of 2.5 mm and a relative WET difference (RWET) of 2.5%, the passing ratio was 100%/85% for the optimized/non-optimized case, respectively. When the optimized proton radiography was merged with the co-registered DRR, the passing ratio was 100% at DTA  =  1.3 mm and RWET  =  1.3%. A special interpolation method allows to strongly reduce the dose by using a coarser grid of the measured beam spot position with a 5 times larger grid distance. We show that despite a dose reduction of 25 times (leading to a dose of 0.016 mGy for the current imaging set-up), the image quality of the optimized radiographies remains fairly unaffected for both the simulated and experimental case.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Air-sea gas exchange: from empiric wind speed relations to regimes and ranges</style></title><secondary-title><style face="normal" font="default" size="100%">Frühjahrstagung der Deutschen Physikalischen Gesellschaft, Fachverband Umweltphysik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/year/2015/conference/heidelberg/part/up/session/16/contribution/3?lang=en</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">UP 16.3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Invited talk</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rahul Nair</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis and Modeling of Passive Stereo and Time-of-Flight Imaging</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Univ. 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Kappes</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Nowozin, S.</style></author><author><style face="normal" font="default" size="100%">Dhruv Batra</style></author><author><style face="normal" font="default" size="100%">Kim, S.</style></author><author><style face="normal" font="default" size="100%">Bernhard X. Kausler</style></author><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Komodakis, N.</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pages><style face="normal" font="default" size="100%">1-30</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Nowozin, S.</style></author><author><style face="normal" font="default" size="100%">Dhruv Batra</style></author><author><style face="normal" font="default" size="100%">Kim, S.</style></author><author><style face="normal" font="default" size="100%">Bernhard X. Kausler</style></author><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Komodakis, N.</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems</style></title><secondary-title><style face="normal" font="default" size="100%">Int.~J.~Comp.~Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">in press (preprint: arXiv:1404.0533)</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, Jörg H</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Dhruv Batra</style></author><author><style face="normal" font="default" size="100%">Kim, Sungwoong</style></author><author><style face="normal" font="default" size="100%">Kausler, Bernhard X</style></author><author><style face="normal" font="default" size="100%">Kröger, Thorben</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Komodakis, Nikos</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Benchmark</style></keyword><keyword><style  face="normal" font="default" size="100%">Combinatorial optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Discrete graphical models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hci.iwr.uni-heidelberg.de/opengm2/</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">115</style></volume><pages><style face="normal" font="default" size="100%">155–184</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, Jörg H</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Dhruv Batra</style></author><author><style face="normal" font="default" size="100%">Kim, Sungwoong</style></author><author><style face="normal" font="default" size="100%">Kausler, Bernhard X</style></author><author><style face="normal" font="default" size="100%">Kröger, Thorben</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Komodakis, Nikos</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Benchmark</style></keyword><keyword><style  face="normal" font="default" size="100%">Combinatorial optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Discrete graphical models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">115</style></volume><pages><style face="normal" font="default" size="100%">155–184</style></pages><isbn><style face="normal" font="default" size="100%">25164671.25</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, Jörg H</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Dhruv Batra</style></author><author><style face="normal" font="default" size="100%">Kim, Sungwoong</style></author><author><style face="normal" font="default" size="100%">Kausler, Bernhard X</style></author><author><style face="normal" font="default" size="100%">Kröger, Thorben</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Komodakis, Nikos</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Benchmark</style></keyword><keyword><style  face="normal" font="default" size="100%">Combinatorial optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Discrete graphical models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">115</style></volume><pages><style face="normal" font="default" size="100%">155–184</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Szeliski et al. published an influential study in 2006 on energy minimization methods for Markov random fields. This study provided valuable insights in choosing the best optimization technique for certain classes of problems. While these insights remain generally useful today, the phenomenal success of random field models means that the kinds of inference problems that have to be solved changed significantly. Specifically, the models today often include higher order interactions, flexible connectivity structures, large label-spaces of different cardinalities, or learned energy tables. To reflect these changes, we provide a modernized and enlarged study. We present an empirical comparison of more than 27 state-of-the-art optimization techniques on a corpus of 2453 energy minimization instances from diverse applications in computer vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2 framework and report extensive results regarding runtime and solution quality. Key insights from our study agree with the results of Szeliski et al. for the types of models they studied. However, on new and challenging types of models our findings disagree and suggest that polyhedral methods and integer programming solvers are competitive in terms of runtime and solution quality over a large range of model types.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, F.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Computational Approach to Log-Concave Density Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">An. St. Univ. 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Math. Imag. Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/article/10.1007/s10851-014-0546-8</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">52</style></volume><pages><style face="normal" font="default" size="100%">436–458</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hassan Abu Alhaija</style></author><author><style face="normal" font="default" size="100%">Sellent, Anita</style></author><author><style face="normal" font="default" size="100%">Kondermann, Daniel</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graphflow—6D large displacement scene flow via graph matching</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">9358</style></volume><pages><style face="normal" font="default" size="100%">285–296</style></pages><isbn><style face="normal" font="default" size="100%">9783319249469</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present an approach for computing dense scene flow from two large displacement RGB-D images. When dealing with large displacements the crucial step is to estimate the overall motion correctly. While state-of-the-art approaches focus on RGB information to establish guiding correspondences, we explore the power of depth edges. To achieve this, we present a new graph matching technique that brings sparse depth edges into correspondence. An additional contribution is the formulation of a continuous-label energy which is used to densify the sparse graph matching output. We present results on challenging Kinect images, for which we outperform state-of-the-art techniques.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schiegg, M.</style></author><author><style face="normal" font="default" size="100%">Hanslovsky, P.</style></author><author><style face="normal" font="default" size="100%">Haubold, C.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Hufnagel, L.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://bioinformatics.oxfordjournals.org/content/early/2014/11/17/bioinformatics.btu764.full.pdf?keytype=ref&amp;ijkey=mTXWsiFrci7R8tc</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">948-956</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">6</style></issue><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Katrin Honauer</style></author><author><style face="normal" font="default" size="100%">Lena Maier-Hein</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">The IEEE International Conference on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">@InProceedings{Honauer_2015_ICCV,
author = {Honauer, Katrin and Maier-Hein, Lena and Kondermann, Daniel},
title = {The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2015}
} </style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Niko Krasowski</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">G. W. Knott</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Anna Kreshuk</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors</style></title><secondary-title><style face="normal" font="default" size="100%">12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pages><style face="normal" font="default" size="100%">536-539</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Schlesinger, Dmitrij</style></author><author><style face="normal" font="default" size="100%">Vetrov, Dmitry</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferring M-best diverse labelings in a single one</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">2015 Inter</style></volume><pages><style face="normal" font="default" size="100%">1814–1822</style></pages><isbn><style face="normal" font="default" size="100%">9781467383912</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the task of finding M-best diverse solutions in a graphical model. In a previous work by Batra et al. an algorithmic approach for finding such solutions was proposed, and its usefulness was shown in numerous applications. Contrary to previous work we propose a novel formulation of the problem in form of a single energy minimization problem in a specially constructed graphical model. We show that the method of Batra et al. can be considered as a greedy approximate algorithm for our model, whereas we introduce an efficient specialized optimization technique for it, based on alpha-expansion. We evaluate our method on two application scenarios, interactive and semantic image segmentation, with binary and multiple labels. In both cases we achieve considerably better error rates than state-of-the art diversity methods. Furthermore, we empirically discover that in the binary label case we were able to reach global optimality for all test instances.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Christopher J Zappa</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Influence of natural surfactants on short wind waves in the coastal Peruvian waters</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">1291–1325</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schelten, Kevin</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Jancsary, Jeremy</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interleaved regression tree field cascades for blind image deconvolution</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pages><style face="normal" font="default" size="100%">494–501</style></pages><isbn><style face="normal" font="default" size="100%">9781479966820</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image blur from camera shake is a common cause for poor image quality in digital photography, prompting a significant recent interest in image deblurring. The vast majority of work on blind deblurring splits the problem into two subsequent steps: First, the blur process (i.e., blur kernel) is estimated, then the image is restored given the estimated kernel using a non-blind deblurring algorithm. Recent work in non-blind deblurring has shown that discriminative approaches can have clear image quality and runtime benefits over typical generative formulations. In this paper, we propose a cascade for blind deblurring that alternates between kernel estimation and discriminative deblurring using regression tree fields (RTFs). We further contribute a new dataset of realistic image blur kernels from human camera shake, which we use to train the discriminative component. Extensive qualitative and quantitative experiments show a clear gain in image quality by interleaving kernel estimation and discriminative deblurring in an iterative cascade.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Ying</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning analysis-by-synthesis for 6d pose estimation in RGB-D images</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">2015 Inter</style></volume><pages><style face="normal" font="default" size="100%">954–962</style></pages><isbn><style face="normal" font="default" size="100%">9781467383912</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Analysis-by-synthesis has been a successful approach for many tasks in computer vision, such as 6D pose estimation of an object in an RGB-D image which is the topic of this work. The idea is to compare the observation with the output of a forward process, such as a rendered image of the object of interest in a particular pose. Due to occlusion or complicated sensor noise, it can be difficult to perform this comparison in a meaningful way. We propose an approach that &quot;learns to compare&quot;, while taking these difficulties into account. This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares observed and rendered images. The network is trained with the maximum likelihood paradigm. We observe empirically that the CNN does not specialize to the geometry or appearance of specific objects. It can be used with objects of vastly different shapes and appearances, and in different backgrounds. Compared to state-of-the-art, we demonstrate a significant improvement on two different datasets which include a total of eleven objects, cluttered background, and heavy occlusion.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Funke, J.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Zhang, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Frangi, A. et al.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning to Segment: Training Hierarchical Segmentation under a Topological Loss</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI. Proceedings, Part III</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">9351</style></volume><pages><style face="normal" font="default" size="100%">268-275</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Krolla</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Didier Stricker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Light Field from Smartphone-Based Dual Video</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-16181-5_46</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">600–610</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-16181-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Oliver Blum</style></author><author><style face="normal" font="default" size="100%">Marcel Gutsche</style></author><author><style face="normal" font="default" size="100%">Sven Wanner</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Harlyn Baker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Light-field camera design for high-accuracy depth estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Videometrics, Range Imaging, and Applications XIII</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2015</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Light-field imaging is a research field with applicability in a variety of imaging areas including 3D cinema, entertainment, robotics, and any task requiring range estimation. In contrast to binocular or multi-view stereo approaches, capturing light fields means densely observing a target scene through a window of viewing directions. A principal benefit in light-field imaging for range computation is that one can eliminate the error-prone and computationally expensive process of establishing correspondence. The nearly continuous space of observation allows to compute highly accurate and dense depth maps free of matching. Here, we discuss how to structure the imaging system for optimal ranging over a defined volume - what we term a bounded frustum. We detail the process of designing the light-field setup, including practical issues such as camera footprint and component size influence the depth of field, lateral and range resolution. Both synthetic and real captured scenes are used to analyze the depth precision resulting from a design, and to show how unavoidable inaccuracies such as camera position and focal length variation limit depth precision. Finally, inaccuracies may be sufficiently well compensated through calibration and must be eliminated at the outset.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Diebold</style></author><author><style face="normal" font="default" size="100%">O. Blum</style></author><author><style face="normal" font="default" size="100%">M. Gutsche</style></author><author><style face="normal" font="default" size="100%">S. Wanner</style></author><author><style face="normal" font="default" size="100%">C. Garbe</style></author><author><style face="normal" font="default" size="100%">H. Baker</style></author><author><style face="normal" font="default" size="100%">B. Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fabio Remondino</style></author><author><style face="normal" font="default" size="100%">Mark R. Shortis</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Light-field camera design for high-accuracy depth estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Videometrics, Range Imaging, and Applications XIII</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">SPIE</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirillov, Alexander</style></author><author><style face="normal" font="default" size="100%">Schlesinger, Dmitrij</style></author><author><style face="normal" font="default" size="100%">Vetrov, Dmitry</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">M-best-diverse labelings for submodular energies and beyond</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">2015-Janua</style></volume><pages><style face="normal" font="default" size="100%">613–621</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the problem of findingM best diverse solutions of energy minimization problems for graphical models. Contrary to the sequential method of Batra et al., which greedily finds one solution after another, we infer all M solutions jointly. It was shown recently that such jointly inferred labelings not only have smaller total energy but also qualitatively outperform the sequentially obtained ones. The only obstacle for using this new technique is the complexity of the corresponding inference problem, since it is considerably slower algorithm than the method of Batra et al. In this work we show that the joint inference of M best diverse solutions can be formulated as a submodular energy minimization if the original MAP-inference problem is submodular, hence fast inference techniques can be used. In addition to the theoretical results we provide practical algorithms that outperform the current state-of-the-art and can be used in both submodular and non-submodular case.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Evridiki Mesarchaki</style></author><author><style face="normal" font="default" size="100%">Christine Kräuter</style></author><author><style face="normal" font="default" size="100%">Kerstin Ellen Krall</style></author><author><style face="normal" font="default" size="100%">Maximilian Bopp</style></author><author><style face="normal" font="default" size="100%">F. Helleis</style></author><author><style face="normal" font="default" size="100%">Jonathan Williams</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring air–sea gas-exchange velocities in a large-scale annular wind–wave tank</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Sci.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">121--138</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schiegg, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-Target Tracking with Probabilistic Graphical Models</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Julian Stapf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel learning-based techniques for dense fluid motion measurements</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/18116</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zheng, Shuai</style></author><author><style face="normal" font="default" size="100%">Prisacariu, Victor Adrian</style></author><author><style face="normal" font="default" size="100%">Averkiou, Melinos</style></author><author><style face="normal" font="default" size="100%">Cheng, Ming Ming</style></author><author><style face="normal" font="default" size="100%">Mitra, Niloy J</style></author><author><style face="normal" font="default" size="100%">Shotton, Jamie</style></author><author><style face="normal" font="default" size="100%">Torr, Philip H.S.</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Object proposals estimation in depth image using compact 3D shape manifolds</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">9358</style></volume><pages><style face="normal" font="default" size="100%">196–208</style></pages><isbn><style face="normal" font="default" size="100%">9783319249469</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Man-made objects, such as chairs, often have very large shape variations, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any collection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based filtering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previous state-of-the-art approaches, on four different shape classes, and show a clear improvement.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The ocean in the lab: measurements with light and shadow</style></title><secondary-title><style face="normal" font="default" size="100%">Ruperto Carola Forschungsmagazin Heidelberg University</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">52–59</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Felix Friedl</style></author><author><style face="normal" font="default" size="100%">Nils Krah</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical sensing of oxygen using a modified Stern-Volmer equation for high laser irradiance</style></title><secondary-title><style face="normal" font="default" size="100%">Sensors and Actuators B: Chemical</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">206</style></volume><pages><style face="normal" font="default" size="100%">336–342</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Felix Friedl</style></author><author><style face="normal" font="default" size="100%">Nils Krah</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical sensing of oxygen using a modified Stern-Volmer equation for high laser irradiance</style></title><secondary-title><style face="normal" font="default" size="100%">Sensors and Actuators B: Chemical</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">206</style></volume><pages><style face="normal" font="default" size="100%">336--342</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Available online 30 September 2014</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mitra, N J</style></author><author><style face="normal" font="default" size="100%">Stam, J</style></author><author><style face="normal" font="default" size="100%">Xu, K</style></author><author><style face="normal" font="default" size="100%">Cheng, Ming-Ming</style></author><author><style face="normal" font="default" size="100%">Prisacariu, Victor Adrian</style></author><author><style face="normal" font="default" size="100%">Zheng, Shuai</style></author><author><style face="normal" font="default" size="100%">Torr, Philip H S</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">I46 [IMAGE PROCESSING AND COMPUTER VISION]</style></keyword><keyword><style  face="normal" font="default" size="100%">partitioning</style></keyword><keyword><style  face="normal" font="default" size="100%">Segmentation—Region growing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://mftp.mmcheng.net/Papers/DenseCut.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Figure-ground segmentation from bounding box input, provided either automatically or manually, has been ex-tremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practi-cal usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense CRF and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10×) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mund, Johannes</style></author><author><style face="normal" font="default" size="100%">Zouhar, Alexander</style></author><author><style face="normal" font="default" size="100%">Meyer, Lothar</style></author><author><style face="normal" font="default" size="100%">Fricke, Hartmut</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Airport surveillance</style></keyword><keyword><style  face="normal" font="default" size="100%">apron control</style></keyword><keyword><style  face="normal" font="default" size="100%">Apron management service</style></keyword><keyword><style  face="normal" font="default" size="100%">FOD</style></keyword><keyword><style  face="normal" font="default" size="100%">Foreign object debris</style></keyword><keyword><style  face="normal" font="default" size="100%">laser scanning</style></keyword><keyword><style  face="normal" font="default" size="100%">LiDAR</style></keyword><keyword><style  face="normal" font="default" size="100%">Object detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Point cloud</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pages><style face="normal" font="default" size="100%">85–94</style></pages><isbn><style face="normal" font="default" size="100%">9781450335621</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mund, Johannes</style></author><author><style face="normal" font="default" size="100%">Zouhar, Alexander</style></author><author><style face="normal" font="default" size="100%">Meyer, Lothar</style></author><author><style face="normal" font="default" size="100%">Fricke, Hartmut</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Airport surveillance</style></keyword><keyword><style  face="normal" font="default" size="100%">apron control</style></keyword><keyword><style  face="normal" font="default" size="100%">Apron management service</style></keyword><keyword><style  face="normal" font="default" size="100%">FOD</style></keyword><keyword><style  face="normal" font="default" size="100%">Foreign object debris</style></keyword><keyword><style  face="normal" font="default" size="100%">laser scanning</style></keyword><keyword><style  face="normal" font="default" size="100%">LiDAR</style></keyword><keyword><style  face="normal" font="default" size="100%">Object detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Point cloud</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pages><style face="normal" font="default" size="100%">85–94</style></pages><isbn><style face="normal" font="default" size="100%">9781450335621</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Antic, B.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Per-Sample Kernel Adaptation for Visual Recognition and Grouping</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michel, Frank</style></author><author><style face="normal" font="default" size="100%">Krull, Alexander</style></author><author><style face="normal" font="default" size="100%">Brachmann, Eric</style></author><author><style face="normal" font="default" size="100%">Yang, Michael Ying</style></author><author><style face="normal" font="default" size="100%">Gumhold, Stefan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pages><style face="normal" font="default" size="100%">181.1–181.11</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we address the problem of one shot pose estimation of articulated ob-jects from an RGB-D image. In particular, we consider object instances with the topol-ogy of a kinematic chain, i.e. assemblies of rigid parts connected by prismatic or revolute joints. This object type occurs often in daily live, for instance in the form of furniture or electronic devices. Instead of treating each object part separately we are using the rela-tionship between parts of the kinematic chain and propose a new minimal pose sampling approach. This enables us to create a pose hypothesis for a kinematic chain consist-ing of K parts by sampling K 3D-3D point correspondences. To asses the quality of our method, we gathered a large dataset containing four objects and 7000+ annotated RGB-D frames 1 . On this dataset we achieve considerably better results than a modified state-of-the-art pose estimation system for rigid objects.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Swoboda, P.</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Hazan, T.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.~SSVM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, Jorg Hendrik</style></author><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Hazan, Tamir</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic correlation clustering and image partitioning using perturbed Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Correlation clustering</style></keyword><keyword><style  face="normal" font="default" size="100%">Multicut</style></keyword><keyword><style  face="normal" font="default" size="100%">Perturb and MAP</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">9087</style></volume><pages><style face="normal" font="default" size="100%">231–242</style></pages><isbn><style face="normal" font="default" size="100%">9783319184609</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We exploit recent progress on globally optimal MAP inference by integer programming and perturbation-based approximations of the log-partition function. This enables to locally represent uncertainty of image partitions by approximate marginal distributions in a mathematically substantiated way, and to rectify local data term cues so as to close contours and to obtain valid partitions. Our approach works for any graphically represented problem instance of correlation clustering, which is demonstrated by an additional social network example.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, J.</style></author><author><style face="normal" font="default" size="100%">Swoboda, P.</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Hazan , T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. SSVM</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, Fabian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Graphical Models for Medical Image Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">University Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phd</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schiegg, M.</style></author><author><style face="normal" font="default" size="100%">Heuer, B.</style></author><author><style face="normal" font="default" size="100%">Haubold, C.</style></author><author><style face="normal" font="default" size="100%">Wolf, S.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models</style></title><secondary-title><style face="normal" font="default" size="100%">ISBI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><pages><style face="normal" font="default" size="100%">394-398</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nair, Rahul</style></author><author><style face="normal" font="default" size="100%">Fitzgibbon, Andrew</style></author><author><style face="normal" font="default" size="100%">Kondermann, Daniel</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reflection modeling for passive stereo</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">2015 Inter</style></volume><pages><style face="normal" font="default" size="100%">2291–2299</style></pages><isbn><style face="normal" font="default" size="100%">9781467383912</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Stereo reconstruction in presence of reality faces many challenges that still need to be addressed. This paper considers reflections, which introduce incorrect matches due to the observation violating the diffuse-world assumption underlying the majority of stereo techniques. Unlike most existing work, which employ regularization or robust data terms to suppress such errors, we derive two least squares models from first principles that generalize diffuse world stereo and explicitly take reflections into account. These models are parametrized by depth, orientation and material properties, resulting in a total of up to 5 parameters per pixel that have to be estimated. Additionally large non-local interactions between viewed and reflected surface have to be taken into account. These two properties make inference of the model appear prohibitive, but we present evidence that inference is actually possible using a variant of patch match stereo.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rubio, J. C.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regularizing Max-Margin Exemplars by Reconstruction and Generative Models</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">4213--4221</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johannes Berger</style></author><author><style face="normal" font="default" size="100%">Andreas Neufeld</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space and Variational Methods in Computer Vision (SSVM 2015)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-18461-6_32</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Accurate camera motion estimation is a fundamental build- ing block for many Computer Vision algorithms. For improved robustness, temporal consistency of translational and rotational camera velocity is often assumed by propagating motion information forward using stochastic filters. Classical stochastic filters, however, use linear approximations for the non-linear observer model and for the non-linear structure of the underlying Lie Group SE(3) and have to approximate the unknown posteriori distribution. In this paper we employ a non-linear measurement model for the camera motion estimation problem that incorporates multiple observation equations. We solve the underlying filtering problem using a novel Minimum Energy Filter on SE(3) and give explicit expressions for the optimal state variables. Experiments on the challenging KITTI benchmark show that, although a simple motion model is only employed, our approach improves rotational velocity esti- mation and otherwise is on par with the state-of-the-art.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johannes Berger</style></author><author><style face="normal" font="default" size="100%">Neufeld, Andreas</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Lenzen, Frank</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Second Order Minimum Energy Filtering on SE(3) with Nonlinear Measurement Equations</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space and Variational Methods in Computer Vision (SSVM 2015)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johannes Berger</style></author><author><style face="normal" font="default" size="100%">Lenzen, Frank</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Neufeld, Andreas</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1507.06810</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">ArXiv, preprint</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johannes Berger</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Andreas Neufeld</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations</style></title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Constant Acceleration Model</style></keyword><keyword><style  face="normal" font="default" size="100%">Lie Group</style></keyword><keyword><style  face="normal" font="default" size="100%">Minimum Energy Filter</style></keyword><keyword><style  face="normal" font="default" size="100%">Optimal Control</style></keyword><keyword><style  face="normal" font="default" size="100%">Recursive Filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">Visual Odometry</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1507.06810</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Camera motion estimation from observed scene features is an important task in image processing to increase the accuracy of many methods, e.g. optical flow and structure-from-motion. Due to the curved geometry of the state space SE(3) and the non-linear relation to the observed optical flow, many recent filtering approaches use a first-order approximation and assume a Gaussian a posteriori distribution or restrict the state to Euclidean geometry. The physical model is usually also limited to uniform motions. 
We propose a second-order minimum energy filter with a generalized kinematic model that copes with the full geometry of SE(3) as well as with the nonlinear dependencies between the state space and observations. The derived filter enables reconstructing motions correctly for synthetic and real scenes, e.g. from the KITTI benchmark. Our experiments confirm that the derived minimum energy filter with higher-order state differential equation copes with higher-order kinematics and is also able to minimize model noise. We also show that the proposed filter is superior to state-of-the-art extended Kalman filters on Lie groups in the case of linear observations and that our method reaches the accuracy of modern visual odometry methods.</style></abstract><custom2><style face="normal" font="default" size="100%">arXiv preprint</style></custom2></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zouhar, Alexander</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Fuchs, Siegfried</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semantic 3-D labeling of ear implants using a global parametric transition prior</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">9350</style></volume><pages><style face="normal" font="default" size="100%">177–184</style></pages><isbn><style face="normal" font="default" size="100%">9783319245706</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work we consider the problem of sematic part-labeling of 3-D meshesof ear implants. This is a challenging problem and automatic solutions are of high practical relevance, since they help to automate the design of hearing aids. The contribution of this work is a new framework which outperforms existing approaches for this task. To achieve the boost in performance we introduce the new concept of a global parametric transition prior. To our knowledge, this is the first time that such a generic prior is used for 3-D mesh processing, and it may be found useful for a large class of 3-D meshes. To foster more research on the important topic of ear implant labeling, we collected a large data set of 3-D meshes, with associated ground truth labels, which we will make publicly available.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Didden, E.-M.</style></author><author><style face="normal" font="default" size="100%">Thorarinsdottir, T.L.</style></author><author><style face="normal" font="default" size="100%">Lenkoski, A.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Shape from Texture using Locally Scaled Point Processes</style></title><secondary-title><style face="normal" font="default" size="100%">Image Anal. Stereol.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">34</style></volume><pages><style face="normal" font="default" size="100%">161-170</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lenzen, Frank</style></author><author><style face="normal" font="default" size="100%">Johannes Berger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Solution-Driven Adaptive Total Variation Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">LNCS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">in press</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Johannes Berger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Solution-Driven Adaptive Total Variation Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">LNCS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-18461-6_17</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider solution-driven adaptive variants of Total Variation, in which the adaptivity is introduced as a fixed point problem. We provide existence theory for such fixed points in the continuous domain. For the applications of image denoising, deblurring and inpainting, we provide experiments which demonstrate that our approach in most cases outperforms state-of-the-art regularization approaches.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-Temporal Motif Deconvolution for Calcium Image Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Master Thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Antic, B.</style></author><author><style face="normal" font="default" size="100%">Uta Büchler</style></author><author><style face="normal" font="default" size="100%">Anna-Sophia Wahl</style></author><author><style face="normal" font="default" size="100%">M. E. Schwab</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatiotemporal Parsing of Motor Kinematics for Assessing Stroke Recovery</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Image Computing and Computer-Assisted Intervention</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Antic, B.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-temporal Video Parsing for Abnormality Detection</style></title><secondary-title><style face="normal" font="default" size="100%">arXiv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1502.06235</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">abs/1502.06235</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Nair, Rahul</style></author><author><style face="normal" font="default" size="100%">Stephan Meister</style></author><author><style face="normal" font="default" size="100%">Wolfgang Mischler</style></author><author><style face="normal" font="default" size="100%">Güssefeld, Burkhard</style></author><author><style face="normal" font="default" size="100%">Katrin Honauer</style></author><author><style face="normal" font="default" size="100%">Sabine Hofmann</style></author><author><style face="normal" font="default" size="100%">Brenner, Claus</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stereo Ground Truth with Error Bars</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision – ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-16814-2_39</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">595–610</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-16814-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Creating stereo ground truth based on real images is a measurement task. Measurements are never perfectly accurate: the depth at each pixel follows an error distribution. A common way to estimate the quality of measurements are error bars. In this paper we describe a methodology to add error bars to images of previously scanned static scenes. The main challenge for stereo ground truth error estimates based on such data is the nonlinear matching of 2D images to 3D points. Our method uses 2D feature quality, 3D point and calibration accuracy as well as covariance matrices of bundle adjustments. We sample the reference data error which is the 3D depth distribution of each point projected into 3D image space. The disparity distribution at each pixel location is then estimated by projecting samples of the reference data error on the 2D image plane. An analytical Gaussian error propagation is used to validate the results. As proof of concept, we created ground truth of an image sequence with 100 frames. Results show that disparity accuracies well below one pixel can be achieved, albeit with much large errors at depth discontinuities mainly caused by uncertain estimates of the camera location.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cali, C.</style></author><author><style face="normal" font="default" size="100%">Baghabra, J.</style></author><author><style face="normal" font="default" size="100%">Boges, D. J.</style></author><author><style face="normal" font="default" size="100%">Holst, G. R.</style></author><author><style face="normal" font="default" size="100%">Anna Kreshuk</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Srinivasan, M.</style></author><author><style face="normal" font="default" size="100%">Lehväslaiho, H.</style></author><author><style face="normal" font="default" size="100%">Magistretti, P. J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Comparative Neurology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">524</style></volume><pages><style face="normal" font="default" size="100%">23-38</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">M. Zisler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TomoGC: Binary Tomography by Constrained Graph Cuts</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.~GCPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, J.H.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">M. Zisler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TomoGC: Binary Tomography by Constrained Graph Cuts</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. GCPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kauppi, J .P.</style></author><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Saarinen, V .M.</style></author><author><style face="normal" font="default" size="100%">Hirvenkari, L.</style></author><author><style face="normal" font="default" size="100%">Parkkonen, L.</style></author><author><style face="normal" font="default" size="100%">Klami, A.</style></author><author><style face="normal" font="default" size="100%">Hari, R.</style></author><author><style face="normal" font="default" size="100%">Kaski, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals</style></title><secondary-title><style face="normal" font="default" size="100%">NeuroImage</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">112</style></volume><pages><style face="normal" font="default" size="100%">288-298</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Darya Trofimova</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Four Dimensional Visualization of Air-Water Gas Exchange</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bell, P.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Training Argus</style></title><secondary-title><style face="normal" font="default" size="100%">Kunstchronik. Monatsschrift für Kunstwissenschaft, Museumswesen und Denkmalpflege</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><number><style face="normal" font="default" size="100%">8</style></number><publisher><style face="normal" font="default" size="100%">Zentralinstitut für Kunstgeschichte</style></publisher><volume><style face="normal" font="default" size="100%">68</style></volume><pages><style face="normal" font="default" size="100%">414--420</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Richmond, David</style></author><author><style face="normal" font="default" size="100%">Kainmueller, Dagmar</style></author><author><style face="normal" font="default" size="100%">Glocker, Ben</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Myers, Gene</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Uncertainty-driven forest predictors for vertebra localization and segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">9349</style></volume><pages><style face="normal" font="default" size="100%">653–660</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Accurate localization, identification and segmentation of vertebrae is an important task in medical and biological image analysis. The prevailing approach to solve such a task is to first generate pixelindependent features for each vertebra, e.g. via a random forest predictor, which are then fed into an MRF-based objective to infer the optimal MAP solution of a constellation model. We abandon this static, twostage approach and mix feature generation with model-based inference in a new, more flexible, way. We evaluate our method on two data sets with different objectives. The first is semantic segmentation of a 21-part body plan of zebrafish embryos in microscopy images, and the second is localization and identification of vertebrae in benchmark human CT.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Arnold, Niklas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualisierung des Gasaustauschs an der windbewegten Wasseroberfläche mittels vertikaler Konzentrationsfelder von gelöstem Sauerstoff quer zur Windrichtung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A measuring technique for oxygen concentration fields is presented in this study. The technique is based on the method of laser induced-fluorescence (LIF). A fluorescent a water soluble ruthenium complex, which is dynamically quenched by oxygen, is used. In order to measure two dimensional fields, a lasersheet is created dynamically through a rotating polygon mirror wheel. With a spatial resolution of 43 µm and a recording frequency of 150 Hz it is possible to record fast processes within the mass boundary layerat the air-water interface. First measurements at a linear wind-wave facility show interesting events, which are presented in the form of videos. Calculation of mean concentration profiles out of the measured data provide results for the thinkness of the mass boundary layer and the transfer velocity in the estimated order of magnitude.</style></abstract><work-type><style face="normal" font="default" size="100%">mastersBachelor&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christine Kräuter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualization of air-water gas exchange</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Guillemette Caulliez</style></author><author><style face="normal" font="default" size="100%">Christopher J Zappa</style></author><author><style face="normal" font="default" size="100%">Julia Schaper</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Water wave measurement from stereo images of specular reflections</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">115401</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new optical instrument for the study of ocean waves, the Reflective Stereo Slope Gauge, has been developed. Its purpose is to measure ocean wave field parameters that are crucial to the air-sea exchange of momentum, heat and gases. The instrument combines a statistical wave slope measurement method similar to Cox and Munk&#039;s sun glitter technique with a dedicated stereo camera and associated illumination setup for direct wave height measurements. The instrument output was validated under controlled conditions in a wind-wave facility.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna Kreshuk</style></author><author><style face="normal" font="default" size="100%">Funke, J.</style></author><author><style face="normal" font="default" size="100%">A. Cardona</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Who is talking to whom: synaptic partner detection in anisotropic volumes of insect brain</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 9349</style></volume><pages><style face="normal" font="default" size="100%">661-668</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kainmueller, Dagmar</style></author><author><style face="normal" font="default" size="100%">Jug, Florian</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Myers, Gene</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Active graph matching for automatic joint segmentation and annotation of C. elegans</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">PART 1</style></number><volume><style face="normal" font="default" size="100%">8673 LNCS</style></volume><pages><style face="normal" font="default" size="100%">81–88</style></pages><isbn><style face="normal" font="default" size="100%">9783319104034</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work we present a novel technique we term active graph matching, which integrates the popular active shape model into a sparse graph matching problem. This way we are able to combine the benefits of a global, statistical deformation model with the benefits of a local deformation model in form of a second-order random field. We present a new iterative energy minimization technique which achieves empirically good results. This enables us to exceed state-of-the art results for the task of annotating nuclei in 3D microscopic images of C. elegans. Furthermore with the help of the generalized Hough transform we are able to jointly segment and annotate a large set of nuclei in a fully automatic fashion for the first time. © 2014 Springer International Publishing.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lou, X.</style></author><author><style face="normal" font="default" size="100%">Schiegg, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. 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Kausler</style></author><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Komodakis, Nikos</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems</style></title><secondary-title><style face="normal" font="default" size="100%">CoRR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hci.iwr.uni-heidelberg.de/opengm2/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">abs/1404.0533</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer-aided diagnosis from weak supervision: A benchmarking study</style></title><secondary-title><style face="normal" font="default" size="100%">Computerized Medical Imaging and Graphics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">44-50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephan Meister</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On Creating Reference Data for Performance Analysis in Image Processing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/16193</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephan Nicolas Robert Meister</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On Creating Reference Data for Performance Analysis in Image Processing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lena Maier-Hein</style></author><author><style face="normal" font="default" size="100%">Sven Mersmann</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">C. Stock</style></author><author><style face="normal" font="default" size="100%">Kenngott, H.</style></author><author><style face="normal" font="default" size="100%">Sanchez, A.</style></author><author><style face="normal" font="default" size="100%">Wagner, M.</style></author><author><style face="normal" font="default" size="100%">Preukschas, A.</style></author><author><style face="normal" font="default" size="100%">Wekerle, A. -L.</style></author><author><style face="normal" font="default" size="100%">Helfert, S.</style></author><author><style face="normal" font="default" size="100%">Bodenstedt, S.</style></author><author><style face="normal" font="default" size="100%">Speidel, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Crowdsourcing for reference correspondence generation in endoscopic images</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning</style></title><secondary-title><style face="normal" font="default" size="100%">2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1109/CVPR.2014.17</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zheng, Shuai</style></author><author><style face="normal" font="default" size="100%">Cheng, Ming Ming</style></author><author><style face="normal" font="default" size="100%">Warrell, Jonathan</style></author><author><style face="normal" font="default" size="100%">Sturgess, Paul</style></author><author><style face="normal" font="default" size="100%">Vineet, Vibhav</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Torr, Philip H.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dense semantic image segmentation with objects and attributes</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Attributes</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Object recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Scene Understanding</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.robots.ox.ac.uk/˜tvg/http://tu-dresden.de/inf/cvld</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">3214–3221</style></pages><isbn><style face="normal" font="default" size="100%">9781479951178</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. &#039;I see a shiny red chair&#039;). In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU indoor scenes datasets show that the proposed approach is able to achieve state-of-the-art results.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Decker, C.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detecting individual body parts improves mouse behavior classification</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eyjolfsdottir, Eyrun</style></author><author><style face="normal" font="default" size="100%">Branson, Steve</style></author><author><style face="normal" font="default" size="100%">Burgos-Artizzu, Xavier P.</style></author><author><style face="normal" font="default" size="100%">Hoopfer, Eric D.</style></author><author><style face="normal" font="default" size="100%">Schor, Jonathan</style></author><author><style face="normal" font="default" size="100%">Anderson, David J.</style></author><author><style face="normal" font="default" size="100%">Perona, Pietro</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fleet, David</style></author><author><style face="normal" font="default" size="100%">Pajdla, Tomas</style></author><author><style face="normal" font="default" size="100%">Schiele, Bernt</style></author><author><style face="normal" font="default" size="100%">Tuytelaars, Tinne</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Detection of social actions in fruit flies</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/10.1007/978-3-319-10605-2 http://www.ncbi.nlm.nih.gov/pubmed/31629782</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">September 2014</style></number><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><volume><style face="normal" font="default" size="100%">8690</style></volume><pages><style face="normal" font="default" size="100%">772–787</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-10604-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Spatio-temporal detection of actions and events in video is a challenging problem. Besides the difficulties related to recognition, a major challenge for detection in video is the size of the search space defined by spatio-temporal tubes formed by sequences of bounding boxes along the frames. Recently methods that generate unsupervised detection proposals have proven to be very effective for object detection in still images. These methods open the possibility to use strong but computationally expensive features since only a relatively small number of detection hypotheses need to be assessed. In this paper we make two contributions towards exploiting detection proposals for spatio-temporal detection problems. First, we extend a recent 2D object proposal method, to produce spatio-temporal proposals by a randomized supervoxel merging process. We introduce spatial, temporal, and spatio-temporal pairwise supervoxel features that are used to guide the merging process. Second, we propose a new efficient supervoxel method. We experimentally evaluate our detection proposals, in combination with our new supervoxel method as well as existing ones. This evaluation shows that our supervoxels lead to more accurate proposals when compared to using existing state-of-the-art supervoxel methods.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Feuchtinger, A.</style></author><author><style face="normal" font="default" size="100%">Walch, A.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digital Pathology: Multiple instance learning can detect Barrett&#039;scancer</style></title><secondary-title><style face="normal" font="default" size="100%">ISBI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pages><style face="normal" font="default" size="100%">1348-1351</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Zhang, C.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Empowering multiple instance histopathology cancer diagnosis by cell graphs</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8674</style></volume><pages><style face="normal" font="default" size="100%">228-235</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreea Denitiu</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Schnörr, Claudius</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Elena Barcucci</style></author><author><style face="normal" font="default" size="100%">Frosini, Andrea</style></author><author><style face="normal" font="default" size="100%">Eds Rinaldi, Simone</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Discrete Geometry for Computer Imagery (DGCI) 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">262--274</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreea Denitiu</style></author><author><style face="normal" font="default" size="100%">Petra, Stefania</style></author><author><style face="normal" font="default" size="100%">Schnörr, Claudius</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Elena Barcucci</style></author><author><style face="normal" font="default" size="100%">Frosini,Andrea</style></author><author><style face="normal" font="default" size="100%">Eds Rinaldi,Simone</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Discrete Geometry for Computer Imagery (DGCI) 2014</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">262–274</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rennebaum, Andreas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung einer reflexionsbasierten Technik zur Messung statistischer Parameter von Windwellen auf der Wasseroberfläche im Labor und Feld</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M</style></author><author><style face="normal" font="default" size="100%">Rubio, J. C.</style></author><author><style face="normal" font="default" size="100%">Schmidt, U.</style></author><author><style face="normal" font="default" size="100%">Wojek, C.</style></author><author><style face="normal" font="default" size="100%">Welbl, J.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Fred A. 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C.</style></author><author><style face="normal" font="default" size="100%">Schmidt, U.</style></author><author><style face="normal" font="default" size="100%">Welbl, J.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI. 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W.</style></author><author><style face="normal" font="default" size="100%">Stiller, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extrinsic calibration of a fisheye multi-camera setup using overlapping fields of view</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligent Vehicles Symposium Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pages><style face="normal" font="default" size="100%">1276--1281</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerstin Ellen Krall</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">First laboratory study of air-sea gas exchange at hurricane wind speeds</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Sci.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">257--265</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernhard Schmitzer</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">preprint</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernhard Schmitzer</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">preprint</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graph based image analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lindner, R.</style></author><author><style face="normal" font="default" size="100%">Lou, X.</style></author><author><style face="normal" font="default" size="100%">Reinstein, J.</style></author><author><style face="normal" font="default" size="100%">Shoeman, R. L.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Winkler, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hexicon 2: Automated Processing of Hydrogen-Deuterium Exchange Mass Spectrometry Data with Improved Deuteration Distribution Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of The American Society for Mass Spectrometry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">1018-1028</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Julian Yarkony</style></author><author><style face="normal" font="default" size="100%">Zhang, C.</style></author><author><style face="normal" font="default" size="100%">Fowlkes, C. C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hierarchical Planar Correlation Clustering for Cell Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">EMMCVPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8932</style></volume><pages><style face="normal" font="default" size="100%">492-504</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christine Kräuter</style></author><author><style face="normal" font="default" size="100%">Darya Trofimova</style></author><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Nils Krah</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High resolution 2-D fluorescence imaging of the mass boundary layer thickness at free water surfaces</style></title><secondary-title><style face="normal" font="default" size="100%">J. Europ. Opt. Soc. Rap. Public.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">14016</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hornáček, Michael</style></author><author><style face="normal" font="default" size="100%">Besse, Frederic</style></author><author><style face="normal" font="default" size="100%">Kautz, Jan</style></author><author><style face="normal" font="default" size="100%">Fitzgibbon, Andrew</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Highly overparameterized optical flow using PatchMatch belief propagation</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">9 DoF</style></keyword><keyword><style  face="normal" font="default" size="100%">large displacement</style></keyword><keyword><style  face="normal" font="default" size="100%">optical flow</style></keyword><keyword><style  face="normal" font="default" size="100%">PatchMatch</style></keyword><keyword><style  face="normal" font="default" size="100%">PMBP</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">PART 3</style></number><volume><style face="normal" font="default" size="100%">8691 LNCS</style></volume><pages><style face="normal" font="default" size="100%">220–234</style></pages><isbn><style face="normal" font="default" size="100%">9783319105772</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Motion in the image plane is ultimately a function of 3D motion in space. We propose to compute optical flow using what is ostensibly an extreme overparameterization: depth, surface normal, and frame-to-frame 3D rigid body motion at every pixel, giving a total of 9 DoF. The advantages of such an overparameterization are twofold: first, geometrically meaningful reasoning can be called upon in the optimization, reflecting possible 3D motion in the underlying scene; second, the &#039;fronto-parallel&#039; assumption implicit in the use of traditional matching pixel windows is ameliorated because the parameterization determines a plane-induced homography at every pixel. We show that optimization over this high-dimensional, continuous state space can be carried out using an adaptation of the recently introduced PatchMatch Belief Propagation (PMBP) energy minimization algorithm, and that the resulting flow fields compare favorably to the state of the art on a number of small- and large-displacement datasets. © 2014 Springer International Publishing.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christine Kräuter</style></author><author><style face="normal" font="default" size="100%">Darya Trofimova</style></author><author><style face="normal" font="default" size="100%">Leila Nagel</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High-resolution 2-D fluorescence imaging of gas transfer at a free water surface</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Science Meeting, 23--28. 02. 2014, Honolulu Hawaii</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A novel 2-D fluorescence imaging technique has been developed to visualize gas exchange between air and water using ammonia as a tracer. Fluorescence is stimulated by high-power LEDs and is observed from above with a low-noise, high-resolution and high-speed camera. The invasion of ammonia into water leads to an increase in pH (from a starting value of 4), which is visualized with a fluorescent dye. The flux of ammonia can be controlled by controlling its air-side concentration. A higher flux leads to an increase of the thickness of the layer, from which fluorescent light is emitted (pH &gt; 7). In this way, a varying fraction of the thickness of the aqueous mass boundary layer is imaged. In addition to the fluorescence measurement, we conducted collocated and simultaneous thermography and wave imaging measurements. With this data set, it is possible to compare heat and gas transfer and to investigate the effect of waves on both transfer processes. First results will be presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Svenja Reith</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High-speed imaging of short wind waves by shape from refraction</style></title><secondary-title><style face="normal" font="default" size="100%">J. Europ. Opt. Soc. Rap. Public.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">14015</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kleesiek, J.</style></author><author><style face="normal" font="default" size="100%">A. Biller</style></author><author><style face="normal" font="default" size="100%">Urban, G.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">M. Bendszus</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ilastik for Multi-modal Brain Tumor Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pages><style face="normal" font="default" size="100%">12-17</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Blumenthal, F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Information-Geometric Optimization for Image Segmentation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Instance Label Prediction by Dirichlet Process Multiple Instance Learning</style></title><secondary-title><style face="normal" font="default" size="100%">UAI. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interactive Segmentation, Uncertainty and Learning</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hoai, Minh</style></author><author><style face="normal" font="default" size="100%">Torresani, Lorenzo</style></author><author><style face="normal" font="default" size="100%">De La Torre, Fernando</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning discriminative localization from weakly labeled data</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Discriminative discovery</style></keyword><keyword><style  face="normal" font="default" size="100%">Event detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Image classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Object detection</style></keyword><keyword><style  face="normal" font="default" size="100%">Time series classification</style></keyword><keyword><style  face="normal" font="default" size="100%">Weakly supervised learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">mar</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">1523–1534</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g., in terms of window size and location) which may be suboptimal for classification. We propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach simultaneously localizes the instances of the positive class and learns a subwindow SVM to recognize them. We extend our method to classification of time series by presenting an algorithm that localizes the most discriminative set of temporal segments in the signal. We evaluate our approach on several datasets for object and action recognition and show that it achieves results similar and in many cases superior to those obtained with full supervision. © 2013 Elsevier Ltd. All rights reserved.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Antic, B.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Latent Constituents for Recognition of Group Activities in Video</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the European Conference on Computer Vision (ECCV) (Oral)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">33--47</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning-based Segmentation for Connectomics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Bopp</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Luft- und wasserseitige Strömungsverhältnisse im ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron)</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/17151</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Bopp</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Luft- und wasserseitige Strömungsverhältnisse im ringförmigen Heidelberger Wind-Wellen-Kanal (Aeolotron)</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">mastersMaster&#039;s thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, Jorg Hendrik</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves</style></title><secondary-title><style face="normal" font="default" size="100%">International Workshop on Graphical Models in Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-16181-5_37</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Christopher J. Zappa</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurement of wind waves statistics from specular reflections</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Science Meeting, 23--28. 02. 2014, Honolulu Hawaii</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Reflective Stereo Slope Gauge (RSSG) [1] was deployed to two cruises in the tropical Pacific Ocean in Dec. 2011 and Dec. 2012 to measure statistics of small-scale wind waves. During the cruises, both open ocean (Samoa - Hawaii, OSSPRE 2011 on R/V Kilo Moana) and coastal upwelling (off Peru, M91 on German FS Meteor) areas were studied. Surface displacement (wave height time series with 50 Hz sampling rate) was measured by a stereo system with two light sources (Helmholtz stereopsis), while statistics of surface slope were obtained using a method related to Cox &amp; Munk&#039;s sun glitter technique [2]. Furthermore, information on the scale of the smallest waves was gained from the brightness of specular reflections (which is related to surface curvature). This parameter is useful for determining the presence of surface slicks. The results underline the importance of monitoring parameters other than wind speed during gas exchange measurements. The presented methods allow for robust estimates of surface slope statistics under a wide range of conditions. [1] Kiefhaber, D., Rocholz, R., Balschbach, G. and Jähne, B., Improved optical instrument for the measurement of water wave statistics in the field, in: Gas Transfer at Water Surfaces, Kyoto University Press, 2011. [2] Cox, C. and W. Munk (1954), Measurements of the roughness of the sea surface from photographs of the sun&#039;s glitter, J. Opt. Soc. Amer., 44 (11), 838-850.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Evridiki Mesarchaki</style></author><author><style face="normal" font="default" size="100%">Christine Kräuter</style></author><author><style face="normal" font="default" size="100%">Kerstin Ellen Krall</style></author><author><style face="normal" font="default" size="100%">Maximilian Bopp</style></author><author><style face="normal" font="default" size="100%">F. Helleis</style></author><author><style face="normal" font="default" size="100%">Jonathan Williams</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring air-sea gas exchange velocities in a large scale annular wind-wave tank</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Sci. 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Bendszus</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Kleesiek, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pages><style face="normal" font="default" size="100%">31-35</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wieler, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple Instance Learning with Random Forests and Applications in Industrial Optical Inspection</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple instance learning with response-optimized random forests</style></title><secondary-title><style face="normal" font="default" size="100%">ICPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pages><style face="normal" font="default" size="100%">3768 - 3773</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kandemir, M.</style></author><author><style face="normal" font="default" size="100%">Klami, A.</style></author><author><style face="normal" font="default" size="100%">Gonen, M.</style></author><author><style face="normal" font="default" size="100%">Vetek, A.</style></author><author><style face="normal" font="default" size="100%">Kaski, S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-task and multi-view learning of user state</style></title><secondary-title><style face="normal" font="default" size="100%">Neurocomputing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">139</style></volume><pages><style face="normal" font="default" size="100%">97-106</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Urban, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural Networks: Optimization and Applications</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Márquez-Neila, Pablo</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Baumela, Luis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-parametric higher-order random fields for image segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">biomedical image analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">higher-order models</style></keyword><keyword><style  face="normal" font="default" size="100%">image denoising</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">structured prediction</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">PART 6</style></number><volume><style face="normal" font="default" size="100%">8694 LNCS</style></volume><pages><style face="normal" font="default" size="100%">269–284</style></pages><isbn><style face="normal" font="default" size="100%">9783319105987</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Models defined using higher-order potentials are becoming increasingly popular in computer vision. However, the exact representation of a general higher-order potential defined over many variables is computationally unfeasible. This has led prior works to adopt parametric potentials that can be compactly represented. This paper proposes a non-parametric higher-order model for image labeling problems that uses a patch-based representation of its potentials. We use the transformation scheme of [11, 25] to convert the higher-order potentials to a pair-wise form that can be handled using traditional inference algorithms. This representation is able to capture structure, geometrical and topological information of labels from training data and to provide more precise segmentations. Other tasks such as image denoising and reconstruction are also possible. We evaluate our method on denoising and segmentation problems with synthetic and real images. © 2014 Springer International Publishing.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Takami, M.</style></author><author><style face="normal" font="default" size="100%">Bell, P.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Offline Learning of Prototypical Negatives for Efficient Online Exemplar SVM</style></title><secondary-title><style face="normal" font="default" size="100%">Winter Conference on Applications of Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6836075</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">377--384</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Petra, Stefania</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Scherzer, O.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical Flow</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Mathematical Methods in Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><edition><style face="normal" font="default" size="100%">2nd</style></edition><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">in press</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical Measurement of Short Wind Waves --- from the Laboratory to the Field</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/16304</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jug, Florian</style></author><author><style face="normal" font="default" size="100%">Pietzsch, Tobias</style></author><author><style face="normal" font="default" size="100%">Kainmüller, Dagmar</style></author><author><style face="normal" font="default" size="100%">Funke, Jan</style></author><author><style face="normal" font="default" size="100%">Kaiser, Matthias</style></author><author><style face="normal" font="default" size="100%">van Nimwegen, Erik</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Myers, Gene</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal joint segmentation and tracking of escherichia coli in the mother machine</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">8677</style></volume><pages><style face="normal" font="default" size="100%">25–36</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a graphical model for the joint segmentation and tracking of E. coli cells from time lapse videos. In our setup cells are grown in narrow columns (growth channels) in a so-called “Mother Machine” [1]. In these growth channels, cells are vertically aligned, grow and divide over time, and eventually leave the channel at the top. The model is built on a large set of cell segmentation hypotheses for each video frame that we extract from data using a novel parametric max-flow variation. Possible tracking assignments between segments across time, including cell identity mapping, cell division, and cell exit events are enumerated. Each such assignment is represented as a binary decision variable with unary costs based on image and object features of the involved segments. We find a cost-minimal and consistent solution by solving an integer linear program. We introduce a new and important type of constraint that ensures that cells exit the Mother Machine in the correct order. Our method finds a globally optimal tracking solution with an accuracy of &gt; 95% (1.22 times the inter-observer error) and is on average 2 − 11 times faster than the microscope produces the raw data.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sven Wanner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Orientation Analysis in 4D Light Fields</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sven Wanner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Orientation Analysis in 4D Light Fields</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/16439</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Julian Yarkony</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Pierre Baldi</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parallel Multicut Segmentation via Dual Decomposition</style></title><secondary-title><style face="normal" font="default" size="100%">New Frontiers in Mining Complex Patterns - Third International Workshop, {NFMCP} 2014, Held in Conjunction with {ECML-PKDD} 2014, Nancy, France, September 19, 2014, Revised Selected Papers</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-17876-9_4</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Swoboda, P.</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Partial Optimality by Pruning for MAP-inference with General GraphicalModels</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pages><style face="normal" font="default" size="100%">1170-1177</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Partial Optimality by Pruning for MAP-inference with General Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Conference on Computer Vision and Pattern Recognition 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Kappes, Jörg H.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Partial Optimality by Pruning for MAP-inference with General Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Conference on Computer Vision and Pattern Recognition 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreea Denitiu</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Schnörr, Cl.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections</style></title><secondary-title><style face="normal" font="default" size="100%">Fundamenta Informaticae</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">135</style></volume><pages><style face="normal" font="default" size="100%">73--102</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreea Denitiu</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, Cl.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections</style></title><secondary-title><style face="normal" font="default" size="100%">Fundamenta Informaticae</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">135</style></volume><pages><style face="normal" font="default" size="100%">73–102</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Besse, Frederic</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Fitzgibbon, Andrew</style></author><author><style face="normal" font="default" size="100%">Kautz, Jan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PMBP: PatchMatch Belief Propagation for correspondence field estimation</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Belief propagation</style></keyword><keyword><style  face="normal" font="default" size="100%">Correspondence fields</style></keyword><keyword><style  face="normal" font="default" size="100%">PatchMatch</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">oct</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><volume><style face="normal" font="default" size="100%">110</style></volume><pages><style face="normal" font="default" size="100%">2–13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">PatchMatch (PM) is a simple, yet very powerful and successful method for optimizing continuous labelling problems. The algorithm has two main ingredients: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these ingredients are related to steps in a specific form of belief propagation (BP) in the continuous space, called max-product particle BP (MP-PBP). However, MP-PBP has thus far been too slow to allow complex state spaces. In the case where all nodes share a common state space and the smoothness prior favours equal values, we show that unifying the two approaches yields a new algorithm, PMBP, which is more accurate than PM and orders of magnitude faster than MP-PBP. To illustrate the benefits of our PMBP method we have built a new stereo matching algorithm with unary terms which are borrowed from the recent PM Stereo work and novel realistic pairwise terms that provide smoothness. We have experimentally verified that our method is an improvement over state-of-the-art techniques at sub-pixel accuracy level.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Besse, Frederic</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Fitzgibbon, Andrew</style></author><author><style face="normal" font="default" size="100%">Kautz, Jan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PMBP: PatchMatch Belief Propagation for correspondence field estimation</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Belief propagation</style></keyword><keyword><style  face="normal" font="default" size="100%">Correspondence fields</style></keyword><keyword><style  face="normal" font="default" size="100%">PatchMatch</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">110</style></volume><pages><style face="normal" font="default" size="100%">2–13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">PatchMatch (PM) is a simple, yet very powerful and successful method for optimizing continuous labelling problems. The algorithm has two main ingredients: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these ingredients are related to steps in a specific form of belief propagation (BP) in the continuous space, called max-product particle BP (MP-PBP). However, MP-PBP has thus far been too slow to allow complex state spaces. In the case where all nodes share a common state space and the smoothness prior favours equal values, we show that unifying the two approaches yields a new algorithm, PMBP, which is more accurate than PM and orders of magnitude faster than MP-PBP. To illustrate the benefits of our PMBP method we have built a new stereo matching algorithm with unary terms which are borrowed from the recent PM Stereo work and novel realistic pairwise terms that provide smoothness. We have experimentally verified that our method is an improvement over state-of-the-art techniques at sub-pixel accuracy level.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Besse, Frederic</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Fitzgibbon, Andrew</style></author><author><style face="normal" font="default" size="100%">Kautz, Jan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PMBP: PatchMatch Belief Propagation for correspondence field estimation</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Belief propagation</style></keyword><keyword><style  face="normal" font="default" size="100%">Correspondence fields</style></keyword><keyword><style  face="normal" font="default" size="100%">PatchMatch</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">110</style></volume><pages><style face="normal" font="default" size="100%">2–13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">PatchMatch (PM) is a simple, yet very powerful and successful method for optimizing continuous labelling problems. The algorithm has two main ingredients: the update of the solution space by sampling and the use of the spatial neighbourhood to propagate samples. We show how these ingredients are related to steps in a specific form of belief propagation (BP) in the continuous space, called max-product particle BP (MP-PBP). However, MP-PBP has thus far been too slow to allow complex state spaces. In the case where all nodes share a common state space and the smoothness prior favours equal values, we show that unifying the two approaches yields a new algorithm, PMBP, which is more accurate than PM and orders of magnitude faster than MP-PBP. To illustrate the benefits of our PMBP method we have built a new stereo matching algorithm with unary terms which are borrowed from the recent PM Stereo work and novel realistic pairwise terms that provide smoothness. We have experimentally verified that our method is an improvement over state-of-the-art techniques at sub-pixel accuracy level.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, Fabian</style></author><author><style face="normal" font="default" size="100%">Schmidt, Stefan</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Med. Image Anal.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">781–794</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, F.</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Image Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">781-794</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, F.</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Image Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">781-794</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Distributions, Laplace</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proof of Lemma 2 Proof of Lemma 3 Proof of Theorem 4 Proof of Lemma 10</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">16</style></number><pages><style face="normal" font="default" size="100%">9–11</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">{Let us write down the linear program corresponding to the QPBO method. The roof duality relaxation for function E is given by equation (17). Adding pairwise terms Cx a x b for a, b ∈ A(p)</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eigenstetter, A.</style></author><author><style face="normal" font="default" size="100%">Takami, M.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Randomized Max-Margin Compositions for Visual Recognition</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">3590--3597</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Esparza, Jose</style></author><author><style face="normal" font="default" size="100%">Vepa, Leo</style></author><author><style face="normal" font="default" size="100%">Helmle, Michael</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Registration of a multi-camera system with a 3D laser range finder</style></title><secondary-title><style face="normal" font="default" size="100%">9th Workshop Driver Assistance Systems (FAS2014), 26.-28.03.2014, Walting</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.uni-das.de/de/Veranstaltungen/fas2014.php</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">37--46</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">He, Xiaowei</style></author><author><style face="normal" font="default" size="100%">Wang, H</style></author><author><style face="normal" font="default" size="100%">Zhang, Fengjun</style></author><author><style face="normal" font="default" size="100%">Wang, Guoping</style></author><author><style face="normal" font="default" size="100%">Zhou, Kun</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows</style></title><secondary-title><style face="normal" font="default" size="100%">Life.Kunzhou.Net</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.acm.org/10.1145/XXXXXXX.YYYYYYY http://life.kunzhou.net/2013/SPHsurfacetension.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">212</style></number><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">1–8</style></pages><isbn><style face="normal" font="default" size="100%">0103660054</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The control polygon of a Bezier curve is well-defined and meaningful–-there is a sequence of weights under which the limiting position of the curve is the control polygon. For a Bezier surface patch, there are many possible polyhedral control structures, and none are canonical. We propose a not necessarily polyhedral control structure for surface patches, regular control surfaces, which are certain C\^0 spline surfaces. While not unique, regular control surfaces are exactly the possible limiting positions of a Bezier patch when the weights are allowed to vary, but the control points are fixed.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maco, B.</style></author><author><style face="normal" font="default" size="100%">Cantoni, M.</style></author><author><style face="normal" font="default" size="100%">Holtmaat, A.</style></author><author><style face="normal" font="default" size="100%">Anna Kreshuk</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">G. W. Knott</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Protocols</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">1354-1366</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Drory, A.</style></author><author><style face="normal" font="default" size="100%">Haubold, C.</style></author><author><style face="normal" font="default" size="100%">Avidan, S.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-Global Matching: A Principled Derivation in Terms of Message Passing</style></title><secondary-title><style face="normal" font="default" size="100%">GCPR. 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Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy</style></title><secondary-title><style face="normal" font="default" size="100%">Histochemistry and Cell Biology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">141</style></volume><pages><style face="normal" font="default" size="100%">613-627</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous measurements of solubilities and diffusion coeffcients of volatile species in liquids</style></title><secondary-title><style face="normal" font="default" size="100%">7th SOPRAN Annual Meeting, Bremen, Germany, 25-26 March 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM J.~Imag.~Sci.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">2139--2174</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lenzen, F.</style></author><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM J. Imag. Sci.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">2139–2174</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Solving QVIs for Image Restoration with Adaptive Constraint Sets</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal on Imaging Sciences (SIIMS), in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sparse Space-Time Deconvolution for Calcium Image Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://papers.nips.cc/paper/5342-sparse-space-time-deconvolution-for-calcium-image-analysis</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">64-72</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Svenja Reith</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-temporal slope measurement of short wind waves under the influence of surface films at the Heidelberg Aeolotron</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/17697</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hornáček, Michael</style></author><author><style face="normal" font="default" size="100%">Fitzgibbon, Andrew</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SphereFlow: 6 DoF scene flow from RGB-D pairs</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><pages><style face="normal" font="default" size="100%">3526–3533</style></pages><isbn><style face="normal" font="default" size="100%">9781479951178</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We take a new approach to computing dense scene flow between a pair of consecutive RGB-D frames. We exploit the availability of depth data by seeking correspondences with respect to patches specified not as the pixels inside square windows, but as the 3D points that are the inliers of spheres in world space. Our primary contribution is to show that by reasoning in terms of such patches under 6 DoF rigid body motions in 3D, we succeed in obtaining compelling results at displacements large and small without relying on either of two simplifying assumptions that pervade much of the earlier literature: brightness constancy or local surface planarity. As a consequence of our approach, our output is a dense field of 3D rigid body motions, in contrast to the 3D translations that are the norm in scene flow. Reasoning in our manner additionally allows us to carry out occlusion handling using a 6 DoF consistency check for the flow computed in both directions and a patchwise silhouette check to help reason about alignments in occlusion areas, and to promote smoothness of the flow fields using an intuitive local rigidity prior. We carry out our optimization in two steps, obtaining a first correspondence field using an adaptation of PatchMatch, and subsequently using alpha-expansion to jointly handle occlusions and perform regularization. We show attractive flow results on challenging synthetic and real-world scenes that push the practical limits of the aforementioned assumptions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Krolla</style></author><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author><author><style face="normal" font="default" size="100%">Didier Stricker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spherical Light Fields</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the British Machine Vision Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">BMVA Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A full view spherical camera exploits its extended field of view to map the complete environment onto a 2D image plane. Thus, with a single shot, it delivers a lot more information about the surroundings than one can gather with a normal perspective or plenoptic camera, which are commonly used in light field imaging. However, in contrast to a light field camera, a spherical camera does not capture directional information about the incident light, and thus a single shot from a spherical camera is not sufficient to reconstruct 3D scene geometry. In this paper, we introduce a method combining spherical imaging with the light field approach. To obtain 3D information with a spherical camera, we capture several independent spherical images by applying a constant vertical offset between the camera positions and combine the images in a Spherical Light Field (SLF). We can then compute disparity maps by structure tensor orientation analysis on epipolar plane images, which in this context are 2D cuts through the spherical light field with constant azimuth angle. This method competes with the acquisition range of laser scanners and allows for a fast and extensive recording of a given scene. We benchmark our approach by comparing disparity maps of ray-traced scenes against its ground truth. Further we provide disparity maps of real world datasets. </style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Nair, Rahul</style></author><author><style face="normal" font="default" size="100%">Stephan Meister</style></author><author><style face="normal" font="default" size="100%">Wolfgang Mischler</style></author><author><style face="normal" font="default" size="100%">Güssefeld, Burkhard</style></author><author><style face="normal" font="default" size="100%">Sabine Hofmann</style></author><author><style face="normal" font="default" size="100%">Brenner, Claus</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stereo ground truth with error bars</style></title><secondary-title><style face="normal" font="default" size="100%">Asian Conference on Computer Vision, ACCV 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lou, X.</style></author><author><style face="normal" font="default" size="100%">Kloft, M.</style></author><author><style face="normal" font="default" size="100%">Rätsch, G.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Nowozin, S. et al</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Structured Learning from Cheap Data</style></title><secondary-title><style face="normal" font="default" size="100%">Advanced Structured Prediction</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">The MIT Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author><author><style face="normal" font="default" size="100%">M. 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Pattern Analysis Machine Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">606--619</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nils Krah</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualization of air and water-sided concentration profiles in laboratory gas exchange experiments</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/16895</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Esparza, Jose</style></author><author><style face="normal" font="default" size="100%">Helmle, Michael</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Jiang, Xiaoyi</style></author><author><style face="normal" font="default" size="100%">Joachim Hornegger</style></author><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Wide base stereo with fisheye optics: a robust approach for 3D reconstruction in driving assistance</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, 36th German Conference, GCPR 2014, Münster, Germany, September 2-5, 2014</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8753</style></volume><pages><style face="normal" font="default" size="100%">342--353</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, C.</style></author><author><style face="normal" font="default" size="100%">Huber, F.</style></author><author><style face="normal" font="default" size="100%">Knop, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. 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Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">1267-1270</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hilgert, Markus</style></author><author><style face="normal" font="default" size="100%">Wink, Michael</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">625 Jahre Ruperto Carola und 25 Jahre Bildverarbeitung</style></title><secondary-title><style face="normal" font="default" size="100%">Universität Heidelberg. Menschen, Lebenswege, Forschung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/1033237000</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Gesellschaft der Freunde Universität Heidelberg e. V.</style></publisher><volume><style face="normal" font="default" size="100%">55</style></volume><pages><style face="normal" font="default" size="100%">71--73</style></pages><isbn><style face="normal" font="default" size="100%">978-3-00-040060-5</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Heidelberger Jahrbücher</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Röder, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Active Learning: New Approaches, and Industrial Applications</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive Second-Order Total Variation: An Approach Aware of Slope
Discontinuities</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 4th International Conference on Scale Space and
Variational Methods in Computer Vision SSVM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">7893</style></volume><pages><style face="normal" font="default" size="100%">61-73</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive Second-Order Total Variation: An Approach Aware of Slope Discontinuities</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">54</style></volume><pages><style face="normal" font="default" size="100%">371--398</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">2</style></issue><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanslovsky, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Advanced Cell Tacking-by-Assignment</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sindeev, Mikhail</style></author><author><style face="normal" font="default" size="100%">Konushin, Anton</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Alpha-flow for video matting</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">PART 3</style></number><volume><style face="normal" font="default" size="100%">7726 LNCS</style></volume><pages><style face="normal" font="default" size="100%">438–452</style></pages><isbn><style face="normal" font="default" size="100%">9783642374302</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work addresses the problem of video matting, that is extracting the opacity-layer of a foreground object from a video sequence. We introduce the notion of alpha-flow which corresponds to the flow in the opacity layer. The idea is derived from the process of rotoscoping, where a user-supplied object mask is smoothly interpolated between keyframes while preserving its correspondence with the underlying image. Our key contribution is an algorithm which infers both the opacity masks and the alpha-flow in an efficient and unified manner. We embed our algorithm in an interactive video matting system where the first and last frame of a sequence are given as keyframes, and additional user strokes may be provided in intermediate frames. 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Garbe</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Grzegorzek, M.</style></author><author><style face="normal" font="default" size="100%">Theobalt, C.</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author><author><style face="normal" font="default" size="100%">Theobalt, C.</style></author><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Denoising Strategies for Time-of-Flight Data</style></title><secondary-title><style face="normal" font="default" size="100%">Time-of-Flight Imaging: Algorithms, Sensors and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8200</style></volume><pages><style face="normal" font="default" size="100%">24-25</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Kim, Kwang In</style></author><author><style face="normal" font="default" size="100%">Schäfer, Henrik</style></author><author><style face="normal" font="default" size="100%">Nair, Rahul</style></author><author><style face="normal" font="default" size="100%">Stephan Meister</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Grzegorzek, Marcin</style></author><author><style face="normal" font="default" size="100%">Theobalt, Christian</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author><author><style face="normal" font="default" size="100%">Theobalt, Christian</style></author><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Denoising Strategies for Time-of-Flight Data</style></title><secondary-title><style face="normal" font="default" size="100%">Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8200</style></volume><pages><style face="normal" font="default" size="100%">25-45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schäfer, Henrik</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Depth and Intensity Based Edge Detection in Time-of-Flight Images</style></title><secondary-title><style face="normal" font="default" size="100%">3DV-Conference, 2013 International Conference on</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">111-118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schäfer, H.</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Depth and Intensity Based Edge Detection in Time-of-Flight Images</style></title><secondary-title><style face="normal" font="default" size="100%">3D Imaging, Modeling, Processing, Visualization and Transmission
(3DIMPVT), 2013 International Conference on</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">111-118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hornáček, Michael</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Depth super resolution by rigid body self-similarity in 3D</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dense matching</style></keyword><keyword><style  face="normal" font="default" size="100%">depth super resolution</style></keyword><keyword><style  face="normal" font="default" size="100%">optimization</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">1123–1130</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of high-resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of &#039;single-image&#039; super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars. © 2013 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thomas Luhmann</style></author><author><style face="normal" font="default" size="100%">Müller, Christina</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Der Standard EMVA 1288 zur Charakterisierung von Kameras und Bildsensoren: von 2D- zu 3D-Kameras</style></title><secondary-title><style face="normal" font="default" size="100%">Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Beiträge der Oldenburger 3D-Tage 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/17699</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Wichmann</style></publisher><pages><style face="normal" font="default" size="100%">388--399</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paulus Salomon Bauer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of an imaging polarimeter for water wave slope measurements</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/15899</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umwelphysik, Univeristät Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Lellmann, B.</style></author><author><style face="normal" font="default" size="100%">Widmann, F.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discrete and Continuous Models for Partitioning Problems</style></title><secondary-title><style face="normal" font="default" size="100%">Int.~J.~Comp.~Visionz</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">104</style></volume><pages><style face="normal" font="default" size="100%">241-269</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Uwe</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Jancsary, Jeremy</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discriminative Non-blind Deblurring</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are compu-tationally expensive. To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality. We address this gap by proposing a dis-criminative approach for non-blind deblurring. One key challenge is that the blur kernel in use at test time is not known in advance. To address this, we analyze existing approaches that use half-quadratic regularization. From this analysis, we derive a discriminative model cascade for image deblurring. Our cascade model consists of a Gaus-sian CRF at each stage, based on the recently introduced regression tree fields. We train our model by loss minimization and use synthetically generated blur kernels to generate training data. Our experiments show that the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Haeusler, R.</style></author><author><style face="normal" font="default" size="100%">Nair, R.</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ensemble Learning for Confidence Measures in Stereo Vision</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR 2013, in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">305-312</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Diebold</style></author><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Epipolar Plane Image Refocusing for Improved Depth Estimation and Occlusion Handling.</style></title><secondary-title><style face="normal" font="default" size="100%">VMV</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">dblp</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dblp.uni-trier.de/db/conf/vmv/vmv2013.html#DieboldG13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Eurographics Association</style></publisher><isbn><style face="normal" font="default" size="100%">978-3-905674-51-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In contrast to traditional imaging, the higher dimensionality of a light field offers directional information about the
captured intensity. This information can be leveraged to estimate the disparity of 3D points in the captured scene.
A recent approach to estimate disparities analyzes the structure tensor and evaluates the orientation on epipolar
plane images (EPIs). While the resulting disparity maps are generally satisfying, the allowed disparity range is
small and occlusion boundaries can become smeared and noisy. In this paper, we first introduce an approach to
extend the total allowed disparity range. This allows for example the investigation of camera setups with a larger
baseline, like in the Middlebury 3D light fields. Second, we introduce a method to handle the difficulties arising at
boundaries between fore- and background objects to achieve sharper edge transitions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hosni, Asmaa</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Bleyer, Michael</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast cost-volume filtering for visual correspondence and beyond</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Interactive Image Segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">optical flow</style></keyword><keyword><style  face="normal" font="default" size="100%">Stereo Matching</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">504–511</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge-preserving filter. In this paper, we propose a generic and simple framework comprising three steps: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-All label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas. © 2012 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerstin Ellen Krall</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">First air-sea gas exchange laboratory study at hurricane wind speeds</style></title><secondary-title><style face="normal" font="default" size="100%">Ocean Sci. Discuss.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.ocean-sci-discuss.net/10/1971/2013/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">1971--1996</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Getting Feasible Variable Estimates From Infeasible Ones: MRF Local
Polytope Study</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Inference for Probabilistic Graphical Models at ICCV.
Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Swoboda, P.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global MAP-Optimality by Shrinking the Combinatorial Search Area
with Convex Relaxation</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">1950-1958</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Accepted</style></notes><custom1><style face="normal" font="default" size="100%">http://hci.iwr.uni-heidelberg.de/opengm2/&quot;</style></custom1></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Kappes, Jorg Hendrik</style></author><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Accepted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sven Wanner</style></author><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally Consistent Multi-Label Assignment on the Ray Space of 4D Light Fields</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR 2013. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">1011-1018</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sven Wanner</style></author><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally consistent multi-label assignment on the ray space of 4D light fields</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernhard Schmitzer</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Hierarchical Approach to Optimal Transport</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space and Variational Methods (SSVM 2013)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">452-464</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vineet, Vibhav</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Torr, Philip H.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Higher order priors for joint intrinsic image, objects, and attributes estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many methods have been proposed to solve the problems of recovering intrinsic scene properties such as shape, reflectance and illumination from a single image, and object class segmentation separately. While these two problems are mutually informative, in the past not many papers have addressed this topic. In this work we explore such joint estimation of intrinsic scene properties recovered from an image, together with the estimation of the objects and attributes present in the scene. In this way, our unified framework is able to capture the correlations between intrinsic properties (reflectance, shape, illumination), objects (table, tv-monitor), and materials (wooden, plastic) in a given scene. For example, our model is able to enforce the condition that if a set of pixels take same object label, e.g. table, most likely those pixels would receive similar reflectance values. We cast the problem in an energy minimization framework and demonstrate the qualitative and quantitative improvement in the overall accuracy on the NYU and Pascal datasets.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Speth, Markus</style></author><author><style face="normal" font="default" size="100%">Reinelt, Gerhard</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Higher-order Segmentation via Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">ArXiv e-prints</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, Jorg Hendrik</style></author><author><style face="normal" font="default" size="100%">Speth, Markus</style></author><author><style face="normal" font="default" size="100%">Reinelt, Gerhard</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Higher-order Segmentation via Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">ArXiv e-prints</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jason Horn</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hochaufgelöste optische Wellenhöhenmessung am Aeolotron mit Laser-induzierter Fluoreszenz</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umwelphysik, Univeristät Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Patrick Fahle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hochauflösende Messung der raumzeitlichen Variation der Neigung winderzeugter Wasserwellen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umwelphysik, Univeristät Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Zwei neue Systeme zur Messung der Neigung winderzeugter Wasserwellen wurden an zwei Wind-Wellen Kanälen in Heidelberg aufgebaut. Das dabei verwendete Prinzip der Imaging Slope Gauge (ISG) bestimmt die Neigung der wellenbewegten Wasseroberfläche aus der Ablenkung von Lichtstrahlen, die an der Luft-Wasser-Grenzfläche gebrochen werden. Beide ISG-Systeme arbeiten mit der bisher unerreichten zeitlichen Auflösung von 1500 Hz, wodurch erstmals verhindert werden kann, dass die Messungen durch zeitliches Aliasing beeinträchtigt werden. Beide Systeme arbeiten jeweils mit unterschiedlichen helligkeitskodierten Lichtquellen. Für die größere der beiden Lichtquellen wurde in dieser Arbeit eine neue Methode zur Kalibrierung entwickelt, getestet und angewendet. Die Methode wird ausführlich beschrieben und wird bei zukünftigen Messungen am Heidelberger Aeolotron zum Einsatz kommen. Die Methode der bereits bestehenden Kalibrierung für die kleinere Lichtquelle wurde im Rahmen dieser Arbeit validiert und wird hier erstmals beschrieben. Ein in Heidelberg bisher nicht verwendeter Algorithmus zur Auswertung der Daten, die Fourier Decomposition Method (FDM), wird erläutert. Als Ergänzung zur bisher verwendeten Auswertung, in der Spektren durch direkte Fouriertransformation gewonnen wurden kann der Algorithmus durch Verwendung der Phaseninformation den Messbereich der ISG deutlich erweitern und auch Wellen mit kleineren Wellenzahlen erfassen. Der Algorithmus wurde auf Daten, aufgenommen am Heidelberger linearen Wind-Wellen Kanal, erfolgreich getestet.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Donath, A.</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">How Good is Crowdsourcing for Optical Flow Ground Truth Generation?</style></title><secondary-title><style face="normal" font="default" size="100%">submitted to CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Geese, Marc</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Sensor Nonuniformity Correction by a Scene-Based Maximum Likelihood Approach</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/14391</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">started 01.10.2009</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marc Geese</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Sensor Nonuniformity Correction by a Scene-Based Maximum Likelihood Approach</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Felix Friedl</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigating the Transfer of Oxygen at the Wavy Air-Water Interface under Wind-Induced Turbulence</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/14582</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Local oxygen (O2) transfer velocities measured in a linear wind-wave tunnel with respect to wind speed and fetch are presented in this thesis. For this, a non-intrusive laser-induced fluorescence (LIF) method was developed to measure vertical O2 concentration profiles in the water-sided mass boundary layer. The fluorophore used is a water soluble ruthenium complex, which is quenched according to the Stern-Volmer equation. This equation, which originally describes the quenching only for a weak excitation, was generalized for arbitrary laser irradiance. Measurements confirm this generalization and yield a new value for the Stern-Volmer constant. The LIF method was applied with high spatial and temporal resolution of 6.2 µm m and 1.2 kHz, respectively, in order to resolve the mass boundary layer and fast processes. To obtain mean O2 concentration profiles with high precision, an algorithm was developed to detect the water surface in the recorded images. The measured mean concentration profiles show a transition in the self-similar shape with the onset of waves. The results for a flat water surface are in agreement with the surface renewal model. For a wavy water surface, the small eddy model and the surface renewal model both describe the data equally well. Vanishing O2 concentration fluctuations at the flat water surface were measured, which is in agreement with existing models for a rigid interface. The local transfer velocities obtained from mean concentration profiles are best parametrized with the friction velocity. In this work, the great potential of LIF measurements to probe transfer velocities locally is demonstrated.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fiaschi, L.</style></author><author><style face="normal" font="default" size="100%">Karl-Heinz Grosser</style></author><author><style face="normal" font="default" size="100%">Afonso, B.</style></author><author><style face="normal" font="default" size="100%">Zlatic, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Keeping Count: Leveraging Temporal Context to Count Heavily Overlapping Objects</style></title><secondary-title><style face="normal" font="default" size="100%">ISBI 2013.Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pages><style face="normal" font="default" size="100%">656-659</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Peter, S.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">K-smallest Spanning Tree Segmentations</style></title><secondary-title><style face="normal" font="default" size="100%">German Conference on Pattern Recognition (DAGM/GCPR). Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">8142</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">375-384</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerstin E. Krall</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Laboratory Investigations of Air-Sea Gas Transfer under a Wide Range of Water Surface Conditions</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/14392</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Transfer velocities of 5 sparingly soluble gases were measured in two different wind wave tanks at wind speeds between u10=1.2 m/s and 67 m/s. Two different gas analysis techniques were used, FT-IR and UV spectroscopy. Additionally, a method was developed that allows the parallel measurement of gas transfer velocity and the solubility. The fast &#039;controlled leakage&#039; method for the measurement of gas transfer velocities was found to be not precise enough to measure Schmidt number exponents and transfer velocities in the Aeolotron. Gas transfer velocities measured spanned more than 3 orders of magnitude, lying between 0.5 cm/h and 1100 cm/h. At lower wind speeds, measured in the Heidelberg Aeolotron, the change of the Schmidt number exponent from 2/3 for a smooth to 1/2 for a wavy water surface was confirmed. A surfactant, which inhibits wave growth, was used in 3 of the 7 experiments. For all surfactant conditions, the change of the Schmidt number exponent spanned a wide range of wind speeds with the mid-point at u10=4.5 m/s for a clean, and at 9 m/s for a surface film covered water surface. It was confirmed that the mean square slope is suitable for the description of the transition of the Schmidt number exponent. The facet model could not reproduce the measured transfer velocities. The transfer velocities measured were found to scale very poorly with the commonly used parameter wind speed u10. The correlation between the mean square slope of the water surface and the transfer velocities was found to be good, except at the lowest mean square slopes. In the Kyoto high speed wind-wave tank, the effect of strong wave breaking and bubble entrainment on the gas transfer velocity was studied. Gas transfer velocities were split up into a purely wave induced part and a part caused by bubbles and wave breaking. The measured gas transfer velocities were found to be up to 350% larger than expected from waves alone at the highest wind speed. Three empirical parameterizations were tested on the bubble induced part, two successfully.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Walecki, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Large-Scale Automatic Reconstruction of Myelianated Axons and Detection of the Nodes of Ranvier</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fiaschi, L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Based Biological Image Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jancsary, Jeremy</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning convex QP relaxations for structured prediction</style></title><secondary-title><style face="normal" font="default" size="100%">30th International Conference on Machine Learning, ICML 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">PART 3</style></number><pages><style face="normal" font="default" size="100%">1952–1960</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a new large margin approach to discriminative training of intractable discrete graphical models. Our approach builds on a convex quadratic programming relaxation of the MAP inference problem. The model parameters are trained directly within this restricted class of energy functions so as to optimize the predictions on the training data. We address the issue of how to parameterize the resulting model and point out its relation to existing approaches. The primary motivation behind our use of the QP relaxation is its computational efficiency; yet, empirically, its predictive accuracy compares favorably to more expensive approaches. This makes it an appealing choice for many practical tasks. Copyright 2013 by the author(s).</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Multi-Level Sparse Representation</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://papers.nips.cc/paper/5076-learning-multi-level-sparse-representations</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ferran Diego</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Multi-Level Sparse Representation for Identifying Neuronal Activity</style></title><secondary-title><style face="normal" font="default" size="100%">Signal Processing with Adaptive Sparse Structured Representations Workshop (SPARS). Book of Abstracts.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Mikula, S.</style></author><author><style face="normal" font="default" size="100%">Denk, W.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning to Segment Neurons with Non-local Quality Measures</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI 2013. 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Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Atif, Muhammad</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal Depth Estimation from a Single Image by Computational Imaging Using Chromatic Aberrations</style></title><secondary-title><style face="normal" font="default" size="100%">tm --- Technisches Messen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><volume><style face="normal" font="default" size="100%">80</style></volume><pages><style face="normal" font="default" size="100%">343--348</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a computational imaging approach to estimate the depth from a single image using axial chromatic aberrations. It includes a co-design of optics and digital processing to select the optimal parameters of a lens such as focal length, f-number, and chromatic focal shift according to the performance of a depth estimation algorithm on the digital side. A simulation framework evaluates the complete systems performance in different imaging conditions including optimal axial chromatic lens aberration. A low-complexity algorithm estimates the depth map of real scenes. Experiments on real and synthetic scenes show the feasibility of the proposed system for depth estimation. In the case of relatively broadband object spectra and a lens with focal length of 4 mm, depth is estimated with an RMS error of 6–10%.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimality Bounds for a Variational Relaxation of the Image Partitioning
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Variational Methods in Computer Vision SSVM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">7893</style></number><pages><style face="normal" font="default" size="100%">477-488</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Savchynskyy, Bogdan</style></author><author><style face="normal" font="default" size="100%">Jörg H. 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Garbe</style></author><author><style face="normal" font="default" size="100%">Eisemann, Martin</style></author><author><style face="normal" font="default" size="100%">Magnor, Marcus</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Grzegorzek, Marcin</style></author><author><style face="normal" font="default" size="100%">Theobalt, Christian</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Survey on Time-of-Flight Stereo Fusion</style></title><secondary-title><style face="normal" font="default" size="100%">Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8200</style></volume><pages><style face="normal" font="default" size="100%">105-127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nair, Rahul</style></author><author><style face="normal" font="default" size="100%">Ruhl, Kai</style></author><author><style face="normal" font="default" size="100%">Lenzen, Frank</style></author><author><style face="normal" font="default" size="100%">Meister, Stephan</style></author><author><style face="normal" font="default" size="100%">Schäfer, Henrik</style></author><author><style face="normal" font="default" size="100%">Garbe, Christoph S.</style></author><author><style face="normal" font="default" size="100%">Eisemann, Martin</style></author><author><style face="normal" font="default" size="100%">Magnor, Marcus</style></author><author><style face="normal" font="default" size="100%">Kondermann, Daniel</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Grzegorzek, Marcin</style></author><author><style face="normal" font="default" size="100%">Theobalt, Christian</style></author><author><style face="normal" font="default" size="100%">Koch, Reinhard</style></author><author><style face="normal" font="default" size="100%">Kolb, Andreas</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Survey on Time-of-Flight Stereo Fusion</style></title><secondary-title><style face="normal" font="default" size="100%">Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8200</style></volume><pages><style face="normal" font="default" size="100%">105-127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Damien Lefloch</style></author><author><style face="normal" font="default" size="100%">Rahul Nair</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Henrik Schäfer</style></author><author><style face="normal" font="default" size="100%">Lee Streeter</style></author><author><style face="normal" font="default" size="100%">Michael J. Cree</style></author><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Grzegorzek, Marcin</style></author><author><style face="normal" font="default" size="100%">Theobalt, Christian</style></author><author><style face="normal" font="default" size="100%">Koch, Reinhard</style></author><author><style face="normal" font="default" size="100%">Kolb, Andreas</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Technical Foundation and Calibration Methods for Time-of-Flight Cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8200</style></volume><pages><style face="normal" font="default" size="100%">3-24</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lefloch, D.</style></author><author><style face="normal" font="default" size="100%">Nair, R.</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Schäfer, H.</style></author><author><style face="normal" font="default" size="100%">Streeter, L.</style></author><author><style face="normal" font="default" size="100%">Michael J. Cree</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author><author><style face="normal" font="default" size="100%">Grzegorzek, M.</style></author><author><style face="normal" font="default" size="100%">Theobalt, C.</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Technical Foundation and Calibration Methods for Time-of-Flight Cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Time-of-Flight Imaging: Algorithms, Sensors and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><number><style face="normal" font="default" size="100%">3-24</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8200</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author><author><style face="normal" font="default" size="100%">E. Strekalovskiy</style></author><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tight convex relaxations for vector-valued labeling</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal on Imaging Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Davis, James</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author><author><style face="normal" font="default" size="100%">Raskar, Ramesh</style></author><author><style face="normal" font="default" size="100%">Theobalt, Christian</style></author><author><style face="normal" font="default" size="100%">Davis, James</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Raskar, Ramesh</style></author><author><style face="normal" font="default" size="100%">Theobalt, Christian</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Time-of-Flight Imaging: Algorithms, Sensors and Applications (Dagstuhl Seminar 12431)</style></title><secondary-title><style face="normal" font="default" size="100%">Dagstuhl Reports</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://drops.dagstuhl.de/opus/volltexte/2013/3904</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">10</style></number><publisher><style face="normal" font="default" size="100%">Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik</style></publisher><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">79--104</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This report documents the program and the outcomes of Dagstuhl Seminar 12431 &quot;Time-of-Flight Imaging: Algorithms, Sensors and Applications&quot;. The seminar brought together researchers with diverse background from both academia and industry to discuss various aspects of Time-of-Flight imaging and general depth sensors. The executive summary and abstracts of the talks given during the seminar as well as the outcome of several working groups on specific research topics are presented in this report.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yarlagadda, P.</style></author><author><style face="normal" font="default" size="100%">Monroy, A.</style></author><author><style face="normal" font="default" size="100%">Bernd Carque</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Computer-based Understanding of Medieval Images</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific Computing &amp; Cultural Heritage</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007/978-3-642-28021-4_10</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">89--97</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-28020-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Speth, Markus</style></author><author><style face="normal" font="default" size="100%">Reinelt, Gerhard</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Markus Speth</style></author><author><style face="normal" font="default" size="100%">Gerhard Reinelt</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernhard X. Kausler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tracking-by-Assignment as a Probabilistic Graphical Model with Applications in Developmental Biology</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Swoboda, Paul</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Heyden, Anders</style></author><author><style face="normal" font="default" size="100%">Kahl, Fredrik</style></author><author><style face="normal" font="default" size="100%">Oskarsson, Magnus</style></author><author><style face="normal" font="default" size="100%">Tai, Xue-Cheng</style></author><author><style face="normal" font="default" size="100%">Olsson, Carl</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Image Segmentation and Cosegmentation with the Wasserstein Distance</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Minimization Methods in Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8081</style></volume><pages><style face="normal" font="default" size="100%">321--334</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-40394-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paul Swoboda</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Anders Heyden</style></author><author><style face="normal" font="default" size="100%">Fredrik Kahl</style></author><author><style face="normal" font="default" size="100%">Carl Olsson</style></author><author><style face="normal" font="default" size="100%">Magnus Oskarsson</style></author><author><style face="normal" font="default" size="100%">Xue-Cheng Tai</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Image Segmentation and Cosegmentation with the Wasserstein Distance</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Minimization Methods in Computer Vision and Pattern Recognition</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">8081</style></volume><pages><style face="normal" font="default" size="100%">321–334</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-40394-1</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Jörg H. 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A recent, successful trend in unsupervised object extraction is to exploit so-called &quot;3D scene-consistency&quot;, that is enforcing that objects obey underlying physical constraints of the 3D scene, such as occupancy of 3D space and gravity of objects. Our main contribution is to introduce the concept of 3D scene-consistency into stereo matching. We show that this concept is beneficial for both tasks, object extraction and depth estimation. In particular, we demonstrate that our approach is able to create a large set of 3D scene-consistent object proposals, by varying e.g. the prior on the number of objects. After automatically ranking the proposals we show experimentally that our results are considerably closer to ground truth than state-of-the-art techniques which either use stereo or monocular images. We envision that our method will build the front-end of a future object recognition system for stereo images. © 2012 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bleyer, Michael</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extracting 3D scene-consistent object proposals and depth from stereo images</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Object Segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Stereo Matching</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">PART 5</style></number><volume><style face="normal" font="default" size="100%">7576 LNCS</style></volume><pages><style face="normal" font="default" size="100%">467–481</style></pages><isbn><style face="normal" font="default" size="100%">9783642337147</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work combines two active areas of research in computer vision: unsupervised object extraction from a single image, and depth estimation from a stereo image pair. A recent, successful trend in unsupervised object extraction is to exploit so-called &quot;3D scene-consistency&quot;, that is enforcing that objects obey underlying physical constraints of the 3D scene, such as occupancy of 3D space and gravity of objects. Our main contribution is to introduce the concept of 3D scene-consistency into stereo matching. We show that this concept is beneficial for both tasks, object extraction and depth estimation. In particular, we demonstrate that our approach is able to create a large set of 3D scene-consistent object proposals, by varying e.g. the prior on the number of objects. After automatically ranking the proposals we show experimentally that our results are considerably closer to ground truth than state-of-the-art techniques which either use stereo or monocular images. 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Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><pages><style face="normal" font="default" size="100%">2306-2309</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jancsary, Jeremy</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Loss-specific training of non-parametric image restoration models: A new state of the art</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">PART 7</style></number><volume><style face="normal" font="default" size="100%">7578 LNCS</style></volume><pages><style face="normal" font="default" size="100%">112–125</style></pages><isbn><style face="normal" font="default" size="100%">9783642337857</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. © 2012 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jancsary, Jeremy</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Loss-specific training of non-parametric image restoration models: A new state of the art</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">PART 7</style></number><volume><style face="normal" font="default" size="100%">7578 LNCS</style></volume><pages><style face="normal" font="default" size="100%">112–125</style></pages><isbn><style face="normal" font="default" size="100%">9783642337857</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">After a decade of rapid progress in image denoising, recent methods seem to have reached a performance limit. Nonetheless, we find that state-of-the-art denoising methods are visually clearly distinguishable and possess complementary strengths and failure modes. Motivated by this observation, we introduce a powerful non-parametric image restoration framework based on Regression Tree Fields (RTF). Our restoration model is a densely-connected tractable conditional random field that leverages existing methods to produce an image-dependent, globally consistent prediction. We estimate the conditional structure and parameters of our model from training data so as to directly optimize for popular performance measures. In terms of peak signal-to-noise-ratio (PSNR), our model improves on the best published denoising method by at least 0.26dB across a range of noise levels. Our most practical variant still yields statistically significant improvements, yet is over 20x faster than the strongest competitor. Our approach is well-suited for many more image restoration and low-level vision problems, as evidenced by substantial gains in tasks such as removal of JPEG blocking artefacts. © 2012 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eigenstetter, A.</style></author><author><style face="normal" font="default" size="100%">Yarlagadda, P.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedins of the Aian Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">152--163</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Philipp Eger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung der Luftströmung über kleinskaligen Wasserwellen mittels Particle Streak Velocimetry in einem linearen Wind-Wellen-Kanal</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Glas, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Methoden zur sechsdimensionalen Objektlageerkennung aus Tiefenbildern</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/13581</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Glas, Manuel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Methoden zur sechsdimensionalen Objektlageerkennung aus Tiefenbildern</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author><author><style face="normal" font="default" size="100%">E. Strekalovskiy</style></author><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Natural Vectorial Total Variation which Arises from Geometric
Measure Theory</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal on Imaging Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">537-563</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bastian Goldlücke</style></author><author><style face="normal" font="default" size="100%">E. Strekalovskiy</style></author><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The natural vectorial total variation which arises from geometric measure theory</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal on Imaging Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neuerungen zum EMVA Standard 1288, Der Release 3.1 des etablierten Standards zur Kameracharakterisierung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jancsary, Jeremy</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-parametric crfs for image labeling</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS Workshop Modern Nonparametric Methods in Machine Learning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><pages><style face="normal" font="default" size="100%">1–5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce a powerful non-parametric image labeling framework, Regression Tree Fields (RTFs), and discuss its application to image restoration. The conditional structure and the parameters of our model are estimated from training data so as to directly optimize for popular performance measures, resulting in excellent predictive performance at low computational cost.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Voss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel Single Camera Techniques for 3D3C Lagrangian Trajectory Measurements in Interfacial Flows</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/13362</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">started 01.02.2009</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horvát, E.-Á.</style></author><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">K. A. Zweig</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">One plus one makes three (for social networks)</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">4,7</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Kappes, Jörg H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">OpenGM: A C++ Library for Discrete Graphical Models</style></title><secondary-title><style face="normal" font="default" size="100%">ArXiv e-prints</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Projectpage: http://hci.iwr.uni-heidelberg.de/opengm2/</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolfgang Mischler</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical measurements of bubbles and spray in wind/water facilities at high wind speeds</style></title><secondary-title><style face="normal" font="default" size="100%">12th International Triennial Conference on Liquid Atomization and Spray Systems 2012, Heidelberg (ICLASS 2012)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">An optical imaging technique for the measurement of the size and velocity distribution of bubbles and droplets is presented. This method was used in a wind-wave tank at high wind-speeds to investigate bubble populations and spray generated by breaking water-waves. These measurements help to model bubble and spray induced gas exchange between atmosphere and ocean. The setup consists of a 2040  1088 CMOS-camera (Basler acA2000-340km, 5.5 mum pixel size), which is used in a telecentric bright-field setup. Thus bubbles or droplets between the light source and the camera become visible in the images as dark disks because of light scattering. For illumination, a single high-power LED (Cree XP-E) in a telecentric illumination system is used. This LED is operated in a pulsed mode with pulse lengths smaller than 2 mus. This is the effective exposure time. In order to acquire two images with a small temporal distance, the camera is set to maximal exposure time. The first image is recorded by flashing the LED at the end of the first exposure and the second image by flashing at the beginning of the second exposure. The effective time difference of consecutive exposures was between 1 mus and twice the exposure time of the camera. The telecentricity (only rays parallel to the optical axis contribute to the images) of the optics reduces the error of the size estimation of the bubbles/droplets. In addition the position along the optical axis can be estimated by a depth from defocus method. With this information it is also possible to determine the measuring volume to estimate the bubble/spray-number density. The size range of this technique is determined by the pixel and sensor size and by the resolution of the lens. Here, a lens with a working distance of 135 mm and a magnification of 0.37 is used, so that one pixel corresponds to 15 mum in object space. With this setup a radius range of 40 mum - 5000 mum can be imaged. The main source for the error of the radius is the blurring of the edges due to defocus. Therefore the accuracy depends on the quality of telecentricity and complexity of image processing. For bubbles with a radius larger than 10 px the error is below 1 % when using simple image processing. This error can be reduced significantly by using elaborate image processing, which is also able to detect bubbles as small as 3 px in size. Different image processing algorithms for spherical objects are presented and investigated regarding accuracy. Bubbles or droplets which show deviation from sphere shape introduce problems for the image processing. For bubbles in still water, such deviations are typically found for radii of greater than 500 mum. Capabilities and limits of the technique are addressed on the example of two experiments. Test measurements were conducted at the high wind speed wind-wave tank in Kyoto, Japan at a wind speed of 42 m/s. This experiment showed that distributions of bubbles can be measured at high wind speeds with no problems in regions, which are not near to the water surface (&gt; 2 cm). For regions close to the interface the image processing algorithms need to be adjusted, since portions of the illumination can be occluded by waves. It is shown with an experiment in the small linear wind/wave facility in Heidelberg, that this technique also works for spray measurements. Systematic measurements of spray could not be conducted in Kyoto, since the walls of the tank were covered by a thin film of water caused by the spray, which heavily disturbed the optical path.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optik, Photonik und Bildverarbeitung --- eine spannende Reise</style></title><secondary-title><style face="normal" font="default" size="100%">Optik &amp; Photonik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">2--3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Atif, Muhammad</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">F. Puente Leon</style></author><author><style face="normal" font="default" size="100%">M. Heizmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal Depth Estimation from a Single Image by Computational Imaging using Chromatic Aberrations</style></title><secondary-title><style face="normal" font="default" size="100%">Forum Bildverarbeitung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://digbib.ubka.uni-karlsruhe.de/volltexte/1000030440</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">KIT Scientific Publishing</style></publisher><pages><style face="normal" font="default" size="100%">23--34</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Erz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">F. Puente Leon</style></author><author><style face="normal" font="default" size="100%">M. 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Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://icml.cc/discuss/2012/753.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Nickisch, Hannes</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">User-centric learning and evaluation of interactive segmentation systems</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Interactive systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">dec</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">100</style></volume><pages><style face="normal" font="default" size="100%">261–274</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. In this paper, we study the problem of evaluating and learning interactive segmentation systems which are extensively used in the real world. The key questions in this context are how to measure (1) the effort associated with a user interaction, and (2) the quality of the segmentation result as perceived by the user. We conduct a user study to analyze user behavior and answer these questions. Using the insights obtained from these experiments, we propose a framework to evaluate and learn interactive segmentation systems which brings the user in the loop. The framework is based on the use of an active robot user-a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem. © 2012 The Author(s).</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, Bernhard</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Schröder, Andreas</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Adaptive Correlation Method for Flow Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">3053 -- 3065</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, Bernhard</style></author><author><style face="normal" font="default" size="100%">Petra, Stefania</style></author><author><style face="normal" font="default" size="100%">Schröder, Andreas</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Adaptive Correlation Method for Flow Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">June</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">3053 – 3065</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Image Denoising with Adaptive Constraint Sets</style></title><secondary-title><style face="normal" font="default" size="100%">LNCS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">206-217</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lenzen, Frank</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Petra, Stefania</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Image Denoising with Adaptive Constraint Sets</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 3rd International Conference on Scale Space and Variational Methods in Computer Vision 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">206-217</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mansfield, Alex</style></author><author><style face="normal" font="default" size="100%">Gehler, Peter</style></author><author><style face="normal" font="default" size="100%">Van Gool, Luc</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visibility maps for improving seam carving</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.adobe.com/products/photoshop/photoshopextended/features/</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">PART 2</style></number><volume><style face="normal" font="default" size="100%">6554 LNCS</style></volume><pages><style face="normal" font="default" size="100%">131–144</style></pages><isbn><style face="normal" font="default" size="100%">9783642357398</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we present a new, improved seam carving algorithm. Seam carving efficiently removes pixels from an image to produce a retargeted image. It has proved popular with users and has been used as a component in many retargeting algorithms. We introduce the visibility map, a new framework for pixel removing image editing methods. This allows us to cast retargeting as a binary graph labelling problem. We derive a general algorithm which uses seam carving operations for efficient greedy optimization of a well defined energy, and compare this with forward energy seam carving and shift map image editing. We test this method with varying parameters on a large number of images, and present an improved seam carving algorithm which can demonstrably produce better results. We draw general conclusions about pixel removing methods for retargeting and motivate future directions of research. © 2012 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Eigenstetter, A.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visual Recognition using Embedded Feature Selection for Curvature Self-Similarity</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the Conference on Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><pages><style face="normal" font="default" size="100%">377--385</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Julia Schaper</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Christopher J. Zappa</style></author><author><style face="normal" font="default" size="100%">William E. Asher</style></author><author><style face="normal" font="default" size="100%">Jessup, A.T.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Water Surface Topography Measurements with the Reflective Stereo Slope Gauge</style></title><secondary-title><style face="normal" font="default" size="100%">AGU Ocean Science Meeting 2012, Salt Lake City</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernhard Schmitzer</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weakly Convex Coupling Continuous Cuts and Shape Priors</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space and Variational Methods (SSVM 2011)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><pages><style face="normal" font="default" size="100%">423-434</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meister, Stephan</style></author><author><style face="normal" font="default" size="100%">Izadi, Shahram</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">M Hämmerle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When can we use KinectFusion for ground truth acquisition?</style></title><secondary-title><style face="normal" font="default" size="100%">Proc Workshop on \ldots</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://meshlab.sourceforge.net/ http://www.msr-waypoint.net/en-us/um/people/pkohli/papers/mikhrk_iros_dataset_2012.pdf%5Cnpapers3://publication/uuid/2615CF9D-C632-4E39-B1C4-B32A4A5D339C</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">3–8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Abstract—KinectFusion is a method for real-time capture of dense 3D geometry of the physical environment using a depth sensor. The system allows capture of a large dataset of 3D scene reconstructions at very low cost. In this paper we discuss the properties of the ...$\backslash$n</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephan Meister</style></author><author><style face="normal" font="default" size="100%">Izadi, S.</style></author><author><style face="normal" font="default" size="100%">Kohli, P.</style></author><author><style face="normal" font="default" size="100%">Hämmerle, M.</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When Can We Use KinectFusion for Ground Truth Acquisition?</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop on Color-Depth Camera Fusion in Robotics, IEEE International
Conference on Intelligent Robots and Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horvát, E.-Á.</style></author><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">K. A. Zweig</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">You Are Who Knows You: Predicting Links Between Non-Members of Facebook</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Complex Systems. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">309-315</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alexander Horn</style></author></authors></contributors><titles></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">started 15.05.2010</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kimmich, Dominikus</style></author></authors></contributors><titles></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Leila Nagel</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">S. Komori</style></author><author><style face="normal" font="default" size="100%">W. R. McGilles</style></author><author><style face="normal" font="default" size="100%">R. Kurose</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The 2009 SOPRAN active thermography pilot experiment in the Baltic Sea</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hdl.handle.net/2433/156156</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">358--367</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Alles im Fluss --- Optischer Fluss für industrielle Anwendungen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.gitverlag.com/de/print/4/18/issues/2009/3381.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Mirko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis, Modeling and Dynamic Optimization of 3D Time-of-Flight Imaging Systems</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/12297</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">(Kurzfassung) Die vorliegende Arbeit befasst sich mit der Optimierung von 3D-Laufzeitkamerasystemen. Diese neuartigen Kameras erfassen Entfernungsbilder, indem sie die beobachtete Szene aktiv beleuchten und die Laufzeit (Time-of-Flight, ToF) des rückgestreuten Lichtes bestimmen. Dabei werden Tiefenbilder aus mehreren Rohbildern konstruiert, wobei typischerweise zwei dieser Bilder simultan mit Hilfe spezieller korrelierender Sensoren aufgenommen werden. Der wissenschaftliche Beitrag dieser Arbeit setzt sich aus vier Entwicklungen zusammen: Präsentiert wird ein physikalisches Sensor-Modell, welches eine Analyse und Optimierung des Prozesses der Rohbildaufnahme ermöglicht. Hierauf gestützt wird ein auf einer logarithmischen Kennlinie beruhendes ToF Sensor-Design vorgeschlagen. Aufgrund von Asymmetrien der beiden parallelen Auslesestufen des Sensors ist gegenwärtig eine mehrfache Akquisition der Rohbilder notwendig. Dies ermöglicht eine Korrektur systematischer Fehler. Die vorliegende Arbeit präsentiert eine Methode zur dynamischen Kalibrierung und Kompensation dieser Asymmetrien. Sie erlaubt die Erzeugung von zwei Tiefenkarten aus den ursprünglichen Rohdaten (eines Tiefenbildes), und bewirkt so eine Verdopplung der Bildwiederholrate. Da mehrere zu unterschiedlichen Zeiten aufgenommene Rohbilder zu einem einzigen Tiefenbild kombiniert werden, treten bei der Abbildung dynamischer Szenerien Bewegungsartefakte auf. Diese Arbeit stellt eine neue, einfache und robuste Methode zur Detektion und Korrektur solcher Artefakte vor. Die in dieser Arbeit präsentierten Algorithmen besitzen eine Berechnungskomplexität, die auch auf Systemen mit limitierten Ressourcen (z.B.~eingebetteten Systemen) eine Ausführung in Echtzeit erlaubt. Die Algorithmen werden unter Nutzung eines kommerziellen ToF Systems demonstriert. (Abstract) The present thesis is concerned with the optimization of 3D Time-of-Flight (ToF) imaging systems. These novel cameras determine range images by actively illuminating a scene and measuring the time until the backscattered light is detected. Depth maps are constructed from multiple raw images. Usually two of such raw images are acquired simultaneously using special correlating sensors. This thesis covers four main contributions: A physical sensor model is presented which enables the analysis and optimization of the process of raw image acquisition. This model supports the proposal of a new ToF sensor design which employs a logarithmic photo response. Due to asymmetries of the two read-out paths current systems need to acquire the raw images in multiple instances. This allows the correction of systematic errors. The present thesis proposes a method for dynamic calibration and compensation of these asymmetries. It facilitates the computation of two depth maps from a single set of raw images and thus increases the frame rate by a factor of two. Since not all required raw images are captured simultaneously motion artifacts can occur. The present thesis proposes a robust method for detection and correction of such artifacts. All proposed algorithms have a computational complexity which allows real-time execution even on systems with limited resources (e.g.~embedded systems). The algorithms are demonstrated by use of a commercial ToF camera.</style></abstract><notes><style face="normal" font="default" size="100%">started 01.07.2008</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Applying Variable Selection to Illumination-Series Data using the Ilastik Tool</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg Collaboratory for Image Processing, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">David Eisenhauer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufbau eines Messsystems zur Amplitudenmessung von Schwerewellen im Aeolotron</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">started 11.04.2011</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christine Kräuter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufteilung des Transferwiderstands zwischen Luft und Wasser beim Austausch flüchtiger Substanzen mittlerer Löslichkeit zwischen Ozean und Atmosphäre</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/13010</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The main focus of this thesis is the investigation of the air-water exchange of volatile tracers of medium solubility. The transfer resistances of tracers with a broad spectrum of solubilities were measured in experiments at the Aeolotron wind-wave facility. The dependence of transfer resistances on friction velocity and mean square slope is studied for both clean water and water with an added surfactant. It becomes clear that neither friction velocity nor mean square slope alone can be used to describe gas exchange for both cases. In addition Schmidt number scaling for tracers with medium solubility was investigated. Schmidt number scaling is a common method to compute the transfer resistance of a tracer using another one. This requires that the air-side or water-side transfer resistance are negligible. This is not the case for tracers with medium solubility. Here an extended Schmidt number scaling method is tested experimentally for the first time. The air-sided resistance is determined by the Schmidt number scaling with a very well soluble reference-tracer (Methanol, alpha = 5470). Accordingly the water-sided resistance is calculated with a water-sided controlled reference-tracer (N2O, alpha = 0.6). The total resistance is obtained using both parts of the resistance and the partitioning equation of Liss and Slater (1974). The comparison of computed and measured resistances shows good agreement. Finally, a simple function to empirically describe the ratio of air-sided to total resistance in dependence of friction velocity and solubility is presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna Kreshuk</style></author><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Christoph Sommer</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Cantoni, M.</style></author><author><style face="normal" font="default" size="100%">G. W. Knott</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images</style></title><secondary-title><style face="normal" font="default" size="100%">PLoS ONE</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">6 (10)</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Segmentation of Large 3D Images of Nervous Systems Using a Higher-order Graphical Model</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna Kreshuk</style></author><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Christoph Sommer</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">G. W. Knott</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Segmentation of Synapses in 3D EM Data</style></title><secondary-title><style face="normal" font="default" size="100%">Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011).
Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">220-223</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Therese Weißbach</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bestimmung der Transfergeschwindigkeit bei blaseninduziertem Gasaustausch</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monroy, A.</style></author><author><style face="normal" font="default" size="100%">Eigenstetter, A.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Beyond Straight Lines - Object Detection using Curvature</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Image Processing (ICIP)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heiko Heck</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildverarbeitendes Verfahren zur Detektion und Vermessung von Luftblasen an der Wasseroberfläche eines Blasentanks</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lou, X.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Biomedical Data Analysis with Prior Knowledge: Modeling and Learning</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bringing the ocean inside the lab, image processing in environmental sciences</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.laborundmore.de/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">G. W. Knott</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI 2011, Proceedings.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6891</style></volume><pages><style face="normal" font="default" size="100%">653-660</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Erz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Charakterisierung von Laufzeitkamerasystemen für Lumineszenzlebensdauermessungen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/11598</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Sven Wanner</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">S. Komori</style></author><author><style face="normal" font="default" size="100%">W. R. McGilles</style></author><author><style face="normal" font="default" size="100%">R. Kurose</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined visualization of wind waves and water surface temperature</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hdl.handle.net/2433/156156</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">496--506</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christine Kräuter</style></author><author><style face="normal" font="default" size="100%">Kerstin E. Richter</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Evridiki Mesarchaki</style></author><author><style face="normal" font="default" size="100%">Jonathan Williams</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comparative lab study of tansfer velocities of volatile tracers with widely varying solubilities</style></title><secondary-title><style face="normal" font="default" size="100%">DPG Frühjahrstagung Dresden, Fachverband Umweltphysik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/29</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Die Löslichkeit einer flüchtigen Substanz in Wasser hat einen entscheidenden Einfluss auf den Gasaustausch zwischen Ozean und Atmosphäre. Bei Stoffen mit einer sehr hohen Löslichkeit wird der Austausch durch Diffusion in der luftseitigen Grenzschicht kontrolliert und bei solchen mit einer sehr niedrigen Löslichkeit von der wasserseitigen Grenzschicht. Bei vielen umweltrelevanten Stoffen (z.B. Aceton, Acetaldehyd, Acetonitril) ist es aber ein Wechselspiel von beiden Prozessen. Die Kombination der Prozesse ist bisher experimentell nicht untersucht worden und es gibt nur einfache Modelle, welche die Intermittenz der Prozesse berücksichtigen. In einem ersten Laborexperiment am Aeolotron, einem ringförmigen Wind-Wellen-Kanal, wurden die Transferwiderstände vieler Gase mit unterschiedlichen Löslichkeiten bei verschiedenen Windgeschwindigkeiten (1,4 m/s bis 8,4 m/s) bestimmt. Die dimensionslosen Löslichkeiten der verwendeten Gase deckten einen Bereich von 5 Größenordnungen ab. Die Gaskonzentrationen wurden durch FTIR-Spektroskopie (Fourier Transform Infrared Spectroscopy) und mit einem PTR-MS (Proton Transfer Reaction - Mass Spectrometer) gemessen. Die Partitionierung des Transferwiderstandes von Gasen mittlerer Löslichkeit in einen luftseitigen und wasserseitigen Teil konnte nachgewiesen werden.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">F. O. Kaster</style></author><author><style face="normal" font="default" size="100%">M.-A. Weber</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Langs, G.</style></author><author><style face="normal" font="default" size="100%">Criminisi, A.</style></author><author><style face="normal" font="default" size="100%">Tu, Z.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations</style></title><secondary-title><style face="normal" font="default" size="100%">LNCS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer, Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 6533</style></volume><pages><style face="normal" font="default" size="100%">74-85</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Continuous Multiclass Labeling Approaches and Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM J.~Imag.~Sci.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">1049-1096</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Continuous Multiclass Labeling Approaches and Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">CoRR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1102.5448</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">abs/1102.5448</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schlecht, J.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Contour-based Object Detection</style></title><secondary-title><style face="normal" font="default" size="100%">BMVC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1--9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Bagon, Shai</style></author><author><style face="normal" font="default" size="100%">Sharp, Toby</style></author><author><style face="normal" font="default" size="100%">Yao, Bangpeng</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Decision tree fields</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1668–1675</style></pages><isbn><style face="normal" font="default" size="100%">9781457711015</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper introduces a new formulation for discrete image labeling tasks, the Decision Tree Field (DTF), that combines and generalizes random forests and conditional random fields (CRF) which have been widely used in computer vision. In a typical CRF model the unary potentials are derived from sophisticated random forest or boosting based classifiers, however, the pairwise potentials are assumed to (1) have a simple parametric form with a pre-specified and fixed dependence on the image data, and (2) to be defined on the basis of a small and fixed neighborhood. In contrast, in DTF, local interactions between multiple variables are determined by means of decision trees evaluated on the image data, allowing the interactions to be adapted to the image content. This results in powerful graphical models which are able to represent complex label structure. Our key technical contribution is to show that the DTF model can be trained efficiently and jointly using a convex approximate likelihood function, enabling us to learn over a million free model parameters. We show experimentally that for applications which have a rich and complex label structure, our model achieves excellent results. © 2011 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lou, X.</style></author><author><style face="normal" font="default" size="100%">F. O. Kaster</style></author><author><style face="normal" font="default" size="100%">Lindner, M.</style></author><author><style face="normal" font="default" size="100%">Bernhard X. Kausler</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Höckendorf, B.</style></author><author><style face="normal" font="default" size="100%">Wittbrodt, J.</style></author><author><style face="normal" font="default" size="100%">Jänicke, H.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DELTR: Digital Embryo Lineage Tree Reconstructor</style></title><secondary-title><style face="normal" font="default" size="100%">Eighth IEEE International Symposium on Biomedical Imaging (ISBI). Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1557-1560</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Schäfer, H.</style></author><author><style face="normal" font="default" size="100%">Christoph S. 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Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gaussian process classification: singly versus doubly stochastic models, and new computational schemes</style></title><secondary-title><style face="normal" font="default" size="100%">Stochastic Environmental Research &amp; Risk Assessment</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">25 (7)</style></volume><pages><style face="normal" font="default" size="100%">865-879</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nicola, A.</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Popa, C.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A general extending and constraining procedure for linear iterative methods</style></title><secondary-title><style face="normal" font="default" size="100%">Int.~J.~Comp.~Math.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1080/00207160.2011.634002</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">in press</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nicola, A.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Popa, C.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A general extending and constraining procedure for linear iterative methods</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Comp. Math.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1080/00207160.2011.634002</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">in press</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sven Wanner</style></author><author><style face="normal" font="default" size="100%">Fehr, Janis</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">G. Bebis</style></author><author><style face="normal" font="default" size="100%">et al.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Generating EPI representations of 4D light fields with a single lens focused plenoptic camera</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Visual Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">90--101</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">He, Kaiming</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Tang, Xiaoou</style></author><author><style face="normal" font="default" size="100%">Sun, Jian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A global sampling method for alpha matting</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">2049–2056</style></pages><isbn><style face="normal" font="default" size="100%">9781457703942</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Alpha matting refers to the problem of softly extracting the foreground from an image. Given a trimap (specifying known foreground/background and unknown pixels), a straightforward way to compute the alpha value is to sample some known foreground and background colors for each unknown pixel. Existing sampling-based matting methods often collect samples near the unknown pixels only. They fail if good samples cannot be found nearby. In this paper, we propose a global sampling method that uses all samples available in the image. Our global sample set avoids missing good samples. A simple but effective cost function is defined to tackle the ambiguity in the sample selection process. To handle the computational complexity introduced by the large number of samples, we pose the sampling task as a correspondence problem. The correspondence search is efficiently achieved by generalizing a randomized algorithm previously designed for patch matching[3]. A variety of experiments show that our global sampling method produces both visually and quantitatively high-quality matting results. © 2011 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Speth, Markus</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Reinelt, Gerhard</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally Optimal Image Partitioning by Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">EMMCVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg Hendrik Kappes</style></author><author><style face="normal" font="default" size="100%">Markus Speth</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Gerhard Reinelt</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally Optimal Image Partitioning by Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">EMMCVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Speth, M.</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Reinelt, G.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally Optimal Image Partitioning by Multicuts</style></title><secondary-title><style face="normal" font="default" size="100%">EMMCVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">31-44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HCI&#039;s Parabolic Lighting Facility - Design and Usage</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Heidelberg Collaboratory for Image Processing, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Mirko</style></author><author><style face="normal" font="default" size="100%">Klaus Zimmermann</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High frame rate for 3D time-of-flight cameras by dynamic sensor calibration</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings IEEE International Conference on Computational Photography (ICCP)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1--8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">3D Time-of-Flight cameras are able to deliver robust depth maps of dynamic scenes. The frame rate, however, is limited because today&#039;s systems utilizing two-tap sensors need to acquire the required raw images in multiple instances in order to compute one depth map. These multiple raw images allow canceling out systematic errors introduced by asymmetries in the two taps, which otherwise would distort the reconstructed depth map. This work presents a method to implicitly calibrate these asymmetries of multi-tap 3D Time-of-Flight sensors. The calibration data are gathered from arbitrary live acquisitions possibly in real-time. The proposed correction of raw data supersedes the commonly used averaging technique. Thus it is possible to compute multiple depth maps from a single set of raw images. This increases the frame rate by at least a factor of two. The method is verified using real camera data and is evaluated quantitatively.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nair, R.</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High Precision TOF-guided Depth from Stereo for Room Scanning</style></title><secondary-title><style face="normal" font="default" size="100%">CVMP, Proceedings.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Felix Friedl</style></author><author><style face="normal" font="default" size="100%">Alexandra G. Herzog</style></author><author><style face="normal" font="default" size="100%">Pius Warken</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hochauflösende raumzeitliche Messung von flüssigkeitsseitigen Konzentrationsfeldern an der wind- und wellenbewegten Wasseroberfläche</style></title><secondary-title><style face="normal" font="default" size="100%">DPG Frühjahrstagung Dresden</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/5/contribution/3</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Konzentrationsprofile in der 20 bis 200 µm dicken Massengrenzschicht innerhalb der wasserseitigen viskosen Grenzschicht an einer wind- und wellenbewegten, freien Wasseroberfläche konnten bisher nicht gemessen werden. Alle bisherigen Messungen wurden an ebenen Wasseroberflächen in Tanks mit bodeninduzierter Turbulenz durchgeführt. Die erstmalige Messung vertikaler Konzentrationsprofile mit einer Auflösung von 11.4 µm und einer Bildfrequenz von 973 Hz ist durch Verwendung der Laser induzierten Phosphoreszenz gelungen. Der dabei verwendete Farbstoff ist ein neuer, lichtempfindlicher Ruthenium Komplex, dessen Quenchkonstante den 17-fachen Wert im Vergleich zu der bisher benutzen Pyrenbuttersäure (PBA) besitzt. Zur Anregung der Phosphoreszenz wird ein auf unter 150 µm fokussierter Laser mit einer Wellenlänge von 445nm verwendet. Der Scheimpflugaufbau mit schrägen Kanalwänden des linearen Wind-Wellen-Kanals ermöglicht die Beobachtung wasserseitiger Konzentrationsprofile auch bei wellenbewegter Wasseroberfläche. Im nächsten Schritt werden zeitaufgelöste, zweidimensionale Konzentrationsfelder quer zur Windrichtung aufgenommen. Erste Tests zeigen vielversprechende Ergebnisse.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Sommer</style></author><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ilastik: Interactive Learning and Segmentation Toolkit</style></title><secondary-title><style face="normal" font="default" size="100%">Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011).Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">230-233</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">F. O. Kaster</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schleicher, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image Processing for Super-Resolution Localization Microscopy Utilizing an FPGA Accelerator</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Töppe, Eno</style></author><author><style face="normal" font="default" size="100%">Oswald, Martin R</style></author><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image-based 3D modeling via cheeger sets</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><number><style face="normal" font="default" size="100%">PART 1</style></number><volume><style face="normal" font="default" size="100%">6492 LNCS</style></volume><pages><style face="normal" font="default" size="100%">53–64</style></pages><isbn><style face="normal" font="default" size="100%">9783642193149</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a novel variational formulation for generating 3D models of objects from a single view. Based on a few user scribbles in an image, the algorithm automatically extracts the object silhouette and subsequently determines a 3D volume by minimizing the weighted surface area for a fixed user-specified volume. The respective energy can be efficiently minimized by means of convex relaxation techniques, leading to visually pleasing smooth surfaces within a matter of seconds. In contrast to existing techniques for single-view reconstruction, the proposed method is based on an implicit surface representation and a transparent optimality criterion, assuring high-quality 3D models of arbitrary topology with a minimum of user input. © 2011 Springer-Verlag Berlin Heidelberg.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mario Frank</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image-Based Supervision of a Periodically Working Machine</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Analysis and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1-10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schapowalow, Alexander</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Implementierung der &quot;Focus-Sweep&quot; Technik mit Hilfe von Scheimpflugoptik
und TDI-Technik</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schapowalow, Alexander</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Implementierung der Focus-Sweep Technik mit Hilfe von Scheimpflugoptik und TDI-Technik</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Fakultät für Mathematik und Informatik, Ruprecht-Karls-Universität Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">S. Komori</style></author><author><style face="normal" font="default" size="100%">W. R. McGilles</style></author><author><style face="normal" font="default" size="100%">R. Kurose</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Improved optical instrument for the measurement of water wave statistics in the field</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hdl.handle.net/2433/156156</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">524--534</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg Hendrik Kappes</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inference on Highly-Connected Discrete Graphical Models with Applications to Visual Object Recognition</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/11872/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Ruprecht-Karls-Universität Heidelberg, Faculty of Mathematics and Computer Sciences</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phddoctoral thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interactive foreground extraction using graph cut</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Markov \ldots</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/pubs/147408/rotheretalmrfbook-grabcut.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1–20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Note, this is an extended version of chapter 7 from the book: Markov Random Fields for Vision and Image Processing, MIT Press [6]. In this Technical Report, references to other chapters are with respect to the book. The differences are, a new section 4.3 and extra details in section 3.2 and 3.3 The topic of interactive image segmentation has received considerable attention in the computer vision community in the last decade. Today, this topic is very mature and commercial products exist which feature advanced research solutions. This means that interactive image segmentation is today probably one of the most used computer vision technologies worldwide. In this chapter we review one class of interactive segmen-tation techniques, which use discrete optimization and a regional selection interface. We begin the chapter by explaining the seminal work of Boykov and Jolly [9]. After that the GrabCut technique [36] is introduced, which improves on [9]. GrabCut is the underlying algorithm for the Background Removal tool in the Microsoft Office 2010 product. In the third part of the chapter many interesting features and details are explained which are part of the product. In this process several recent research articles are reviewed. Finally, the Background Removal tool, as well as [9, 36], are evaluated in different ways on publicly available databases. This includes static and dynamic user inputs. 1</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph N. Straehle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interactive Segmentation of Neural Electron Microscopy Data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnieders, Jana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigation of Momentum Transfer across the Air-Sea Interface by Means of Active and Passive Thermography</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerstin E. Richter</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">S. Komori</style></author><author><style face="normal" font="default" size="100%">W. R. McGilles</style></author><author><style face="normal" font="default" size="100%">R. Kurose</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A laboratory study of the Schmidt number dependency of air-water gas transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hdl.handle.net/2433/156156</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">322--332</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Leila Nagel</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Lock-in thermography at the ocean surface: a local and fast method to investigate heat and gas exchange between ocean and atmosphere</style></title><secondary-title><style face="normal" font="default" size="100%">DPG Frühjahrstagung Dresden, Fachverband Umweltphysik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/28</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Heat is used as a proxy tracer for gases to study the transport processes across the sea-surface interface to obtain a detailed insight into the diffusive and turbulent processes controlling the transport. A carbon dioxide laser forces a periodically varying heat flux density onto the water surface and the amplitude damping and phase shift of the sea surface temperature is measured from infrared image sequences. The transport process can be treated by linear system theory and the relation between the input signal (periodically varying surface flux density) and the output (surface temperature) is estimated. Within the framework of the SOPRAN initiative three field experiments in the Baltic Sea were conducted. The locally derived heat transfer rates are scaled to gas transfer rates, which are in good agreement with empirical gas transfer wind speed relationships for moderate winds speeds. At high wind speed, the transfer rates are lower, which is explained by the fact that heat transport is insensitive to bubble-mediated gas transfer, i.e. it measures only a part of the transfer process directly at the water surface. Together with eddy covariance measurements a significant improvement of the parameterization of heat and gas transfer velocities can be expected.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lindner, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Machine Learning Approach to Improve Digital Embryo Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Julia Schaper</style></author><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mean square slope measurements in the field with the reflective stereo slope gauge</style></title><secondary-title><style face="normal" font="default" size="100%">EGU General Assembly, Vienna</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">An optical instrument for the measurement of surface ocean small-scale wave statistics has been developed. This reflective stereo slope gauge (RSSG) is capable of simultaneous measurements of height and slope statistics of the water surface in the field. The instrument is a significant technical improvement of the early work by Waas and Jähne (1992) and comprises a stereo camera setup, artificial light sources and a wind following system. The measurement principle is similar to Cox &amp; Munk&#039;s derivation of slope statistics from photographs of Sun glitter (Cox and Munk, 1954). However, the RSSG uses artificial light sources instead of relying on natural illumination and can thus be used independent of daytime or cloud cover. The probability distribution of the occurrence of specular reflections at given image coordinates can be related to the probability distribution of the surface slope, if the position of the instrument relative to the water surface is known. The slope probability distribution is measured for small slopes up to 0.15. From this partial probability distribution, estimates of the mean square slope (mss) and other statistical parameters can be extracted. The distance from of the instrument to the water surface is obtained from stereo triangulation, while an inclination sensor measures its tilt. Stereo triangulation at the specular reflecting water surface requires the use of two light sources in complementary positions to ensure that both cameras detect reflections coming from the same surface patches. Furthermore, to guarantee that each camera can only sees the corresponding light source, the stereo images are acquired sequentially and the LED light sources are pulsed. The water surface does not change significantly in between the image acquisitions, since the exposure time (and thus the minimum delay of the second image acquisition) is limited to 0.2 ms. The instrument can measure the along-wind and cross-wind mean square slope components, even under varying wind conditions. A wind following system was implemented that is able to rotate the stereo base to keep it aligned in an along-wind or cross-wind direction. Even though the instrument cannot record slope time series and only makes statistic measurements, it has significant advantages over other techniques that are commonly used. Measurements are non-invasive (no instrument parts suspended into or submersed in water) and mostly independent of natural illumination (light source peak wavelength is 940 nm, IR filters suppress skylight, only direct sun glitter may cause complications), not influenced by upwelling light (strong absorption of light at 940 nm by water) and have a spatial resolution that allows the measurement of slope statistics also for capillary waves. At the same time, the (gravity) wave amplitudes can be inferred from the stereo information. The RSSG was characterized and tested in the laboratory and deployed to the Baltic Sea in July and September 2010 to perform local wave statistics measurements at the footprint of heat exchange experiments with the active controlled flux technique (Schimpf et al., 2010). First results from these experiments that demonstrate the capability of the RSSG to measure wave slope statistics in a variety of conditions are presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author><author><style face="normal" font="default" size="100%">Julia Schaper</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurement of ocean wave statistics with the reflective stereo slope gauge</style></title><secondary-title><style face="normal" font="default" size="100%">DPG Frühjahrstagung Dresden, Fachverband Umweltphysik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/30</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">An optical instrument for the measurement of surface ocean small-scale wave statistics has been developed. This reflective stereo slope gauge (RSSG) is a significant technical improvement of the early work by Waas and Jähne (1992) and capable of simultaneous measurements of height and slope statistics of the water surface in the field. It comprises a stereo camera setup to measure wave heights by stereo triangulation. The slope measurement is based on Cox &amp; Munk&#039;s derivation of slope statistics from photographs of sun glitter (1954) but uses artificial light sources to be independent of natural illumination. The probability distribution of the occurrence of specular reflections in the images can be related to the probability distribution of the surface slope. Although the instrument only makes statistical measurements, it has significant advantages over other common techniques. Measurements are non-invasive (no instrument parts suspended into or submersed in water) and mostly independent of natural illumination (IR light source with</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maximilian Bopp</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung der Schubspannungsgeschwindigkeit am Heidelberger Aeolotron mittels der Impulsbilanzmethode</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Breitenreicher, Dirk</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data</style></title><secondary-title><style face="normal" font="default" size="100%">Int.~J.~Comp.~Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/v266873267180602/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">92</style></volume><pages><style face="normal" font="default" size="100%">32--52</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Breitenreicher, Dirk</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model-Based Multiple Rigid Object Detection and Registration in Unstructured Range Data</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Comp. Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/v266873267180602/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">92</style></volume><pages><style face="normal" font="default" size="100%">32–52</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vicente, Sara</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Object cosegmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">2217–2224</style></pages><isbn><style face="normal" font="default" size="100%">9781457703942</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Cosegmentation is typically defined as the task of jointly segmenting something similar in a given set of images. Existing methods are too generic and so far have not demonstrated competitive results for any specific task. In this paper we overcome this limitation by adding two new aspects to cosegmentation: (1) the &quot;something&quot; has to be an object, and (2) the &quot;similarity&quot; measure is learned. In this way, we are able to achieve excellent results on the recently introduced iCoseg dataset, which contains small sets of images of either the same object instance or similar objects of the same class. The challenge of this dataset lies in the extreme changes in viewpoint, lighting, and object deformations within each set. We are able to considerably outperform several competitors. To achieve this performance, we borrow recent ideas from object recognition: the use of powerful features extracted from a pool of candidate object-like segmentations. We believe that our work will be beneficial to several application areas, such as image retrieval. © 2011 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bleyer, Michael</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Scharstein, Daniel</style></author><author><style face="normal" font="default" size="100%">Sinha, Sudipta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Object stereo Joint stereo matching and object segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">3081–3088</style></pages><isbn><style face="normal" font="default" size="100%">9781457703942</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a method for joint stereo matching and object segmentation. In our approach a 3D scene is represented as a collection of visually distinct and spatially coherent objects. Each object is characterized by three different aspects: a color model, a 3D plane that approximates the object&#039;s disparity distribution, and a novel 3D connectivity property. Inspired by Markov Random Field models of image segmentation, we employ object-level color models as a soft constraint, which can aid depth estimation in powerful ways. In particular, our method is able to recover the depth of regions that are fully occluded in one input view, which to our knowledge is new for stereo matching. Our model is formulated as an energy function that is optimized via fusion moves. We show high-quality disparity and object segmentation results on challenging image pairs as well as standard benchmarks. We believe our work not only demonstrates a novel synergy between the areas of image segmentation and stereo matching, but may also inspire new work in the domain of automatic and interactive object-level scene manipulation. © 2011 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">F. O. Kaster</style></author><author><style face="normal" font="default" size="100%">Merkel, B.</style></author><author><style face="normal" font="default" size="100%">Nix, O.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Science - Research and Development</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">26</style></volume><pages><style face="normal" font="default" size="100%">65-85</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Splitthoff, N.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On oblique random forests</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2011. Proceedings.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">453-469</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS 6912</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Erz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimale Kameraauswahl für maschinelles Sehen durch standardisierte Charakterisierung</style></title><secondary-title><style face="normal" font="default" size="100%">tm --- Technisches Messen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">377--383</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimality Bounds for a Variational Relaxation of the Image Partitioning
Problem</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Min. Meth. Comp. Vis. Patt. Recogn.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">132-146</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Boykov, Y.</style></author><author><style face="normal" font="default" size="100%">Kahl, F.</style></author><author><style face="normal" font="default" size="100%">Schmidt, F. R.</style></author><author><style face="normal" font="default" size="100%">Lempitsky, V. F.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Min. Meth. Comp. Vis. Patt. Recogn.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6819</style></volume><pages><style face="normal" font="default" size="100%">132--146</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Lenzen, Frank</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Boykov, Y.</style></author><author><style face="normal" font="default" size="100%">Kahl, F.</style></author><author><style face="normal" font="default" size="100%">Lempitsky, V. F.</style></author><author><style face="normal" font="default" size="100%">Schmidt, F. R.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Min. Meth. Comp. Vis. Patt. Recogn.</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6819</style></volume><pages><style face="normal" font="default" size="100%">132–146</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Lenzen, Frank</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Dec</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1112.0974</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IPA group, Heidelberg University</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, Fabian</style></author><author><style face="normal" font="default" size="100%">Schmidt, Stefan</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fichtinger, Gabor</style></author><author><style face="normal" font="default" size="100%">Martel, Anne L.</style></author><author><style face="normal" font="default" size="100%">Peters, Terry M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6893</style></volume><pages><style face="normal" font="default" size="100%">370--377</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, Fabian</style></author><author><style face="normal" font="default" size="100%">Schmidt, Stefan</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6893</style></volume><pages><style face="normal" font="default" size="100%">370–377</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fabian Rathke</style></author><author><style face="normal" font="default" size="100%">Stefan Schmidt</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fichtinger, Gabor</style></author><author><style face="normal" font="default" size="100%">Martel, Anne L.</style></author><author><style face="normal" font="default" size="100%">Peters, Terry M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6893</style></volume><pages><style face="normal" font="default" size="100%">370–377</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, F.</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Order Preserving and Shape Prior Constrained Intra-Retinal Layer
Segmentation in Optical Coherence Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">MICCAI 2011, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6893</style></volume><pages><style face="normal" font="default" size="100%">370-377</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monroy, A.</style></author><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Arnold, M.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parametric Object Detection for Iconographic Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific Computing &amp; Cultural Heritage</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.academia.edu/9439693/Parametric_Object_Detection_for_Iconographic_Analysis</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bleyer, Michael</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PatchMatch Stereo - Stereo Matching with Slanted Support Windows</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">14.1–14.11</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Common local stereo methods match support windows at integer-valued disparities. The implicit assumption that pixels within the support region have constant disparity does not hold for slanted surfaces and leads to a bias towards reconstructing frontoparallel surfaces. This work overcomes this bias by estimating an individual 3D plane at each pixel onto which the support region is projected. The major challenge of this approach is to find a pixels optimal 3D plane among all possible planes whose number is infinite. We show that an ideal algorithm to solve this problem is PatchMatch 1 that we extend to find an approximate nearest neighbor according to a plane. In addition to Patch- Matchs spatial propagation scheme, we propose (1) view propagation where planes are propagated among left and right views of the stereo pair and (2) temporal propagation where planes are propagated from preceding and consecutive frames of a video when doing temporal stereo. Adaptive support weights are used in matching cost aggregation to improve results at disparity borders. We also show that our slanted support windows can be used to compute a cost volume for global stereo methods, which allows for explicit treatment of occlusions and can handle large untextured regions. In the results we demonstrate that our method reconstructs highly slanted surfaces and achieves impressive disparity details with sub-pixel precision. In the Middlebury table, our method is currently top-performer among local methods and takes rank 2 among approximately 110 competitors if sub-pixel precision is considered.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Image Segmentation with Closedness Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of ICCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Image Segmentation with Closedness Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of ICCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Thorsten Beier</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic Image Segmentation with Closedness Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">ICCV, Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">2611 - 2618</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pletscher, Patrick</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Putting MAP back on the map</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">6835 LNCS</style></volume><pages><style face="normal" font="default" size="100%">111–121</style></pages><isbn><style face="normal" font="default" size="100%">9783642231223</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. © 2011 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pletscher, Patrick</style></author><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Putting MAP back on the map</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">6835 LNCS</style></volume><pages><style face="normal" font="default" size="100%">111–121</style></pages><isbn><style face="normal" font="default" size="100%">9783642231223</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important problem of learning the full form of the potential functions of pairwise CRFs. We examine two popular learning techniques, maximum likelihood estimation and maximum margin training. The main focus of the paper is on models such as pairwise CRFs, that are simplistic (misspecified) and do not fit the data well. We empirically demonstrate that for misspecified models maximum-margin training with MAP prediction is superior to maximum likelihood estimation with any other prediction method. Additionally we examine the common belief that MLE is better at producing predictions matching image statistics. © 2011 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Monroy, A.</style></author><author><style face="normal" font="default" size="100%">Bernd Carque</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reconstructing the Drawing Process of Reproductions from Medieval Images</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Conference on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://hciweb.iwr.uni-heidelberg.de/compvis/research/manesse/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">2974--2977</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gehler, Peter Vincent</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kiefel, Martin</style></author><author><style face="normal" font="default" size="100%">Zhang, Lumin</style></author><author><style face="normal" font="default" size="100%">Schölkopf, Bernhard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Recovering intrinsic images with a global sparsity prior on reflectance</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><isbn><style face="normal" font="default" size="100%">9781618395993</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We address the challenging task of decoupling material properties from lighting properties given a single image. In the last two decades virtually all works have concentrated on exploiting edge information to address this problem. We take a different route by introducing a new prior on reflectance, that models reflectance values as being drawn from a sparse set of basis colors. This results in a Random Field model with global, latent variables (basis colors) and pixel-accurate output reflectance values. We show that without edge information high-quality results can be achieved, that are on par with methods exploiting this source of information. Finally, we are able to improve on state-of-the-art results by integrating edge information into our model. We believe that our new approach is an excellent starting point for future developments in this field.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Regularizers for Vector-Valued Data and Labeling Problems in Image Processing</style></title><secondary-title><style face="normal" font="default" size="100%">Control Systems and Computers</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">43–54</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author><author><style face="normal" font="default" size="100%">Björn Voss</style></author><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Lindner, M.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">27 (7)</style></volume><pages><style face="normal" font="default" size="100%">987-993</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sparse Higher Order Functions of Discrete Variables–-Representation and Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">research.microsoft.com</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/pubs/147370/RotherKohli-SparseHigherOrder.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">45</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Higher order energy functions have the ability to encode high level structural dependencies between pix-els, which have been shown to be extremely powerful for image labeling problems. Their use, however , is severely hampered in practice by the intractable complexity of representing and minimizing such functions. We observed that higher order functions encountered in computer vision are very often &quot;sparse&quot;, i.e. many labelings of a higher order clique are equally unlikely and hence have the same high cost. In this paper, we address the problem of minimizing such sparse higher order energy functions. Our method works by transforming the problem into an equivalent quadratic function minimization problem. The resulting quadratic function can be minimized using popular message passing or graph cut based algorithms for MAP inference. Although this is primarily a theoretical paper, we also show how labeling problems such as texture denoising and inpainting can be formulated using sparse higher order energy functions. We demonstrate experimentally that for some challenging tasks our formulation is able to out-perform various state-of-the art techniques, especially the well-known patch-based approach of Freeman et al. [11]. Given the broad use of patch-based models in computer vision, we believe that our contributions will be applicable in many problem domains.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Breitenreicher, Dirk</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sparse Template-Based Variational Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Adaptive Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">149-166</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Breitenreicher, Dirk</style></author><author><style face="normal" font="default" size="100%">Lellmann, Jan</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sparse Template-Based Variational Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Adaptive Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">149-166</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lou, X.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Shawe-Taylor, J.</style></author><author><style face="normal" font="default" size="100%">Zemel, R.S.</style></author><author><style face="normal" font="default" size="100%">Pereira, F.C.N.</style></author><author><style face="normal" font="default" size="100%">Weinberger, K.Q.</style></author><author><style face="normal" font="default" size="100%">Bartlett, P.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Structured Learning for Cell Tracking</style></title><secondary-title><style face="normal" font="default" size="100%">NIPS 2011. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1296-1304</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study of Nesterov&#039;s Scheme for Lagrangian Decomposition and MAP
Labeling</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Computer Vision and Pattern Recognition
(CVPR), accepted as oral presentation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1817 - 1823</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study of Nesterov&#039;s Scheme for Lagrangian Decomposition and MAP Labeling</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Savchynskyy, B.</style></author><author><style face="normal" font="default" size="100%">Kappes, J. H.</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study of Nesterov&#039;s Scheme for Lagrangian Decomposition and MAP Labeling</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nowozin, Sebastian</style></author><author><style face="normal" font="default" size="100%">Sharp, Toby</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Supplementary Material : Decision Tree Fields</style></title><secondary-title><style face="normal" font="default" size="100%">Iccv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yarlagadda, P.</style></author><author><style face="normal" font="default" size="100%">Monroy, A.</style></author><author><style face="normal" font="default" size="100%">Bernd Carque</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections</style></title><secondary-title><style face="normal" font="default" size="100%">Conference on Computer Vision and Image Analysis of Art II</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">7869</style></volume><pages><style face="normal" font="default" size="100%">61--69</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">IS&amp;T</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lena Maier-Hein</style></author><author><style face="normal" font="default" size="100%">A. M. Franz</style></author><author><style face="normal" font="default" size="100%">M. Fangerau</style></author><author><style face="normal" font="default" size="100%">Schmidt, Mirko</style></author><author><style face="normal" font="default" size="100%">Alexander Seitel</style></author><author><style face="normal" font="default" size="100%">Sven Mersmann</style></author><author><style face="normal" font="default" size="100%">T. Kilgus</style></author><author><style face="normal" font="default" size="100%">A. Groch</style></author><author><style face="normal" font="default" size="100%">K. Yung</style></author><author><style face="normal" font="default" size="100%">Thiago R. dos Santos</style></author><author><style face="normal" font="default" size="100%">Hans-Peter Meinzer</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">H. Handels</style></author><author><style face="normal" font="default" size="100%">J. Ehrhardt</style></author><author><style face="normal" font="default" size="100%">Hans-Peter Meinzer</style></author><author><style face="normal" font="default" size="100%">T. Tolxdorff</style></author><author><style face="normal" font="default" size="100%">T. M. Deserno</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards mobile augmented reality for on-patient visualization of medical images</style></title><secondary-title><style face="normal" font="default" size="100%">Bildverarbeitung für die Medizin (2011)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">389--393</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Despite considerable technical and algorithmic developments related to the fields of medical image acquisition and processing in the past decade, the devices used for visualization of medical images have undergone rather minor changes. As anatomical information is typically shown on monitors provided by a radiological work station, the physician has to mentally transfer internal structures shown on the screen to the patient. In this work, we present a new approach to on-patient visualization of 3D medical images, which combines the concept of augmented reality (AR) with an intuitive interaction scheme. The method requires mounting a Time-of-Flight (ToF) camera to a portable display (e.g., a tablet PC). During the visualization process, the pose of the camera and thus the viewing direction of the user is continuously determined with a surface matching algorithm. By moving the device along the body of the patient, the physician gets the impression of being able to look directly into the human body. The concept can be used for intervention planning, anatomy teaching and various other applications that require intuitive visualization of 3D data.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">F. O. Kaster</style></author><author><style face="normal" font="default" size="100%">Henning, A.</style></author><author><style face="normal" font="default" size="100%">M.-A. Weber</style></author><author><style face="normal" font="default" size="100%">Bachert, P.</style></author><author><style face="normal" font="default" size="100%">Bösinger, P.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using Spatial Prior Knowledge in the Spectral Fitting of Magnetic Resonance Spectroscopic Images</style></title><secondary-title><style face="normal" font="default" size="100%">NMR in Biomedicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">25(1)</style></volume><pages><style face="normal" font="default" size="100%">1-13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, B.</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Schröder, A.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Adaptive Correlation Method for Flow Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">21, 6</style></volume><pages><style face="normal" font="default" size="100%">3053 - 3065</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Image Denoising with Adaptive Constraint Sets</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 3nd International Conference on Scale Space and
Variational Methods in Computer Vision 2011, in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6667</style></volume><pages><style face="normal" font="default" size="100%">206-217</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Metaxas, D. N.</style></author><author><style face="normal" font="default" size="100%">Quan, L.</style></author><author><style face="normal" font="default" size="100%">Van Gool, L. J.</style></author><author><style face="normal" font="default" size="100%">Sanfeliu, A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Recursive Joint Estimation of Dense Scene Structure and
Camera Motion from Monocular High Speed Traffic Sequences</style></title><secondary-title><style face="normal" font="default" size="100%">2011 IEEE International Conference on Computer Vision ICCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1692-1699</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences</style></title><secondary-title><style face="normal" font="default" size="100%">2011 IEEE International Conference on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1692 -- 1699</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Lenzen, Frank</style></author><author><style face="normal" font="default" size="100%">Kappes, Jörg H.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences</style></title><secondary-title><style face="normal" font="default" size="100%">2011 IEEE International Conference on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><pages><style face="normal" font="default" size="100%">1692 – 1699</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolfgang Mischler</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Weissbach, T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Vereinfachung der Massenbilanz im Blasentank für luftseitige Messung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Antic, B.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Video Parsing for Abnormality Detection</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">2415--2422</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jakob Kunz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualisierung der wasserseitigen Massengrenzschicht beim konvektionsgetriebenen Gasaustausch mithilfe einer Lumineszenzmethode und Thermografie</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pfannmöller, M.</style></author><author><style face="normal" font="default" size="100%">Flügge, H.</style></author><author><style face="normal" font="default" size="100%">Benner, G.</style></author><author><style face="normal" font="default" size="100%">Wacker, I.</style></author><author><style face="normal" font="default" size="100%">Christoph Sommer</style></author><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author><author><style face="normal" font="default" size="100%">Schmale, S.</style></author><author><style face="normal" font="default" size="100%">Schmidt, H.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Rabe, T.</style></author><author><style face="normal" font="default" size="100%">Kowalsky, W.</style></author><author><style face="normal" font="default" size="100%">Schröder, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualizing a homogeneous blend in bulk heterojunction polymer solar cells by analytical electron microscopy</style></title><secondary-title><style face="normal" font="default" size="100%">Nano Letters</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">3099-3107</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Julia Schaper</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Wave Height Estimation with Stereo Images of the Reflective Stereo Slope Gauge (RSSG)</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tanja Platt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weiterentwicklung einer hochauflösenden LIF-Methode zur Messung von Sauerstoffkonzentrationsprofilen in der wasserseitigen Grenzschicht</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">started 11.04.2011</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Haug</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">F. Puente Leon</style></author><author><style face="normal" font="default" size="100%">M. Heizmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">6 DoF appearance-based object localization with local covariant features</style></title><secondary-title><style face="normal" font="default" size="100%">Forum Bildverarbeitung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://digbib.ubka.uni-karlsruhe.de/volltexte/1000020266</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">KIT Scientific Publishing</style></publisher><pages><style face="normal" font="default" size="100%">13--24</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lena Maier-Hein</style></author><author><style face="normal" font="default" size="100%">Schmidt, Mirko</style></author><author><style face="normal" font="default" size="100%">A. M. Franz</style></author><author><style face="normal" font="default" size="100%">Thiago R. dos Santos</style></author><author><style face="normal" font="default" size="100%">Alexander Seitel</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">J. M. Fitzpatrick</style></author><author><style face="normal" font="default" size="100%">Hans-Peter Meinzer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accounting for an­iso­tro­pic noise in fine registration of time-of-flight range data with high-resolution surface data</style></title><secondary-title><style face="normal" font="default" size="100%">Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6361</style></volume><pages><style face="normal" font="default" size="100%">251--258</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Time-of-Flight (ToF) sensors have become a considerable alternative to conventional surface acquisition techniques such as laser range scanning and stereo vision. Application of ToF cameras for the purpose of intra-operative registration requires matching of the noisy surfaces generated from ToF range data onto pre-interventionally acquired high-resolution surfaces. The contribution of this paper is two-fold: Firstly, we present a novel method for fine rigid registration of noisy ToF data with high-resolution surface meshes taking into account both, the noise characteristics of ToF cameras and the resolution of the target mesh. Secondly, we introduce an evaluation framework for assessing the performance of ToF registration methods based on physically realistic ToF range data generated from a virtual scence. According to experiments within the presented evaluation framework, the proposed method outperforms the standard ICP algorithm with respect to correspondence search and transformation computation, leading to a decrease in the target registration error (TRE) of more than 70%.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hüsken, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Active and Online Learning for Interactive Image Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Haug</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ansichtsbasierte 6 DoF Objekterkennung mit lokalen kovarianten Regionen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Anna Kreshuk</style></author><author><style face="normal" font="default" size="100%">Stankiewicz, M.</style></author><author><style face="normal" font="default" size="100%">Lou, X.</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Mayer, M. P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated detection and analysis of bimodal isotope peak distribution in H/D exchange mass spectrometry using HeXicon</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Mass Spectrometry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">302</style></volume><pages><style face="normal" font="default" size="100%">125-131</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Scheelen, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Quality Control in the Life Sciences</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Julian Stapf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bestimmung der dynamischen Oberflächenspannung mit Hilfe der Blasendruckmethode</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/17777</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Hörnlein</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Boosted Feature Generation for Classification Problems Involving High Numbers of Inputs and Classes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Classification problems involving high numbers of inputs and classes play an important role in the field of machine learning. Image classification, in particular, is a very active field of research with numerous applications. In addition to their high number, inputs of image classification problems often show significant correlation. Also, in proportion to the number of inputs, the number of available training samples is usually low. Therefore techniques combining low susceptibility to overfitting with good classification performance have to be found. Since for many tasks data has to be processed in real time, computational efficiency is crucial as well. Boosting is a machine learning technique, which is used successfully in a number of application areas, in particular in the field of machine vision. Due to it&#039;s modular design and flexibility, Boosting can be adapted to new problems easily. In addition, techniques for optimizing classifiers produced by Boosting with respect to computational efficiency exist. Boosting builds linear ensembles of base classifiers in a stage-wise fashion. Sample-weights reflect whether training samples are hard-to-classify or not. Therefore Boosting is able to adapt to the given classification problem over the course of training. The present work deals with the design of techniques for adapting Boosting to problems involving high numbers of inputs and classes. In the first part, application of Boosting to multi-class problems is analyzed. After giving an overview of existing approaches, a new formulation for base-classifiers solving multi-class problems by splitting them into pair-wise binary subproblems is presented. Experimental evaluation shows the good performance and computational efficiency of the proposed technique compared to state-of-the-art techniques. In the second part of the work, techniques that use Boosting for feature generation are presented. These techniques use the distribution of sample weights, produced by Boosting, to learn features that are adapted to the problems solved in each Boosting stage. By using smoothing-spline base classifiers, gradient descent schemes can be incorporated to find features that minimize the cost function of the current base classifier. Experimental evaluation shows, that Boosting with linear projective features significantly outperforms state-of-the-art approaches like e.g. SVM and Random Forests. In order to be applicable to image classification problems, the presented feature generation scheme is extended to produce shift-invariant features. The utilized features are inspired by the features used in Convolutional Neural Networks and perform a combination of convolution and subsampling. Experimental evaluation for classification of handwritten digits and car side-views shows that the proposed system is competitive to the best published results. The presented scheme has the advantages of being very simple and involving a low number of design parameters only.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Calibration of the 2010-CISG Setup at the Aeolotron</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Institute of Environmental Physics, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational Methods for the Analysis of Mass Spectrometry Images</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Pappin, D. J.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Judith A. J. Steen</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">26 (1)</style></volume><pages><style face="normal" font="default" size="100%">77-83</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nair, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Construction and analysis of random tree ensembles</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Continuous Multiclass Labeling Approaches and Algorithms</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Feb.</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/10460/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Univ. of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Tech. Rep.</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vicente, Sara</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cosegmentation revisited: Models and optimization</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">PART 2</style></number><volume><style face="normal" font="default" size="100%">6312 LNCS</style></volume><pages><style face="normal" font="default" size="100%">465–479</style></pages><isbn><style face="normal" font="default" size="100%">3642155510</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The problem of cosegmentation consists of segmenting the same object (or objects of the same class) in two or more distinct images. Recently a number of different models have been proposed for this problem. However, no comparison of such models and corresponding optimization techniques has been done so far. We analyze three existing models: the L1 norm model of Rother et al. [1], the L2 norm model of Mukherjee et al. [2] and the &quot;reward&quot; model of Hochbaum and Singh [3]. We also study a new model, which is a straightforward extension of the Boykov-Jolly model for single image segmentation [4]. In terms of optimization, we use a Dual Decomposition (DD) technique in addition to optimization methods in [1,2]. Experiments show a significant improvement of DD over published methods. Our main conclusion, however, is that the new model is the best overall because it: (i) has fewest parameters; (ii) is most robust in practice, and (iii) can be optimized well with an efficient EM-style procedure. © 2010 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lou, X.</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Graf, C.</style></author><author><style face="normal" font="default" size="100%">Lee, C.</style></author><author><style face="normal" font="default" size="100%">Judith A. J. Steen</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author><author><style face="normal" font="default" size="100%">Mayer, M. P.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments</style></title><secondary-title><style face="normal" font="default" size="100%">Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">26(12)</style></volume><pages><style face="normal" font="default" size="100%">1535-1541</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of a Reflective Stereo Slope Gauge for the Measurement of Ocean Surface Wave Slope Statistics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/12673/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wagner, J.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficiently Clustering Earth Mover&#039;s Distance</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedins of the Aian Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">477--488</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Empirical Comparison of Inference Algorithms for Graphical Models
with Higher Order Factors Using OpenGM</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc.~32th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">6376</style></number><pages><style face="normal" font="default" size="100%">353-362</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc.~32th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EMVA 1288 standard for machine vision -- objective specification of vital camera data</style></title><secondary-title><style face="normal" font="default" size="100%">Optik &amp; Photonik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">53--54</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolfgang Mischler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung eines Experiments zur Messung von Blasendichten und blaseninduziertem Gasaustausch</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jonas Gliß</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung und Implementierung eines Cavity-Enhanced-Spektrometers am Wind-Wellen-Kanal</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Im Rahmen dieser Arbeit wurde ein resonatorverstärktes Spektrometer (englisch: Cavity- Enhanced Spectrometer, hier: CE-Spektrometer) zur Messung der Gasaustauschgeschwindigkeit UV-aktiver Substanzen aufgebaut und im großen Heidelberger Wind-Wellen-Kanal zum Einsatz gebracht. Das CE-Spektrometer besteht im Wesentlichen aus einem optischen Resonator, einer Deuteriumlampe und einem Gitterspektrometer, die durch eine Spannvorrichtung quer zur Windrichtung im Luftraum des Kanals montiert wurden. Durch den Resonator können optische Weglängen von bis zu 40m bei gleichzeitig kompakter Bauweise von nur 80 cm realisiert werden. Für geringe Spurenstoffkonzentrationen ist die effektive optische Weglänge größer als für hohe Konzentrationen. Dadurch erreicht das CE-Spektrometer eine höhere Dynamik, als ein konventieneller Spektrometeraufbau ohne Resonator. Die für Gasaustauschmessungen benötigten relativen Tracerkonzentrationen können mit einer Auflösung von 1 Hz bestimmt werden. Zum Test des CE-Spektrometers wurden Gasaustauschraten von 1,4-Difluorbenzol gemessen und mit Ergebnissen eines bestehenden konventionellen Aufbaus verglichen. Der Vergleich hat die Einsatzfähigkeit der neuen Methode bestätigt. Das CE-Spektrometer bietet die Möglichkeit, durch Messung kurzzeitiger und lokaler Variationen in den Gaskonzentrationen, Einblicke in die intermittente Natur des Gastransports zwischen Ozean und Atmosphäre zu erhalten.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Timm, W.</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Judith A. J. Steen</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating the Confidence of Peptide Identifications without Decoy Databases</style></title><secondary-title><style face="normal" font="default" size="100%">Analytical Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">4314-4318</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alexandra G. Herzog</style></author><author><style face="normal" font="default" size="100%">T. Binder</style></author><author><style face="normal" font="default" size="100%">Felix Friedl</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating water-sided vertical gas concentration profiles by inverse modeling</style></title><secondary-title><style face="normal" font="default" size="100%">2nd Int. Conf. Eng. Optimization, Lisbon, 6--9. Sep. 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Mirko</style></author><author><style face="normal" font="default" size="100%">Michael Erz</style></author><author><style face="normal" font="default" size="100%">Klaus Zimmermann</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exact modelling of time-of-flight cameras for optimal depth maps</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Computational Photography (ICCP) 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Poster</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploratory and computational analysis of huge volume images for connectomics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Breitenreicher, D.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Daniilidis, K.</style></author><author><style face="normal" font="default" size="100%">Maragos, P.</style></author><author><style face="normal" font="default" size="100%">Paragios, N.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Computer Vision (ECCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">6312</style></volume><pages><style face="normal" font="default" size="100%">494--505</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Breitenreicher, D.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Daniilidis, K.</style></author><author><style face="normal" font="default" size="100%">Maragos, P.</style></author><author><style face="normal" font="default" size="100%">Paragios, N.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Computer Vision (ECCV)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">6312</style></volume><pages><style face="normal" font="default" size="100%">494–505</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Börner, K.</style></author><author><style face="normal" font="default" size="100%">Hermle, J.</style></author><author><style face="normal" font="default" size="100%">Christoph Sommer</style></author><author><style face="normal" font="default" size="100%">Brown, N. P.</style></author><author><style face="normal" font="default" size="100%">Knapp, B.</style></author><author><style face="normal" font="default" size="100%">Glass, B.</style></author><author><style face="normal" font="default" size="100%">Torralba, G.</style></author><author><style face="normal" font="default" size="100%">Reymann, J.</style></author><author><style face="normal" font="default" size="100%">Beil, N.</style></author><author><style face="normal" font="default" size="100%">Beneke, J.</style></author><author><style face="normal" font="default" size="100%">Pepperkok, R.</style></author><author><style face="normal" font="default" size="100%">Schneider, R.</style></author><author><style face="normal" font="default" size="100%">Ludwig, T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">From experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV-1 replication</style></title><secondary-title><style face="normal" font="default" size="100%">Biotechnology Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">39-49</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fusion moves for markov random field optimization</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Combinatorial algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">Graph algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Image restoration.</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">Motion</style></keyword><keyword><style  face="normal" font="default" size="100%">Stereo</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">8</style></number><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">1392–1405</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one possible way of achieving this by using graph cuts to combine pairs of suboptimal labelings or solutions. We call this combination process the fusion move. By employing recently developed graph-cut-based algorithms (so-called QPBO-graph cut), the fusion move can efficiently combine two proposal labelings in a theoretically sound way, which is in practice often globally optimal. We demonstrate that fusion moves generalize many previous graph-cut approaches, which allows them to be used as building blocks within a broader variety of optimization schemes than were considered before. In particular, we propose new optimization schemes for computer vision MRFs with applications to image restoration, stereo, and optical flow, among others. Within these schemes the fusion moves are used 1) for the parallelization of MRF optimization into several threads, 2) for fast MRF optimization by combining cheap-to-compute solutions, and 3) for the optimization of highly nonconvex continuous-labeled MRFs with 2D labels. Our final example is a nonvision MRF concerned with cartographic label placement, where fusion moves can be used to improve the performance of a standard inference method (loopy belief propagation). © 2006 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fusion moves for markov random field optimization</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Combinatorial algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">Graph algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Image restoration.</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">Motion</style></keyword><keyword><style  face="normal" font="default" size="100%">Stereo</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">8</style></number><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">1392–1405</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The efficient application of graph cuts to Markov Random Fields (MRFs) with multiple discrete or continuous labels remains an open question. In this paper, we demonstrate one possible way of achieving this by using graph cuts to combine pairs of suboptimal labelings or solutions. We call this combination process the fusion move. By employing recently developed graph-cut-based algorithms (so-called QPBO-graph cut), the fusion move can efficiently combine two proposal labelings in a theoretically sound way, which is in practice often globally optimal. We demonstrate that fusion moves generalize many previous graph-cut approaches, which allows them to be used as building blocks within a broader variety of optimization schemes than were considered before. In particular, we propose new optimization schemes for computer vision MRFs with applications to image restoration, stereo, and optical flow, among others. Within these schemes the fusion moves are used 1) for the parallelization of MRF optimization into several threads, 2) for fast MRF optimization by combining cheap-to-compute solutions, and 3) for the optimization of highly nonconvex continuous-labeled MRFs with 2D labels. Our final example is a nonvision MRF concerned with cartographic label placement, where fusion moves can be used to improve the performance of a standard inference method (loopy belief propagation). © 2006 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dominik Daume</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fusion von Midwave-infrared- und Longwave-infrared-Wärmebildgeräten zur Klassifizierung von Flugobjekten</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gulshan, Varun</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Criminisi, Antonio</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author><author><style face="normal" font="default" size="100%">Zisserman, Andrew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Geodesic star convexity for interactive image segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">3129–3136</style></pages><isbn><style face="normal" font="default" size="100%">9781424469840</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we introduce a new shape constraint for interactive image segmentation. It is an extension of Veksler&#039;s [25] star-convexity prior, in two ways: from a single star to multiple stars and from Euclidean rays to Geodesic paths. Global minima of the energy function are obtained subject to these new constraints. We also introduce Geodesic Forests, which exploit the structure of shortest paths in implementing the extended constraints. The star-convexity prior is used here in an interactive setting and this is demonstrated in a practical system. The system is evaluated by means of a &quot;robot user&quot; to measure the amount of interaction required in a precise way. We also introduce a new and harder dataset which augments the existing Grabcut dataset [1] with images and ground truth taken from the PASCAL VOC segmentation challenge [7]. ©2010 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Montanvert, A.</style></author><author><style face="normal" font="default" size="100%">Soille, P.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Geometric Analysis of 3D Electron Microscopy Data</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">22-26</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pius Warken</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hochauflösende LIF-Methode zur Messung von Sauerstoffkonzentrationsprofilen in der wasserseitigen Grenzschicht</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">How to Extract the Geometry and Topology from Very Large 3D Segmentations</style></title><secondary-title><style face="normal" font="default" size="100%">ArXiv e-prints</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1009.6215</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alexandra G. Herzog</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Imaging of Water-sided Gas-Concentration Fields at a Wind-Driven, Wavy Air-Water Interface</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ding, Lei</style></author><author><style face="normal" font="default" size="100%">Yilmaz, Alper</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interactive image segmentation using probabilistic hypergraphs</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Hypergraphs</style></keyword><keyword><style  face="normal" font="default" size="100%">Image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Interactive segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Semi-supervised learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.research.microsoft.com/vision/cambridge</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">1863–1873</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper introduces a novel interactive framework for segmenting images using probabilistic hypergraphs which model the spatial and appearance relations among image pixels. The probabilistic hypergraph provides us a means to pose image segmentation as a machine learning problem. In particular, we assume that a small set of pixels, which are referred to as seed pixels, are labeled as the object and background. The seed pixels are used to estimate the labels of the unlabeled pixels by learning on a hypergraph via minimizing a quadratic smoothness term formed by a hypergraph Laplacian matrix subject to the known label constraints. We derive a natural probabilistic interpretation of this smoothness term, and provide a detailed discussion on the relation of our method to other hypergraph and graph based learning methods. We also present a front-to-end image segmentation system based on the proposed method, which is shown to achieve promising quantitative and qualitative results on the commonly used GrabCut dataset. © 2009 Elsevier Ltd. All rights reserved.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Sommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interactive Learning for the Analysis of Biomedical and Industrial Imagery</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sven Wanner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interaktives Rendering von Wellendaten windgetriebener Wasseroberflächen und Ereignisklassifizierung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/11904</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigating the mechanisms of air-water gas transfer by quantitative imaging techniques: history, current progress and remaining challenges</style></title><secondary-title><style face="normal" font="default" size="100%">6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">abstract keynote talk</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search</style></title><secondary-title><style face="normal" font="default" size="100%">ArXiv e-prints</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1009.4102</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. Nickisch</style></author><author><style face="normal" font="default" size="100%">C. Rother</style></author><author><style face="normal" font="default" size="100%">P. Kohli</style></author><author><style face="normal" font="default" size="100%">C. Rhemann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning an Interactive Segmentation System - Supplemental Material</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Christoph Sommer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Goesele, M.</style></author><author><style face="normal" font="default" size="100%">Roth, S.</style></author><author><style face="normal" font="default" size="100%">Schiele, B.</style></author><author><style face="normal" font="default" size="100%">Schindler, K.</style></author><author><style face="normal" font="default" size="100%">Kuijper, A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning of Optimal Illumination for Material Classification</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 32nd DAGM Symposium on Pattern Recognition, Darmstadt, Germany</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">6376/2010</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">563-572</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Christoph Sommer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Goesele, Michael</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author><author><style face="normal" font="default" size="100%">Schiele, Bernt</style></author><author><style face="normal" font="default" size="100%">Schindler, Konrad</style></author><author><style face="normal" font="default" size="100%">Kuijper, Arjan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning of optimal illumination for material classification</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6376</style></volume><pages><style face="normal" font="default" size="100%">563--572</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a method to classify materials in illumination series data. An illumination series is acquired using a device which is capable to generate arbitrary lighting environments covering nearly the whole space of the upper hemisphere. The individual images of the illumination series span a high-dimensional feature space. Using a random forest classifier different materials, which vary in appearance (which itself depends on the patterns of incoming illumination), can be distinguished reliably. The associated Gini feature importance allows for determining the features which are most relevant for the classification result. By linking the features to illumination patterns a proposition about optimal lighting for defect detection can be made, which yields valuable information for the selection and placement of light sources.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">J. M. Buhmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning the Compositional Nature of Visual Object Categories for Recognition</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><volume><style face="normal" font="default" size="100%">32</style></volume><pages><style face="normal" font="default" size="100%">501--516</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fehr, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Local Rotation Invariant Patch Descriptors for 3D Vector Fields</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, International Conference on, Istanbul, Turkey, August 23-26, 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">1381-1384</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Niegel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung konvektionsgetriebener Transfergeschwindigkeit von Sauerstoff an der Luft-Wasser-Grenzfläche</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Judith A. J. Steen</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MGFp: An Open Mascot Generic Format Parser Library Implementation</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Proteome Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">9 (5)</style></volume><pages><style face="normal" font="default" size="100%">27622763</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Wei Feng</style></author><author><style face="normal" font="default" size="100%">Jiaya Jia</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Minimizing sparse higher order energy functions of discrete variables</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">1382–1389</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Higher order energy functions have the ability to encode high level structural dependencies between pixels, which have been shown to be extremely powerful for image labeling problems. Their use, however, is severely hampered in practice by the intractable complexity of representing and minimizing such functions. We observed that higher order functions encountered in computer vision are very often “sparse”, i.e. many labelings of a higher order clique are equally unlikely and hence have the same high cost. In this paper, we address the problem of minimizing such sparse higher order energy functions. Our method works by transforming the problem into an equivalent quadratic function minimization problem. The resulting quadratic function can be minimized using popular message passing or graph cut based algorithms for MAP inference. Although this is primarily a theoretical paper, it also shows how higher order functions can be used to obtain impressive results for the binary texture restoration problem.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernhard X. Kausler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modeling of Spectral Peaks for Mass-Spectrometry-based Proteomics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Universities of Karlsruhe and Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Daniilidis, K.</style></author><author><style face="normal" font="default" size="100%">Maragos, P.</style></author><author><style face="normal" font="default" size="100%">Paragios, N.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Computer Vision (ECCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6313</style></volume><pages><style face="normal" font="default" size="100%">735--747</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Daniilidis, K.</style></author><author><style face="normal" font="default" size="100%">Maragos, P.</style></author><author><style face="normal" font="default" size="100%">Paragios, N.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Computer Vision (ECCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">6313</style></volume><pages><style face="normal" font="default" size="100%">735--747</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, J. H.</style></author><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Daniilidis, K.</style></author><author><style face="normal" font="default" size="100%">Maragos, P.</style></author><author><style face="normal" font="default" size="100%">Paragios, N.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Computer Vision (ECCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">6313</style></volume><pages><style face="normal" font="default" size="100%">735–747</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhuang Lin</style></author><author><style face="normal" font="default" size="100%">Michael Erz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">P. Schelkens</style></author><author><style face="normal" font="default" size="100%">T. Ebrahimi</style></author><author><style face="normal" font="default" size="100%">F. Truchetet</style></author><author><style face="normal" font="default" size="100%">P. Saarikko</style></author><author><style face="normal" font="default" size="100%">G. Cristobal</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-frequency multi-sampling fluorescence lifetime imaging using a high-speed line-scan camera</style></title><secondary-title><style face="normal" font="default" size="100%">Optics, Photonics, and Digital Technologies for Multimedia Applications, 12--15 April 2010, Brussels</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">7723</style></volume><pages><style face="normal" font="default" size="100%">77231S</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">SPIE Proc.</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multimodal Medical Image Analysis: from Visualization to Disease Modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Zeitschrift für Med. Physik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">1-2</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Singaraju, Dheeraj</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">New appearance models for natural image matting</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">659–666</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image matting is the task of estimating a fore- and background layer from a single image. To solve this ill posed problem, an accurate modeling of the scene&#039;s appearance is necessary. Existing methods that provide a closed form solution to this problem, assume that the colors of the foreground and background layers are locally linear. In this paper, we show that such models can be an overfit when the colors of the two layers are locally constant. We derive new closed form expressions in such cases, and show that our models are more compact than existing ones. In particular, the null space of our cost function is a subset of the null space constructed by existing approaches. We discuss the bias towards specific solutions for each formulation. Experiments on synthetic and real data confirm that our compact models estimate alpha mattes more accurately than existing techniques, without the need of additional user interaction.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Voss</style></author><author><style face="normal" font="default" size="100%">Alexander Heinlein</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new approach for 3C3D measurements of aqueous boundary layer flows relative to the wind-wave undulated interface</style></title><secondary-title><style face="normal" font="default" size="100%">6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">poster</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Voss</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel strategy for water sided interfacial 3D3Cflow-visualization using a single camera</style></title><secondary-title><style face="normal" font="default" size="100%">14th International Symposium on Flow Visualization</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">D1-018</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">F. O. Kaster</style></author><author><style face="normal" font="default" size="100%">Kassemeyer, S.</style></author><author><style face="normal" font="default" size="100%">Merkel, B.</style></author><author><style face="normal" font="default" size="100%">Nix, O.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">T. M. Deserno</style></author><author><style face="normal" font="default" size="100%">H. Handels</style></author><author><style face="normal" font="default" size="100%">T. Tolxdorff</style></author><author><style face="normal" font="default" size="100%">Hans-Peter Meinzer</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements</style></title><secondary-title><style face="normal" font="default" size="100%">Bildverarbeitung für die Medizin 2010 -- Algorithmen, Systeme, Anwendungen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">97-101</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">Informatik Aktuell</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">F. Puente Leon</style></author><author><style face="normal" font="default" size="100%">Heinzmann, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal Lighting for Defect Detection: Illumination Systems, Machine
Learning, and Practical Verification</style></title><secondary-title><style face="normal" font="default" size="100%">Forum Bildverarbeitung, Regensburg, 02.-03.12.2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">KIT SCientific Publishing</style></publisher><pages><style face="normal" font="default" size="100%">301-312</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">F. Puente Leon</style></author><author><style face="normal" font="default" size="100%">M. Heizmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal lighting for defect detection: illumination systems, machine learning, and practical verification</style></title><secondary-title><style face="normal" font="default" size="100%">Forum Bildverarbeitung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://digbib.ubka.uni-karlsruhe.de/volltexte/1000020266</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">KIT Scientific Publishing</style></publisher><pages><style face="normal" font="default" size="100%">241--252</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Frank Lenzen</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimality Bounds for Variational Relaxations of Optimal Partition
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Image Proc.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">586-595</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chellappa, Rama.</style></author><author><style face="normal" font="default" size="100%">Association for Computing Machinery.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proceedings - 7th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2010</style></title><secondary-title><style 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size="100%">Recognition and Analysis of Objects in Medieval Images</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">296--305</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Breitenreicher, Dirk</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust 3D object registration without explicit correspondence using geometric integration</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Vision and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/g20710062l014241/</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">601-611</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Breitenreicher, Dirk</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust 3D object registration without explicit correspondence using geometric integration</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Vision and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/g20710062l014241/</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">601-611</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust Methods for the Proteomic Data Analysis Pipeline</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Thorben Kröger</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x</style></title><secondary-title><style face="normal" font="default" size="100%">ArXiv e-prints</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://arxiv.org/abs/1008.2909v1</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mansfield, Alex</style></author><author><style face="normal" font="default" size="100%">Gehler, Peter</style></author><author><style face="normal" font="default" size="100%">Van Gool, Luc</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Scene carving: Scene consistent image retargeting</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.fujifilm.com/products/3d/camera/finepix_</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">PART 1</style></number><volume><style face="normal" font="default" size="100%">6311 LNCS</style></volume><pages><style face="normal" font="default" size="100%">143–156</style></pages><isbn><style face="normal" font="default" size="100%">3642155480</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image retargeting algorithms often create visually disturbing distortion. We introduce the property of scene consistency, which is held by images which contain no object distortion and have the correct object depth ordering. We present two new image retargeting algorithms that preserve scene consistency. These algorithms make use of a user-provided relative depth map, which can be created easily using a simple GrabCut-style interface. Our algorithms generalize seam carving. We decompose the image retargeting procedure into (a) removing image content with minimal distortion and (b) re-arrangement of known objects within the scene to maximize their visibility. Our algorithms optimize objectives (a) and (b) jointly. However, they differ considerably in how they achieve this. We discuss this in detail and present examples illustrating the rationale of preserving scene consistency in retargeting. © 2010 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Robin Weber</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Setup of a Laser Slope Gauge for the Measurement of Wave Slope Distributions at the Small Circular Wind Wave Facility</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alexandra G. Herzog</style></author><author><style face="normal" font="default" size="100%">Felix Friedl</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous high-resolution LIF measurements of dissolved gas concentration fields and measurements of wave slope at a wavy free water surface with wind-induced turbulence</style></title><secondary-title><style face="normal" font="default" size="100%">15h Int. Symp on Appl. Laser Techniques to Fluid Mechanics, Lisbon, Portugal, July 05--08, 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Atif, Muhammad</style></author><author><style face="normal" font="default" size="100%">Klaus Zimmermann</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A space-variant (3D) image simulation tool for computational cameras</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Computational Photography (ICCP) 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Poster</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A spatially varying PSF-based prior for alpha matting</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">2149–2156</style></pages><isbn><style face="normal" font="default" size="100%">9781424469840</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we considerably improve on a state-of-theart alpha matting approach by incorporating a new prior which is based on the image formation process. In particular, we model the prior probability of an alpha matte as the convolution of a high-resolution binary segmentation with the spatially varying point spread function (PSF) of the camera. Our main contribution is a new and efficient deconvolution approach that recovers the prior model, given an approximate alpha matte. By assuming that the PSF is a kernel with a single peak, we are able to recover the binary segmentation with an MRF-based approach, which exploits flux and a new way of enforcing connectivity. The spatially varying PSF is obtained via a partitioning of the image into regions of similar defocus. Incorporating our new prior model into a state-of-the-art matting technique produces results that outperform all competitors, which we confirm using a publicly available benchmark. ©2010 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alexandra G. Herzog</style></author><author><style face="normal" font="default" size="100%">Felix Friedl</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-temporal fluctuations of water-sided gas concentration fields under wind-induced turbulence</style></title><secondary-title><style face="normal" font="default" size="100%">6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">abstract</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-temporal measurements of short wind water waves</style></title><secondary-title><style face="normal" font="default" size="100%">EGU General Assembly 2010, Symposium AS2.2</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">EGU2010-5509</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rathke, Fabian</style></author><author><style face="normal" font="default" size="100%">Hansen, Katja</style></author><author><style face="normal" font="default" size="100%">Brefeld, Ulf</style></author><author><style face="normal" font="default" size="100%">Müller, Klaus-Robert</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">StructRank: A new approach for ligand-based virtual screening</style></title><secondary-title><style face="normal" font="default" size="100%">J. Chem. Inf. Model.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">51</style></volume><pages><style face="normal" font="default" size="100%">83–92</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bergtholdt, Martin</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Schmidt, Stefan</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study of Parts-Based Object Class Detection Using Complete Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">Int.~J.~Comp.~Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/openurl.asp?genre=article&amp;id=doi:10.1007/s11263-009-0209-1</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1-2</style></number><volume><style face="normal" font="default" size="100%">87</style></volume><pages><style face="normal" font="default" size="100%">93-117</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bergtholdt, Martin</style></author><author><style face="normal" font="default" size="100%">Kappes, Jörg H.</style></author><author><style face="normal" font="default" size="100%">Schmidt, Stefan</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study of Parts-Based Object Class Detection Using Complete Graphs</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Comp. Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/openurl.asp?genre=article&amp;id=doi:10.1007/s11263-009-0209-1</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1-2</style></number><volume><style face="normal" font="default" size="100%">87</style></volume><pages><style face="normal" font="default" size="100%">93-117</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stephan Meister</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study on Ground Truth Generation for Optical Flow</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bleyer, Michael</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Surface stereo with soft segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><pages><style face="normal" font="default" size="100%">1570–1577</style></pages><isbn><style face="normal" font="default" size="100%">9781424469840</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper proposes a new stereo model which encodes the simple assumption that the scene is composed of a few, smooth surfaces. A key feature of our model is the surface-based representation, where each pixel is assigned to a 3D surface (planes or B-splines). This representation enables several important contributions: Firstly, we formulate a higher-order prior which states that pixels of similar appearance are likely to belong to the same 3D surface. This enables to incorporate the very popular color segmentation constraint in a soft and principled way. Secondly, we use a global MDL prior to penalize the number of surfaces. Thirdly, we are able to incorporate, in a simple way, a prior which favors low curvature surfaces. Fourthly, we improve the asymmetric occlusion model by disallowing pixels of the same surface to occlude each other. Finally, we use the known fusion move approach which enables a powerful optimization of our model, despite the infinite number of possible labelings (surfaces). ©2010 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Berthe</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Christensen, C.</style></author><author><style face="normal" font="default" size="100%">Goubergrits, L.</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Affeld, K.</style></author><author><style face="normal" font="default" size="100%">U. Kertzscher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Three-dimensional, three-component wall-PIV</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">online</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Glocker, Ben</style></author><author><style face="normal" font="default" size="100%">Heibel, T. Hauke</style></author><author><style face="normal" font="default" size="100%">Navab, Nassir</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TriangleFlow: Optical flow with triangulation-based higher-order likelihoods</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://vision.middlebury.edu/flow/</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">PART 3</style></number><volume><style face="normal" font="default" size="100%">6313 LNCS</style></volume><pages><style face="normal" font="default" size="100%">272–285</style></pages><isbn><style face="normal" font="default" size="100%">364215557X</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We use a simple yet powerful higher-order conditional random field (CRF) to model optical flow. It consists of a standard photo-consistency cost and a prior on affine motions both modeled in terms of higher-order potential functions. Reasoning jointly over a large set of unknown variables provides more reliable motion estimates and a robust matching criterion. One of the main contributions is that unlike previous region-based methods, we omit the assumption of constant flow. Instead, we consider local affine warps whose likelihood energy can be computed exactly without approximations. This results in a tractable, so-called, higher-order likelihood function. We realize this idea by employing triangulation meshes which immensely reduce the complexity of the problem. Optimization is performed by hierarchical fusion moves and an adaptive mesh refinement strategy. Experiments show that we achieve high-quality motion fields on several data sets including the Middlebury optical flow database. © 2010 Springer-Verlag.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Riedl, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Using Protein Identification Data to Improve Mass Spectrometry Feature Extraction</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heitz, D.</style></author><author><style face="normal" font="default" size="100%">Mémin, E.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational fluid flow measurements from image sequences: synopsis and perspectives</style></title><secondary-title><style face="normal" font="default" size="100%">Exp.~Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">369-393</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">DOI 10.1007/s00348-009-0778-3</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heitz, D.</style></author><author><style face="normal" font="default" size="100%">Mémin, E.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational fluid flow measurements from image sequences: synopsis and perspectives</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">369-393</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">DOI 10.1007/s00348-009-0778-3</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yarlagadda, P.</style></author><author><style face="normal" font="default" size="100%">Monroy, A.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Voting by Grouping Dependent Parts</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the European Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">6315</style></volume><pages><style face="normal" font="default" size="100%">197--210</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Kai Degreif</style></author><author><style face="normal" font="default" size="100%">Joachim Kuss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Wind/wave-tunnel measurements of chemical enhancement of the carbon dioxide gas exchange rate</style></title><secondary-title><style face="normal" font="default" size="100%">6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">abstract</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Volker Beushausen</style></author><author><style face="normal" font="default" size="100%">Karsten Roetmann</style></author><author><style face="normal" font="default" size="100%">Waldemar Schmunk</style></author><author><style face="normal" font="default" size="100%">Mike Wellhausen</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Nitsche</style></author><author><style face="normal" font="default" size="100%">C. Dobriloff</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">2D-measurement technique for simultaneous quantitative determination of mixing ratio and velocity field in microfluidic applications</style></title><secondary-title><style face="normal" font="default" size="100%">Imaging Measurement Methods for Flow Analysis, Results of the DFG Priority Programme 1147 Imaging Measurement Methods for Flow Analysis 2003-2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">155--164</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Two-dimensional Molecular-Tagging-Velocimetry (2D-MTV) has been used to investigate velocity fields of liquid flow in a micro mixer. Optical tagging was realized by using caged dye. For the first time patterns were generated by structured laser illumination using optical masks. This allows the generation of nearly any imaginable pattern. The flow induced deformation of the optically written pattern is tracked by imaging of laser induced fluorescence. Quantitative analysis of raw image series is carried out by novel optical flow based techniques. A comparison to the standard technique of uPIV has also been conducted. Additionally Planar Spontaneous Raman Scattering (PSRS) was applied in order to determine concentration fields for mixtures of ethanol and water.</style></abstract><custom3><style face="normal" font="default" size="100%">Notes on Numerical Fluid Mechanics and Multidisciplinary Design</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Schröder, A.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Nitsche</style></author><author><style face="normal" font="default" size="100%">C. Dobriloff</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">3D Tomography from Few Projections in Experimental Fluid Mechanics</style></title><secondary-title><style face="normal" font="default" size="100%">Imaging Measurement Methods for Flow Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">63-72</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Notes on Numerical Fluid Mechanics and Multidisciplinary Design</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shesh, Amit</style></author><author><style face="normal" font="default" size="100%">Criminisi, Antonio</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Smyth, Gavin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D-aware image editing for out of bounds photography</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - Graphics Interface</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.flickr.com/groups/oob/</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">47–54</style></pages><isbn><style face="normal" font="default" size="100%">9781568814704</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper, we propose algorithms to manipulate 2D images in a way that is consistent with the 3D geometry of the scene that they capture. We present these algorithms in the context of creating &quot;Out of Bounds&quot; (OOB) images - compelling, depth-rich images generated from single, conventional 2D photographs (fig. 1). Starting from a single image our tool enables rapid OOB prototyping; i.e. the ability to quickly create and experiment with many different variants of the OOB effect before deciding which one best expresses the users&#039; artistic intentions. We achieve this with a flexible work-flow driven by an intuitive user interface. The rich 3D perception of the final composition is achieved by exploiting two strong cues - occlusions and shadows. A realistic-looking 3D frame is interactively inserted in the scene between segmented foreground objects and the background to generate novel occlusions and enhance the scene&#039;s perception of depth. This perception is further enhanced by adding new, realistic cast shadows. The key contributions of this paper are: (i) new algorithms for inserting simple 3D objects like frames in 2D images requiring minimal camera calibration, and (ii) new techniques for the realistic synthesis of cast shadows, even for complex 3D objects. These algorithms, although presented for OOB photography, may be directly used in general image composition tasks. With our tool, untrained users can turn ordinary photos into compelling OOB images in seconds. In contrast with existing workflows, at any time the artist can modify any aspect of the composition while avoiding time-consuming pixel painting operations. Such a tool has important commercial applications, and is much more suitable for OOB prototyping than existing image editors.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Popa, C.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accelerating Constrained SIRT with Applications in Tomographic Particle Image Reconstruction</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9477</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">John H. Steele</style></author><author><style face="normal" font="default" size="100%">Karl K. Turekian</style></author><author><style face="normal" font="default" size="100%">Steve A. Thorpe</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Air-sea gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Encyclopedia Ocean Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Elsevier</style></publisher><pages><style face="normal" font="default" size="100%">147-156</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The exchange of inert and sparingly soluble gases, including carbon dioxide, methane, and oxygen, between the atmosphere and oceans is controlled by a 20-200 um-thick boundary layer at the top of the ocean. The hydrodynamics in this layer is significantly different from boundary layers at rigid walls since the orbital motion of the waves is of the same order as the velocities in the viscous boundary layer. Laboratory and field measurements show that wind waves and surfactants significantly influence the gas-transfer process. Because of limited experimental techniques, the details of the mechanisms and the structure of the turbulence in the boundary layer at a wavy water surface are still not known. A number of new imaging techniques are described which give direct insight into the transfer processes and promise to trigger substantial theoretical progress in the near future.</style></abstract><notes><style face="normal" font="default" size="100%">invited</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Görlitz, L.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Staudacher, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Allocation of particles to development processes</style></title><secondary-title><style face="normal" font="default" size="100%">Patent</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hayn, M.</style></author><author><style face="normal" font="default" size="100%">S. Beirle</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">T. Wagner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysing spatio-temporal patterns of the global NO&lt;SUB&gt;2&lt;/SUB&gt;-distribution retrieved frome GOME satellite observations using a generalized additive model</style></title><secondary-title><style face="normal" font="default" size="100%">Atmospheric Chemistry and Physics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">17</style></number><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">9367-9398</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jäger, M.</style></author><author><style face="normal" font="default" size="100%">Kiel, A.</style></author><author><style face="normal" font="default" size="100%">Herten, D.-P.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of Single-Molecule Fluorescence Spectroscopic Data with a Markov Modulated Poisson Process</style></title><secondary-title><style face="normal" font="default" size="100%">ChemPhysChem</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">10:14</style></volume><pages><style face="normal" font="default" size="100%">2486-2495</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fehr, J.</style></author><author><style face="normal" font="default" size="100%">Burkhardt, H.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">G. Bebis</style></author><author><style face="normal" font="default" size="100%">et al.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bag of Features Approach for 3D Shape Retrieval</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISVC 2009, Part I</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5875</style></volume><pages><style face="normal" font="default" size="100%">34-43</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leila Nagel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bestimmung der Temperaturdifferenz über die viskose Grenzschicht mit Hilfe des Oberflächenerneuerungsmodells</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hansen, Katja</style></author><author><style face="normal" font="default" size="100%">Rathke, Fabian</style></author><author><style face="normal" font="default" size="100%">Schroeter, Timon</style></author><author><style face="normal" font="default" size="100%">Rast, Georg</style></author><author><style face="normal" font="default" size="100%">Fox, Thomas</style></author><author><style face="normal" font="default" size="100%">Kriegl, Jan M</style></author><author><style face="normal" font="default" size="100%">Mika, Sebastian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bias-correction of regression models: a case study on hERG inhibition</style></title><secondary-title><style face="normal" font="default" size="100%">J. Chem. Inf. Model.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">1486–1496</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">6</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ozlu, N.</style></author><author><style face="normal" font="default" size="100%">Monigatti, F.</style></author><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Field, C. M.</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author><author><style face="normal" font="default" size="100%">Mitchison, T. J.</style></author><author><style face="normal" font="default" size="100%">Judith Jebanthirajah Steen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Binding partner switching on microtubules and aurora-B in the mitosis to cytokinesis transition</style></title><secondary-title><style face="normal" font="default" size="100%">Molecular &amp; Cellular Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Hörnlein</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Herbert Süße</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Joachim Denzler</style></author><author><style face="normal" font="default" size="100%">Gunther Notni</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Boosting shift-invariant features</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5748</style></volume><pages><style face="normal" font="default" size="100%">121--130</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work presents a novel method for training shift-invariant features using a Boosting framework. Features performing local convolutions followed by subsampling are used to achieve shift-invariance. Other systems using this type of features, e.g. Convolutional Neural Networks, use complex feed-forward networks with multiple layers. In contrast, the proposed system adds features one at a time using smoothing spline base classifiers. Feature training optimizes base classifier costs. Boosting sample-reweighting ensures features to be both descriptive and independent. Our system has a lower number of design parameters as comparable systems, so adapting the system to new problems is simple. Also, the stage-wise training makes it very scalable. Experimental results show the competitiveness of our approach.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">F. O. Kaster</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">C. M. Zechmann</style></author><author><style face="normal" font="default" size="100%">M.-A. Weber</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Nix, O.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Dössel, Olaf</style></author><author><style face="normal" font="default" size="100%">Schlegel, Wolfgang C.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Classification of Spectroscopic Images in the DIROlab Environment</style></title><secondary-title><style face="normal" font="default" size="100%">World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">25050252</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">25/V</style></volume><pages><style face="normal" font="default" size="100%">252--255</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">IFMBE Proceedings</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Masuch, R.</style></author><author><style face="normal" font="default" size="100%">Himmelreich, U.</style></author><author><style face="normal" font="default" size="100%">Bachert, P.</style></author><author><style face="normal" font="default" size="100%">Petrich, W.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparison of Random Forest and its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">10:213</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Keränen, S. V. 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Fischer</style></author><author><style face="normal" font="default" size="100%">Hammonds, A.</style></author><author><style face="normal" font="default" size="100%">Celniker, S. E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computational Analysis of Quantitative Changes in Gene Expression and Embryo Morphology between Species</style></title><secondary-title><style face="normal" font="default" size="100%">Evolution-The Molecular Landscape</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lauer, F.</style></author><author><style face="normal" font="default" size="100%">Bloch, G.</style></author><author><style face="normal" font="default" size="100%">Vidal, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Continuous Optimization Framework for Hybrid System Identification</style></title><secondary-title><style face="normal" font="default" size="100%">submitted to Automatica</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christian Gosch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Contour Methods for View Point Tracking</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9684/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phd</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, Jing</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Steidl, Gabriele</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Hodge Decomposition and Regularization of Image Flows</style></title><secondary-title><style face="normal" font="default" size="100%">J.~Math.~Imag.~Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">169-177</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Tai, X.-C.</style></author><author><style face="normal" font="default" size="100%">Mórken, K.</style></author><author><style face="normal" font="default" size="100%">Lie, K.-A.</style></author><author><style face="normal" font="default" size="100%">Lysaker, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space and Variational Methods in Computer Vision (SSVM 2009)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5567</style></volume><pages><style face="normal" font="default" size="100%">150-162</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Mórken, K.</style></author><author><style face="normal" font="default" size="100%">Lysaker, M.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Tai, X.-C.</style></author><author><style face="normal" font="default" size="100%">Lie, K.-A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space and Variational Methods in Computer Vision (SSVM 2009)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5567</style></volume><pages><style face="normal" font="default" size="100%">150-162</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Optimization for Multi-Class Image Labeling with a Novel Family
of Total Variation Based Regularizers</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">646-653</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Computer Vision (ICCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">646 -- 653</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marco Hering</style></author><author><style face="normal" font="default" size="100%">Klaus Körner</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Correlated speckle noise in white-light interferometry: theoretical analysis of measurement uncertainty</style></title><secondary-title><style face="normal" font="default" size="100%">Appl. Optics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">525--538</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The partial coherent illumination of the specimen, which is required for white-light interferometric measurements of optically rough surfaces, directly leads to speckle. The electric field of such speckle patterns strongly fluctuates in amplitude and phase. This spatially correlated noise influences the accuracy of the measuring device. Although a variety of noise sources in white-light interferometry has been studied in recent years, they do not account for spatial correlation and, hence, they cannot be applied to speckle noise. Thus, we derive a new model enabling quantitative predictions for measurement uncertainty caused by speckle. The model reveals that the accuracy can be attributed mainly to the degree of spatial correlation, i.e., the average size of a speckle, and to the coherence length of the light source. The same parameters define the signal-to-noise ratio in the spectral domain. The model helps to design filter functions that are perfectly adapted to the noise characteristics of the respective device, thus improving the accuracy of postprocessing algorithms for envelope detection. The derived expressions are also compared to numerical simulations and experimental data of two different types of interferometers. These results are a first validation of the theoretical considerations of this article.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fehr, J.</style></author><author><style face="normal" font="default" size="100%">Reisert, M.</style></author><author><style face="normal" font="default" size="100%">Burkhardt, H.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">G. Bebis</style></author><author><style face="normal" font="default" size="100%">et al.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Cross-Correlation and Rotation Estimation of Local 3D Vector FieldPatches</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ISVC 2009, Part I</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5875</style></volume><pages><style face="normal" font="default" size="100%">287-296</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mario Frank</style></author><author><style face="normal" font="default" size="100%">Matthias Plaue</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Denoising of Continuous-Wave Time-Of-Flight Depth Images Using Confidence Measures</style></title><secondary-title><style face="normal" font="default" size="100%">Optical Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">48, 077003</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Nix, O.</style></author><author><style face="normal" font="default" size="100%">C. M. Zechmann</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating Kinetic Parameter Maps from Dynamic Contrast-Enhanced MRI using Spatial Prior Knowledge</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transaction on Medical Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">28:10</style></volume><pages><style face="normal" font="default" size="100%">1534-1547</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bähnisch, C.</style></author><author><style face="normal" font="default" size="100%">Stelldinger, P.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast and Accurate 3D Edge Detection for Surface Reconstruction</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5748</style></volume><pages><style face="normal" font="default" size="100%">111-120</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leila Nagel</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">First results from the 2009 SOPRAN active thermography pilot experiment in the Baltic Sea</style></title><secondary-title><style face="normal" font="default" size="100%">Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">To improve the parametrization of the transfer velocities a novel active thermography system was developed. At the end of SOPRAN 1 it has been deployed in a pilot experiment at the bow of the research vessel FS Alkor in the Baltic sea. A periodically varying heat flux is applied by a CO2-laser. The transfer velocity is estimated by the amplitude damping and phase shift of the temperature signal in the Fourier domain, gained from the infrared images of the water surface. First results are shown. The system will be used in the second phase of the SOPRAN project on a regular base. Together with eddy covariance measurements deployed from FINO 2, a significant improvement of the parametrization of the heat an gas transfer velocities can be expected.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thieke, C.</style></author><author><style face="normal" font="default" size="100%">Nix, O.</style></author><author><style face="normal" font="default" size="100%">Koehn, A.</style></author><author><style face="normal" font="default" size="100%">Floca, R.</style></author><author><style face="normal" font="default" size="100%">van Straaten, D.</style></author><author><style face="normal" font="default" size="100%">Hahn, H.</style></author><author><style face="normal" font="default" size="100%">Strauss, L. G.</style></author><author><style face="normal" font="default" size="100%">Siems, U.</style></author><author><style face="normal" font="default" size="100%">Graf, M.</style></author><author><style face="normal" font="default" size="100%">Pruem, H.</style></author><author><style face="normal" font="default" size="100%">Klein, J.</style></author><author><style face="normal" font="default" size="100%">Laue, H.</style></author><author><style face="normal" font="default" size="100%">F. O. Kaster</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A framework and multi-application prototype for integrated radiological diagnostics and radiation therapy</style></title><secondary-title><style face="normal" font="default" size="100%">Strahlentherapie und Onkologie</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">Suppl. 1</style></number><volume><style face="normal" font="default" size="100%">185</style></volume><pages><style face="normal" font="default" size="100%">81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nicola, A.</style></author><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Popa, C.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On a general extending and constraining procedure for linear iterative methods</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9761</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nicola, A.</style></author><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Popa, C.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On a general extending and constraining procedure for linear iterative methods</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">August</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9761</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Technical Report</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Woodford, Oliver J</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Global Perspective on MAP Inference for Low-Level Vision Supplementary material to ICCV submission \# 1536</style></title><secondary-title><style face="normal" font="default" size="100%">Optimization</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications. However, in a maximum a posteriori (MAP) framework, MRFs inherently encourage delta function marginal statistics. By contrast, many low-level vision problems have heavy tailed marginal statistics, making the MRF model unsuitable. In this paper we introduce a more general Marginal Probability Field (MPF), of which the MRF is a special, linear case, and show that convex energy MPFs can be used to encourage arbitrary marginal statistics. We introduce a flexible, extensible framework for effectively optimizing the resulting NP-hard MAP problem , based around dual-decomposition and a modified min-cost flow algorithm, and which achieves global optimality in some instances. We use a range of applications, including image denoising and texture synthesis, to demonstrate the benefits of this class of MPF over MRFs.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Sharp, Toby</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image segmentation with a bounding box prior</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">277–284</style></pages><isbn><style face="normal" font="default" size="100%">9781424444205</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">User-provided object bounding box is a simple and popular interaction paradigm considered by many existing interactive image segmentation frameworks. However, these frameworks tend to exploit the provided bounding box merely to exclude its exterior from consideration and sometimes to initialize the energy minimization. In this paper, we discuss how the bounding box can be further used to impose a powerful topological prior, which prevents the solution from excessive shrinking and ensures that the user-provided box bounds the segmentation in a sufficiently tight way. The prior is expressed using hard constraints incorporated into the global energy minimization framework leading to an NP-hard integer program. We then investigate the possible optimization strategies including linear relaxation as well as a new graph cut algorithm called pinpointing. The latter can be used either as a rounding method for the fractional LP solution, which is provably better than thresholding-based rounding, or as a fast standalone heuristic. We evaluate the proposed algorithms on a publicly available dataset, and demonstrate the practical benefits of the new prior both qualitatively and quantitatively. ©2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kiefhaber</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Instrument development for combined height/slope/curvature statistics measurements of wind water waves in the field</style></title><secondary-title><style face="normal" font="default" size="100%">Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">An optical method for the measurement of slope statistics of capillary and short gravity wind waves on the ocean is currently under development. Specular reflections from infrared LED light sources are observed on the water surface with a stereo camera setup. The principle is similar to Cox &amp; Munk&#039;s derivation of slope statistics from photographs of the Sun&#039;s glitter. Fractional area of the speckles in the image is related to the local slope probability distribution, speckle size and brightness is correlated with surface curvature, and the local water height can be inferred from parallax in the stereo images. The instrument measures slope statistics locally, making it a beneficial appendix to air-sea interaction measurements. It is scheduled to accompany heat transfer experiments in the Baltic Sea in phase II of the SOPRAN project.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Breitenreicher, Dirk</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Boykov, Y.</style></author><author><style face="normal" font="default" size="100%">Schmidt, F. R.</style></author><author><style face="normal" font="default" size="100%">Blake, A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/1470n7577713069q/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5681</style></volume><pages><style face="normal" font="default" size="100%">274-287</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Breitenreicher, Dirk</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Boykov, Y.</style></author><author><style face="normal" font="default" size="100%">Blake, A.</style></author><author><style face="normal" font="default" size="100%">Schmidt, F. R.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence</style></title><secondary-title><style face="normal" font="default" size="100%">Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/1470n7577713069q/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5681</style></volume><pages><style face="normal" font="default" size="100%">274-287</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vicente, Sara</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Joint optimization of segmentation and appearance models</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">755–762</style></pages><isbn><style face="normal" font="default" size="100%">9781424444205</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many interactive image segmentation approaches use an objective function which includes appearance models as an unknown variable. Since the resulting optimization problem is NP-hard the segmentation and appearance are typically optimized separately, in an EM-style fashion. One contribution of this paper is to express the objective function purely in terms of the unknown segmentation, using higher-order cliques. This formulation reveals an interesting bias of the model towards balanced segmentations. Furthermore, it enables us to develop a new dual decomposition optimization procedure, which provides additionally a lower bound. Hence, we are able to improve on existing optimizers, and verify that for a considerable number of real world examples we even achieve global optimality. This is important since we are able, for the first time, to analyze the deficiencies of the model. Another contribution is to establish a property of a particular dual decomposition approach which involves convex functions depending on foreground area. As a consequence, we show that the optimal decomposition for our problem can be computed efficiently via a parametric maxflow algorithm. ©2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fehr, J.</style></author><author><style face="normal" font="default" size="100%">Burkhardt, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Local Rotation Invariant Patch Descriptors for 3D Vector Fields</style></title><secondary-title><style face="normal" font="default" size="100%">to be submitted</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Methoden zur schnellen und genauen Messung der Gasaustauschrate im Aelotron mit Gasen niedriger und hoher Löslichkeit</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Forschungsgruppe Bildverarbeitung, Interdisziplinäres Zentrum für Wissenschaftliches Rechnen und Institut für Umweltphysik, Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Optical Flow Estimation with Applications to Fluid Dynamics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/10184</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kondermann, Daniel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modular Optical Flow Estimation with Applications to Fluid Dynamics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Malik, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-scale Object Detection by Clustering Lines</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">484--491</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Heeren, R. M. A.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multivariate Watershed Segmentation of Compositional Data</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5810</style></volume><pages><style face="normal" font="default" size="100%">180-192</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Trittler, S.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Near-Optimum Sampling Design and an Efficient Algorithm for Single Tone Frequency Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Digital Signal Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">628-639</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Singaraju, Dheeraj</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">New appearance models for natural image matting</style></title><secondary-title><style face="normal" font="default" size="100%">2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">2009 IEEE</style></volume><pages><style face="normal" font="default" size="100%">659–666</style></pages><isbn><style face="normal" font="default" size="100%">9781424439935</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image matting is the task of estimating a fore- and background layer from a single image. To solve this ill posed problem, an accurate modeling of the scene&#039;s appearance is necessary. Existing methods that provide a closed form solution to this problem, assume that the colors of the foreground and background layers are locally linear. In this paper, we show that such models can be an overfit when the colors of the two layers are locally constant. We derive new closed form expressions in such cases, and show that our models are more compact than existing ones. In particular, the null space of our cost function is a subset of the null space constructed by existing approaches. We discuss the bias towards specific solutions for each formulation. Experiments on synthetic and real data confirm that our compact models estimate alpha mattes more accurately than existing techniques, without the need of additional user interaction. © 2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerstin E. Richter</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">New schemes for fast measurements of air-sea gas exchange in the Aeolotron lab</style></title><secondary-title><style face="normal" font="default" size="100%">Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A number of novel experimental techniques to measure the gas transfer rates of volatile tracers in the wind/wave flume Aeolotron were developed. With the aid of the recently remodeled air conditioning system, the temperature, humidity and air flush rate could be controlled precisely in an open (exchange of air) and closed circulation system. The newly developed experimental schemes were optimized to measure gas transfer rates of half a dozen chemical species simultaneously using FTIR and UV spectroscopy together with thermographic measurements and wind wave measurements. These new experimental schemes are fast enough (temporal resolution less than a minute) to perform measurements in non-stationary and transient conditions. Results of the first series of measurements include precise measurements of the Schmidt number exponent at clean water surfaces.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mbock, K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Novel Algorithm for Motion Estimation with Explicit Consideration of Perturbations</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Objektive Kriterien unterstützen die anwendungsorientierte Auswahl einer Kamera</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.vdma-verlag.com/home/p464.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">VDMA, Frankfurt</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolfgang Mischler</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical method for measuring size-distribution and lifetime of bubbles</style></title><secondary-title><style face="normal" font="default" size="100%">Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A high resolution imaging method for measuring the size distribution of bubbles entrained by water jets between 10-1000 um in diameter is presented. The bubble clouds are similar to those produced by breaking water waves. The goal of these measurements is the validation of models for bubble mediated gas transfer. A Depth from Focus technique is used to determine the 3D position and the size of the bubbles at the same time. This yields the size-distribution and its dependence on depth. Additionally the velocity of the bubbles is estimated using repeatedly exposed images. To evaluate the lifetime of bubbles a high-speed camera is used to count the bubbles crossing the surface. The measuring principle, image analysis and first results are presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Wang, Jue</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Rott, Pamela</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A perceptually motivated online benchmark for image matting</style></title><secondary-title><style face="normal" font="default" size="100%">2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.alphamatting.com.</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2009 IEEE</style></volume><pages><style face="normal" font="default" size="100%">1826–1833</style></pages><isbn><style face="normal" font="default" size="100%">9781424439935</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. © 2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Wang, Jue</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Rott, Pamela</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A perceptually motivated online benchmark for image matting</style></title><secondary-title><style face="normal" font="default" size="100%">2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Benchmark</style></keyword><keyword><style  face="normal" font="default" size="100%">Evaluation</style></keyword><keyword><style  face="normal" font="default" size="100%">Matting</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.alphamatting.com.</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2009 IEEE</style></volume><pages><style face="normal" font="default" size="100%">1826–1833</style></pages><isbn><style face="normal" font="default" size="100%">9781424439935</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. © 2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Wang, Jue</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Rott, Pamela</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A perceptually motivated online benchmark for image matting</style></title><secondary-title><style face="normal" font="default" size="100%">2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">2009 IEEE</style></volume><pages><style face="normal" font="default" size="100%">1826–1833</style></pages><isbn><style face="normal" font="default" size="100%">9781424439935</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The availability of quantitative online benchmarks for low-level vision tasks such as stereo and optical flow has led to significant progress in the respective fields. This paper introduces such a benchmark for image matting. There are three key factors for a successful benchmarking system: (a) a challenging, high-quality ground truth test set; (b) an online evaluation repository that is dynamically updated with new results; (c) perceptually motivated error functions. Our new benchmark strives to meet all three criteria. We evaluated several matting methods with our benchmark and show that their performance varies depending on the error function. Also, our challenging test set reveals problems of existing algorithms, not reflected in previously reported results. We hope that our effort will lead to considerable progress in the field of image matting, and welcome the reader to visit our benchmark at www.alphamatting.com. © 2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Mirko</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A physical model of Time-of-Flight 3D imaging systems, including suppression of ambient light</style></title><secondary-title><style face="normal" font="default" size="100%">3rd Workshop on Dynamic 3-D Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5742</style></volume><pages><style face="normal" font="default" size="100%">1--15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We have developed a physical model of continuous-wave Time-of-Flight cameras, which focuses on a realistic reproduction of the sensor data. The derived simulation gives the ability to simulate data acquired by a ToF system with low computational effort. The model is able to use an arbitrary optical excitation and to simulate the sampling of a target response by a two-tap sensor, which can use any switching function. Nonlinear photo response and pixel saturation, as well as spatial variations from pixel to pixel like photo response non-uniformity (PRNU) and dark signal non-uniformity (DSNU) can be modeled. Also the influence of interfering background light and on-sensor suppression of ambient light can be simulated. The model was verified by analyzing two scenarios: The cameras response to an increasing, homogeneous irradiation as well as the systematic phase deviation caused by higher harmonics of the optical excitation. In both scenarios the model gave a precise reproduction of the observed data.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meine, H.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Stelldinger, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pixel Approximation Errors in Common Watershed Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Discrete Geometry for Computer Imagery</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5810</style></volume><pages><style face="normal" font="default" size="100%">193-202</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meine, H.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Stelldinger, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pixel Approximation Errors in Common Watershed Algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Discrete Geometry for Computer Imagery</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5810</style></volume><pages><style face="normal" font="default" size="100%">193-202</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Claudia Kondermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Postprocessing and Restoration of Optical Flows</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9681</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kondermann, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Postprocessing and Restoration of Optical Flows</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ. Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Görlitz, L.</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Processing Spectral Data</style></title><secondary-title><style face="normal" font="default" size="100%">Surface and Interface Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">636-644</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kassemeyer, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantification of Tumour Angiogenesis Using Pattern Recognition</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Bonea, A.</style></author><author><style face="normal" font="default" size="100%">Nadler, B.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition. 31st DAGM Symposium, Jena, Germany, September 9-11, 2009. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5748</style></volume><pages><style face="normal" font="default" size="100%">502-511</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Erz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Reinhard Koch</style></author><author><style face="normal" font="default" size="100%">Andreas Kolb</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Radiometric and spectrometric calibrations, and distance noise measurement of TOF cameras</style></title><secondary-title><style face="normal" font="default" size="100%">3rd Workshop on Dynamic 3-D Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5742</style></volume><pages><style face="normal" font="default" size="100%">28--41</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper proposes to extend the EMVA 1288 standard to characterize the properties and noise of image sensors for ToF cameras. The concepts for radiometric and spectrometric sensitivities were extended for intensity images recorded by lock-in pixels. The characterization of the distance information was performed by describing the phase shift analogous to intensities. Results of sensitivity and noise measurements are presented for two ToF cameras: PMDTec CamCube and MESA Imaging SR3101. Both cameras had no intrinsic filter, so the quantum efficiency could be measured from UV to IR. The noise in the phase measurement could be related to the noise in the intensity.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Grützmann, Andreas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reconstruction of Moving Surfaces of Revolution from Sparse 3-D Measurements using a Stereo Camera and Structured Light</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/10162</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The aim of this thesis is the development and analysis of an algorithmic framework for the reconstruction of a parametric model for a moving surface of revolution from a sequence of sparse 3-D point clouds. A new measurement device with a large field of view that allows for acquisition of three-dimensional data in challenging environments is utilized. During the measurement process, the observed object may be subject to motion which can be described in terms of an analytical model. The proposed method is developed and analyzed, along with an application for the surface reconstruction of a wheel. It is shown that the precision of the coarse surface model independently fitted to each measurement can be significantly improved by fitting a global model to all measurements of the sequence simultaneously. The global model also takes into account the object&#039;s motion. The three-dimensional point clouds are acquired by an optical device which consists of a stereo camera and an illumination unit projecting a dot pattern. A rather high density of surface points within the camera&#039;s field of view is established by means of multiple laser projectors. Through an elaborate calibration procedure of the stereo camera and the projector, and by utilizing the trifocal epipolar constraints of the measurement device, a high accuracy in the three-dimensional point cloud is achieved.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Mader, T.</style></author><author><style face="normal" font="default" size="100%">J. M. Buhmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Seeing the Objects Behind the Dots: Recognition in Videos from a Moving Camera</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">83</style></volume><pages><style face="normal" font="default" size="100%">57--71</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Staudacher, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Görlitz, L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Self Adjustment of Scanning Electron Microscopes / Selbstadaptivität von Rasterelektronenmikroskopen</style></title><secondary-title><style face="normal" font="default" size="100%">Patent, Patent Number  WO2009062781A1</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Greis, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-automatic analysis of high-information-content neurobiological image data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kumpf, Tobias</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sichtfelddesign mittels Freiformspiegel für katadioptrische Systeme</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Nitsche</style></author><author><style face="normal" font="default" size="100%">C. Dobriloff</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatiotemporal image analysis for flow measurements</style></title><secondary-title><style face="normal" font="default" size="100%">Imaging Measurement Methods for Flow Analysis, Results of the DFG Priority Programme 1147 Imaging Measurement Methods for Flow Analysis 2003-2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">289--305</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this chapter, a framework will be presented for measuring and modeling transport processes using novel visualization techniques and extended optical flow techniques for digital image sequence analysis. In this way, parameters besides the 2-D xy velocity components can be extracted concurrently from the acquired 2-D image sequences, such as wall shear rates and momentum transport close to boundaries, diffusion coefficients, and depth z in addition to the z velocity components. Depending on the application, particularly the temporal regularization can be enhanced, leading to stabilization of results and reduction of spatial regularization. This is frequently of high importance for flows close to boundaries. Results from applications will be presented from the fields of environmental and life sciences as well as from engineering.</style></abstract><custom3><style face="normal" font="default" size="100%">Notes on Numerical Fluid Mechanics and Multidisciplinary Design</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lauer, F.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spectral Clustering of Linear Subspaces for Motion Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, in press</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">678-685</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lauer, F.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spectral Clustering of Linear Subspaces for Motion Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.~IEEE Int.~Conf.~Computer Vision (ICCV&#039;09)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">accepted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lauer, F.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spectral Clustering of Linear Subspaces for Motion Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE Int. Conf. Computer Vision (ICCV&#039;09)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 29-Oct. 2</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Kyoto, Japan</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">accepted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bleyer, Michael</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A stereo approach that handles the matting problem via imagewarping</style></title><secondary-title><style face="normal" font="default" size="100%">2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alpha Matting</style></keyword><keyword><style  face="normal" font="default" size="100%">Stereo Matching</style></keyword><keyword><style  face="normal" font="default" size="100%">Stereo Matting</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">2009 IEEE</style></volume><pages><style face="normal" font="default" size="100%">501–508</style></pages><isbn><style face="normal" font="default" size="100%">9781424439935</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose an algorithm that simultaneously extracts disparities and alpha matting information given a stereo image pair. Our method divides the reference image into a set of overlapping, partially transparent color segments. Each segment pixel is assigned an alpha value and color. The disparity inside the segment is modeled via a plane. The goodness of alphas, colors and disparity planes is measured by a new energy function. Its basic idea is to use the three parameters for generating artificial views representing the left and right images. If alphas, colors and disparity planes are correct, these artificial images should be very similar to the real ones. For generating the artificial right view, we warp all pixels of the left into the geometry of the right image using the disparity planes. We introduce the assumption of constant solidity in order to correctly model how pixels&#039; alpha values are affected by the warping operation. Experimental results on the Middlebury set show that our algorithm gives good results in comparison to the state-of-the-art in stereo matching. ©2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Singaraju, D</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Rhemann, C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Supplementary material for New Appearance Models for Image Matting</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shotton, Jamie</style></author><author><style face="normal" font="default" size="100%">Winn, John</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Criminisi, Antonio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Boosting</style></keyword><keyword><style  face="normal" font="default" size="100%">Conditional random field</style></keyword><keyword><style  face="normal" font="default" size="100%">Context</style></keyword><keyword><style  face="normal" font="default" size="100%">image understanding</style></keyword><keyword><style  face="normal" font="default" size="100%">Layout</style></keyword><keyword><style  face="normal" font="default" size="100%">Object recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Piecewise training</style></keyword><keyword><style  face="normal" font="default" size="100%">Segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantic image segmentation</style></keyword><keyword><style  face="normal" font="default" size="100%">Textons</style></keyword><keyword><style  face="normal" font="default" size="100%">Texture</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://jamie.shotton.org/work/code.html</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">81</style></volume><pages><style face="normal" font="default" size="100%">2–23</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695-702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.). © 2007 Springer Science+Business Media, LLC.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mario Frank</style></author><author><style face="normal" font="default" size="100%">Matthias Plaue</style></author><author><style face="normal" font="default" size="100%">Holger Rapp</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Theoretical and Experimental Error Analysis of Continuous-Wave Time-Of-Flight Range Cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Optical Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">48, 013602</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mario Frank</style></author><author><style face="normal" font="default" size="100%">Matthias Plaue</style></author><author><style face="normal" font="default" size="100%">Holger Rapp</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Opt. Eng.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">013602</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We offer a formal investigation of the measurement principle of time-of-flight 3-D cameras using correlation of amplitude-modulated continuous-wave signals. These sensors can provide both depth maps and IR intensity pictures simultaneously and in real time. We examine the theory of the data acquisition in detail. The variance of the range measurements is derived in a concise way and we show that the computed range follows an offset normal distribution. The impact of quantization of that distribution is discussed. All theoretically investigated errors like the behavior of the variance, depth bias, saturation and quantization effects are supported by experimental results.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Leila Nagel</style></author><author><style face="normal" font="default" size="100%">Hung, L. -P.</style></author><author><style face="normal" font="default" size="100%">Tsai, W. -T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Thermographic measurements of the temperature difference across the air- water interface: results from experimental and numerical studies</style></title><secondary-title><style face="normal" font="default" size="100%">Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Thermography is a powerful tool for analyzing spatially and temporally resolved the transport of heat across the air-water interface. In this contribution, measurements of the temperature difference across the aqueous boundary layer will be presented. These measurements are based on a statistical analysis of the temperature distribution directly at the water surface. This technique will be presented together with results of measurements conducted in the laboratory and in the field. These thermographic measurements are compared to measurements with standard techniques. Also, the same statistical analysis is performed on data from direct numerical simulation of a wind-driven, aqueous turbulent boundary-layer flow. The agreement between thermography and simulation is very good. Moreover, the full 3D simulations are used for a thorough analysis and validation of the thermographic technique and help to discuss the results.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TomoPIV meets Compressed Sensing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9760</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TomoPIV meets Compressed Sensing</style></title><secondary-title><style face="normal" font="default" size="100%">Pure Math.~Appl.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.mat.unisi.it/newsito/puma/public_html/contents.php</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1-2</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">49 -- 76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TomoPIV meets Compressed Sensing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">August</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9760</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Technical Report</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TomoPIV meets Compressed Sensing</style></title><secondary-title><style face="normal" font="default" size="100%">Pure Math. Appl.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.mat.unisi.it/newsito/puma/public_html/contents.php</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1-2</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">49 – 76</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gianniotis, N.</style></author><author><style face="normal" font="default" size="100%">Ti&amp;ntilde;o, P.</style></author><author><style face="normal" font="default" size="100%">Spreckley, S.</style></author><author><style face="normal" font="default" size="100%">Raychaudhury, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Topographic Mapping of Astronomical Light Curves via a Physically
Inspired Probabilistic Model</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of ICANN 2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">567-576</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, Jing.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Steidl, Gabriele</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Tai, X.-C.</style></author><author><style face="normal" font="default" size="100%">Mórken, K.</style></author><author><style face="normal" font="default" size="100%">Lie, K.-A.</style></author><author><style face="normal" font="default" size="100%">Lysaker, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Total-Variation Based Piecewise Affine Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space and Variational Methods in Computer Vision (SSVM 2009)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5567</style></volume><pages><style face="normal" font="default" size="100%">552-564</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, Jing.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Steidl, Gabriele</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Tai, X.-C.</style></author><author><style face="normal" font="default" size="100%">Mórken, K.</style></author><author><style face="normal" font="default" size="100%">Lysaker, M.</style></author><author><style face="normal" font="default" size="100%">Lie, K.-A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Total-Variation Based Piecewise Affine Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space and Variational Methods in Computer Vision (SSVM 2009)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5567</style></volume><pages><style face="normal" font="default" size="100%">552-564</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yarlagadda, P.</style></author><author><style face="normal" font="default" size="100%">Monroy, A.</style></author><author><style face="normal" font="default" size="100%">Bernd Carque</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards a Computer-based Understanding of Medieval Images</style></title><secondary-title><style face="normal" font="default" size="100%">Scientific Computing &amp; Cultural Heritage</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007%2F978-3-642-28021-4_10#page-1</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">89--97</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Amstalden, E. R.</style></author><author><style face="normal" font="default" size="100%">Glunde, K.</style></author><author><style face="normal" font="default" size="100%">Heeren, R. M. A.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Digital Staining using Imaging Mass Spectrometry and Random Forests</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Proteome Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">3558-3567</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Schwarzkopf, Patrick</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transparency for Industrial Cameras and Sensors</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.gitverlag.com/de/print/4/18/issues/2009/3381.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Flatow, F.</style></author><author><style face="normal" font="default" size="100%">Klinger, M.</style></author><author><style face="normal" font="default" size="100%">Schepanski, K.</style></author><author><style face="normal" font="default" size="100%">Tegen, I.</style></author><author><style face="normal" font="default" size="100%">Rannacher, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transport of dust across the Sahara from satellite image sequence analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Eos Transactions</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">90</style></volume><pages><style face="normal" font="default" size="100%">EP21A-0565</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">52</style></issue><notes><style face="normal" font="default" size="100%">Fall Meet. Suppl.</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Flatow, F.</style></author><author><style face="normal" font="default" size="100%">Klinger, M.</style></author><author><style face="normal" font="default" size="100%">Schepanski, K.</style></author><author><style face="normal" font="default" size="100%">Tegen, I.</style></author><author><style face="normal" font="default" size="100%">Rannacher, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transport of Sahara dust into the Atlantic Ocean from satellite image sequence analysis</style></title><secondary-title><style face="normal" font="default" size="100%">SOLAS Open Science Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">27</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this contribution, image processing techniques in conjunction with efficient numerical solvers will be used for measuring the transport of atmospheric dust originating in the Sahara to the Atlantic Ocean. Mineral dust aerosols are an important component of the Earth&#039;s climate system through a number of direct and indirect influences, and have a strong effect on terrestrial and oceanic biogeochemical cycles. We use Meteosat Second Generation (MSG) infra-red (IR) difference dust index images for visualizing dust plumes in the atmosphere. For every 15-minute scan the dust index is computed basing on the difference of the brightness temperatures measured by the Spinning enhanced Visible and Infra-Red Imager (SEVIRI) at the wavelengths centred at 8.7 m, 10.8 m and 12.0 m. Sinks and sources of the plumes are detected and trajectories computed. Newly developed techniques for computing optical flows and transport parameters are employed. Through the estimation of these transport parameters, dust deposition in the ocean can be quantified. We will present results for a wide range of events, highlighting the capabilities of our approach.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vlasenko, A.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Nitsche</style></author><author><style face="normal" font="default" size="100%">C. Dobriloff</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Approaches for Model-Based PIV and Visual Fluid Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Imaging Measurement Methods for Flow Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">247-256</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Notes on Numerical Fluid Mechanics and Multidisciplinary Design</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vlasenko, A.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Nitsche, W.</style></author><author><style face="normal" font="default" size="100%">C. Dobriloff</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Approaches for Model-Based PIV and Visual Fluid Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Imaging Measurement Methods for Flow Analysis</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Notes on Numerical Fluid Mechanics and Multidisciplinary Design</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">247-256</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Correlation and Decomposition Methods for Particle Image Velocimetry</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9766/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Heidelberg University, Faculty of Mathematics and Computer Sciences</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phddoctoral thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gianniotis, N.</style></author><author><style face="normal" font="default" size="100%">Ti&amp;ntilde;o, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualization of Structured Data via Generative Probabilistic Modeling</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5400</style></volume><pages><style face="normal" font="default" size="100%">118-137</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Berthe</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">U. Ketzscher</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Nitsche</style></author><author><style face="normal" font="default" size="100%">C. Dobriloff</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The wall PIV measurement technique for near wall flow fields in biofliud mechanics</style></title><secondary-title><style face="normal" font="default" size="100%">Imaging Measurement Methods for Flow Analysis, Results of the DFG Priority Programme 1147 Imaging Measurement Methods for Flow Analysis 2003-2009</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">11--20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This chapter describes the development of a new time resolved 3D PIV technique for near wall flow field measurements. This measurement technique, called wall-PIV, is based on Beer-Lambert&#039;s law. It substitutes the classical PIV laser sheet by a diffuse, monochromatic full-field illumination that is limited to the near wall region by an absorbing molecular dye in the fluid. Aimed range of applications is the investigation of flow fields next to one- or two dimensionally curved, possibly flexing surfaces. The three dimensional three component flow estimation uses a new optical flow algorithm, based on particle trajectories. Results of the measurement technique&#039;s application on a displacement pediatric blood pump are presented.</style></abstract><custom3><style face="normal" font="default" size="100%">Notes on Numerical Fluid Mechanics and Multidisciplinary Design</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nguyen, Minh Hoai</style></author><author><style face="normal" font="default" size="100%">Torresani, Lorenzo</style></author><author><style face="normal" font="default" size="100%">De La Torre, Fernando</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weakly supervised discriminative localization and classification: A joint learning process</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">1925–1932</style></pages><isbn><style face="normal" font="default" size="100%">9781424444205</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g. in terms of window size and location) which may be suboptimal for classification. In this paper we propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach simultaneously localizes the instances of the positive class and learns a subwindow SVM to recognize them. We extend our method to classification of time series by presenting an algorithm that localizes the most discriminative set of temporal segments in the signal. We evaluate our approach on several datasets for object and action recognition and show that it achieves results similar and in many cases superior to those obtained with full supervision. ©2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nguyen, Minh Hoai</style></author><author><style face="normal" font="default" size="100%">Torresani, Lorenzo</style></author><author><style face="normal" font="default" size="100%">De La Torre, Fernando</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weakly supervised discriminative localization and classification: A joint learning process</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><pages><style face="normal" font="default" size="100%">1925–1932</style></pages><isbn><style face="normal" font="default" size="100%">9781424444205</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Visual categorization problems, such as object classification or action recognition, are increasingly often approached using a detection strategy: a classifier function is first applied to candidate subwindows of the image or the video, and then the maximum classifier score is used for class decision. Traditionally, the subwindow classifiers are trained on a large collection of examples manually annotated with masks or bounding boxes. The reliance on time-consuming human labeling effectively limits the application of these methods to problems involving very few categories. Furthermore, the human selection of the masks introduces arbitrary biases (e.g. in terms of window size and location) which may be suboptimal for classification. In this paper we propose a novel method for learning a discriminative subwindow classifier from examples annotated with binary labels indicating the presence of an object or action of interest, but not its location. During training, our approach simultaneously localizes the instances of the positive class and learns a subwindow SVM to recognize them. We extend our method to classification of time series by presenting an algorithm that localizes the most discriminative set of temporal segments in the signal. We evaluate our approach on several datasets for object and action recognition and show that it achieves results similar and in many cases superior to those obtained with full supervision. ©2009 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Monigatti, F.</style></author><author><style face="normal" font="default" size="100%">Ivanov, A. R.</style></author><author><style face="normal" font="default" size="100%">Rappsilber, J.</style></author><author><style face="normal" font="default" size="100%">Winter, D.</style></author><author><style face="normal" font="default" size="100%">Judith A. J. Steen</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">When Less Can Yield More - Computational Preprocessing of MS/MS Spectra for Peptide Identification Preprocessing</style></title><secondary-title><style face="normal" font="default" size="100%">Proteomics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">4978-4984</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Banerjee</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">S. Schabel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An active thermographic technique for highly resolved heat transport measurements in paper drying</style></title><secondary-title><style face="normal" font="default" size="100%">APPITA Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://search.informit.com.au/documentSummary;dn=113990239180698;res=IELHSS</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">61</style></volume><pages><style face="normal" font="default" size="100%">244--249</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A novel measurement technique, based on infrared thermography, has been applied for characterizing the thermal properties of paper with high spatial resolution. The technique is referred to as &#039;active thermography&#039; since the temperature response of a paper sheet is observed and analysed with respect to an external periodic heating. Through the analysis of the temperature response of the paper surface in the Fourier domain for different modulation frequencies of incident heat flux, heat transfer velocities across the solid-gas interface can be estimated. This in turn results in estimation of the heat capacity of paper by solving the heat balance equations of the system. The thermal heating is applied spatially homogeneously. Therefore all these calculations are performed for all pixels in the image sequence. The spatial distribution and temporal development of all these parameters were visualized. The technique shows high potential for non-invasive dynamic measurements in paper drying.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Voss</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Alignment and Retention Time Correction of LC-MS Data in Proteomics</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pavel Pavlov</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of Motion in Scale Space</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/9378</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This work includes some new aspects of motion estimation by the optic flow method in scale spaces. The usual techniques for motion estimation are limited to the application of coarse to fine strategies. The coarse to fine strategies can be successful only if there is enough information in every scale. In this work we investigate the motion estimation in the scale space more basically. The wavelet choice for scale space decomposition of image sequences is discussed in the first part of this work. We make use of the continuous wavelet transform with rotationally symmetric wavelets. Bandpass decomposed sequences allow the replacement of the structure tensor by the phase invariant energy operator. The structure tensor is computationally more expensive because of its spatial or spatio-temporal averaging. The energy operator needs in general no further averaging. The numerical accuracy of the motion estimation with the energy operator is compared to the results of usual techniques, based on the structure tensor. The comparison tests are performed on synthetic and real life sequences. Another practical contribution is the accuracy measurement for motion estimation by adaptive smoothed tensor fields. The adaptive smoothing relies on nonlinear anisotropic diffusion with discontinuity and curvature preservation. We reached an accuracy gain under properly chosen parameters for the diffusion filter. A theoretical contribution from mathematical point of view is a new discontinuity and curvature preserving regularization for motion estimation. The convergence of solutions for the isotropic case of the nonlocal partial differential equation is shown. For large displacements between two consecutive frames the optic flow method is systematically corrupted because of the violence of the sampling theorem. We developed a new method for motion analysis by scale decomposition, which allows to circumvent the systematic corruption without using the coarse to fine strategy. The underlying assumption is, that in a certain neighborhood the grey value undergoes the same displacement. If this is fulfilled, then the same optic flow should be measured in all scales. If there arise inconsistencies in a pixel across the scale space, so they can be detected and the scales containing this inconsistencies are not taken into account.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of Spectral Data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Steffen Haschler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufbau eines Praktikumsversuch zum Gasaustauch zwischen Atmosphäre und Ozean</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gehler, Peter Vincent</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author><author><style face="normal" font="default" size="100%">Minka, Tom</style></author><author><style face="normal" font="default" size="100%">Sharp, Toby</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian color constancy revisited</style></title><secondary-title><style face="normal" font="default" size="100%">26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><isbn><style face="normal" font="default" size="100%">9781424422432</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Computational color constancy is the task of estimating the true reflectances of visible surfaces in an image. In this paper we follow a line of research that assumes uniform illumination of a scene, and that the principal step in estimating reflectances is the estimation of the scene illuminant. We review recent approaches to illuminant estimation, firstly those based on formulae for normalisation of the reflectance distribution in an image - so-called grey-world algorithms, and those based on a Bayesian formulation of image formation. In evaluating these previous approaches we introduce a new tool in the form of a database of 568 high-quality, indoor and outdoor images, accurately labelled with illuminant, and preserved in their raw form, free of correction or normalisation. This has enabled us to establish several properties experimentally. Firstly automatic selection of grey-world algorithms according to image properties is not nearly so effective as has been thought. Secondly, it is shown that Bayesian illuminant estimation is significantly improved by the improved accuracy of priors for illuminant and reflectance that are obtained from the new dataset. ©2008 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildverarbeitung verlangt nach Fortschritt - Forschung und berufliche Weiterbildung werden zum A und O</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sensorreport.ch/sen_Inhalt_5_08.ebs</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Szeliski, Richard</style></author><author><style face="normal" font="default" size="100%">Zabih, Ramin</style></author><author><style face="normal" font="default" size="100%">Scharstein, Daniel</style></author><author><style face="normal" font="default" size="100%">Veksler, Olga</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Agarwala, Aseem</style></author><author><style face="normal" font="default" size="100%">Tappen, Marshall</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comparative study of energy minimization methods for Markov random fields with smoothness-based priors</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Belief propagation</style></keyword><keyword><style  face="normal" font="default" size="100%">Global optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Graph Cuts</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">Performance evaluation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://vision.middlebury.edu/MRF.</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">1068–1080</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting energy minimization problems have been widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods. However, the tradeoffs among different energy minimization algorithms are still not well understood. In this paper we describe a set of energy minimization benchmarks and use them to compare the solution quality and running time of several common energy minimization algorithms. We investigate three promising recent methods graph cuts, LBP, and tree-reweighted message passing in addition to the well-known older iterated conditional modes (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching, interactive segmentation, and denoising. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods. Benchmarks, code, images, and results are available at http://vision.middlebury.edu/ MRF/. © 2008 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Szeliski, Richard</style></author><author><style face="normal" font="default" size="100%">Zabih, Ramin</style></author><author><style face="normal" font="default" size="100%">Scharstein, Daniel</style></author><author><style face="normal" font="default" size="100%">Veksler, Olga</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Agarwala, Aseem</style></author><author><style face="normal" font="default" size="100%">Tappen, Marshall</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comparative study of energy minimization methods for Markov random fields with smoothness-based priors</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Belief propagation</style></keyword><keyword><style  face="normal" font="default" size="100%">Global optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Graph Cuts</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">Performance evaluation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">jun</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">1068–1080</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Among the most exciting advances in early vision has been the development of efficient energy minimization algorithms for pixel-labeling tasks such as depth or texture computation. It has been known for decades that such problems can be elegantly expressed as Markov random fields, yet the resulting energy minimization problems have been widely viewed as intractable. Recently, algorithms such as graph cuts and loopy belief propagation (LBP) have proven to be very powerful: for example, such methods form the basis for almost all the top-performing stereo methods. However, the tradeoffs among different energy minimization algorithms are still not well understood. In this paper we describe a set of energy minimization benchmarks and use them to compare the solution quality and running time of several common energy minimization algorithms. We investigate three promising recent methods graph cuts, LBP, and tree-reweighted message passing in addition to the well-known older iterated conditional modes (ICM) algorithm. Our benchmark problems are drawn from published energy functions used for stereo, image stitching, interactive segmentation, and denoising. We also provide a general-purpose software interface that allows vision researchers to easily switch between optimization methods. Benchmarks, code, images, and results are available at http://vision.middlebury.edu/ MRF/. © 2008 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreas Haja</style></author><author><style face="normal" font="default" size="100%">Steffen Abraham</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gerhard Rigoll</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Comparison of Region Detectors for Tracking</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proceedings 30th DAGM Symposium, Munich, Germany, June 2008</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">112--121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work, the performance of five popular region detectors is compared in the context of tracking. Firstly, conventional nearest-neighbor matching based on the similarity of region descriptors is used to assemble trajectories from unique region-to-region correspondences. Based on carefully estimated homographies between planar object surfaces in neighboring frames of an image sequence, both their localization accuracy and length, as well as the percentage of successfully tracked regions is evaluated and compared. The evaluation results serve as a supplement to existing studies and facilitate the selection of appropriate detectors suited to the requirements of a specific application. Secondly, a novel tracking method is presented, which integrates for each region all potential matches into directed multi-edge graphs. From these, trajectories are extracted using Dijkstra&#039;s algorithm. It is shown, that the resulting localization error is significantly lower than with nearest-neighbor matching while at the same time, the percentage of tracked regions is increased.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanselmann, M.</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Amstalden, E. R.</style></author><author><style face="normal" font="default" size="100%">Glunde, K.</style></author><author><style face="normal" font="default" size="100%">Heeren, R. M. A.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Concise Representation of MS Images by Probabilistic Latent Semantic Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Analytical Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">24</style></number><volume><style face="normal" font="default" size="100%">80</style></volume><pages><style face="normal" font="default" size="100%">9649-9658</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fundana, Ketut</style></author><author><style face="normal" font="default" size="100%">Heyden, Anders</style></author><author><style face="normal" font="default" size="100%">Gosch, Christian</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Continuous Graph Cuts for Prior-Based Object Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">19th Int.~Conf.~Patt.~Recog.~(ICPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">1--4</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, Jing</style></author><author><style face="normal" font="default" size="100%">Steidl, Gabriele</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Hodge Decomposition of Image Flows</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 30th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">416--425</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lellmann, J.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/8759/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Decomposition of Quadratric Variational Problems</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 30th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">325--334</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Decomposition of Quadratric Variational Problems</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 30th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">325--334</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Judith A. J. Steen</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author><author><style face="normal" font="default" size="100%">Georgi, A.</style></author><author><style face="normal" font="default" size="100%">Kenneth C. Parker</style></author><author><style face="normal" font="default" size="100%">Springer, M.</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Kirschner, M. W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Different Phosphorylation States of the Anaphase Promoting Complex in Response to Anti-Mitotic Drugs: A Quantitative Proteomic Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the National Academy of Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">16</style></number><volume><style face="normal" font="default" size="100%">105</style></volume><pages><style face="normal" font="default" size="100%">6069-6074</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Scholz</style></author><author><style face="normal" font="default" size="100%">T. Wiersbinski</style></author><author><style face="normal" font="default" size="100%">Paul Ruhnau</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">R. Hain</style></author><author><style face="normal" font="default" size="100%">Volker Beushausen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Double-pulse planar-LIF investigations using fluorescence motion analysis for mixture formation investigation</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">583--593</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A concept for dynamic mixture formation investigations of fuel/air mixtures is presented which can equally be applied to several other laser induced fluorescence (LIF) applications. Double-pulse LIF imaging was used to gain insight into dynamic mixture formation processes. The setup consists of a modified standard PIV setup. The &quot;fuel/air ratio measurement by laser induced fluorescence (FARLIF)&quot; approach is used for a quantification of the LIF images in order to obtain pairs of 2D fuel/air ratio maps. Two different evaluation concepts for LIF double pulse images are discussed. The first is based on the calculation of the temporal derivative field of the fuel/air ratio distribution. The result gives insight into the dynamic mixing process, showing where and how the mixture is changing locally. The second concept uses optical flow methods in order to estimate the motion of fluorescence (i.e., mixture) structures to gain insight into the dynamics, showing the distortion and the motion of the inhomogeneous mixture field. For this &quot;fluorescence motion analysis&quot; (FMA) two different evaluation approaches the &quot;variational gradient based approach&quot; and the &quot;variational cross correlation based approach&quot; are presented. For the validation of both, synthetic LIF image pairs with predefined motion fields were generated. Both methods were applied and the results compared with the known original motion field. This validation shows that FMA yields reliable results even for image pairs with low signal/noise ratio. Here, the &quot;variational gradient based approach&quot; turned out to be the better choice so far. Finally, the experimental combination of double-pulse FARLIF imaging with FMA and simultaneous PIV measurement is demonstrated. The comparison of the FMA motion field and the flow velocity field captured by PIV shows that both results basically reflect complementary information of the flow field. It is shown that the motion field of the fluorescence structures does not (necessarily) need to represent the actual flow velocity and that the flow velocity field alone can not illustrate the structure motion in any case. Therefore, the simultaneous measurement of both gives the deepest insight into the dynamic mixture formation process. The examined concepts and evaluation approaches of this paper can easily be adapted to various other planar LIF methods (with the LIF signal representing, e.g., species concentration, temperature, density etc.) broadening the insight for a wide range of different dynamic processes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Popa, C.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct. 22-Oct. 25</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Ed Acad Romane, Bucuresti</style></publisher><pub-location><style face="normal" font="default" size="100%">Constanta, Romania</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Popa, C.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Ed Acad Romane</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pub-location><style face="normal" font="default" size="100%">Bucharest, Romania</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kerstin E. Richter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ermittlung der Leckraten der WiSSCy-Messkampagne 2007 in Hamburg mittels UV-Spektroskopie</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Claudia Kondermann</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">1-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Claudia Kondermann</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">1--6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Linear inverse problems in computer vision, including motion estimation, shape fitting and image reconstruction, give rise to parameter estimation problems with highly correlated errors in variables. Established total least squares methods estimate the most likely corrections Acirc and bcirc to a given data matrix [A, b] perturbed by additive Gaussian noise, such that there exists a solution y with [A + Acirc, b +bcirc]y = 0. In practice, regression imposes a more restrictive constraint namely the existence of a solution x with [A + Acirc]x = [b + bcirc]. In addition, more complicated correlations arise canonically from the use of linear filters. We, therefore, propose a maximum likelihood estimator for regression in the general case of arbitrary positive definite covariance matrices. We show that Acirc, bcirc and x can be found simultaneously by the unconstrained minimization of a multivariate polynomial which can, in principle, be carried out by means of a Grobner basis. Results for plane fitting and optical flow computation indicate the superiority of the proposed method.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Popa, Constantin</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extended and Constrained Cimmino-type Algorithms with Applications in Tomographic Image Reconstruction</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/8798/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Constantin Popa</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extended and Constrained Cimmino-type Algorithms with Applications in Tomographic Image Reconstruction</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">November</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/8798/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Technical Report</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Torresani, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Feature correspondence via graph matching: Models and global optimization</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">PART 2</style></number><volume><style face="normal" font="default" size="100%">5303 LNCS</style></volume><pages><style face="normal" font="default" size="100%">596–609</style></pages><isbn><style face="normal" font="default" size="100%">3540886850</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter and occlusion, such as points extracted from a pair of images containing a human figure in distinct poses. We formulate this matching task as an energy minimization problem by defining a complex objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general a NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods. © 2008 Springer Berlin Heidelberg.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Torresani, Lorenzo</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Feature correspondence via graph matching: Models and global optimization</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">PART 2</style></number><volume><style face="normal" font="default" size="100%">5303 LNCS</style></volume><pages><style face="normal" font="default" size="100%">596–609</style></pages><isbn><style face="normal" font="default" size="100%">3540886850</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we present a new approach for establishing correspondences between sparse image features related by an unknown non-rigid mapping and corrupted by clutter and occlusion, such as points extracted from a pair of images containing a human figure in distinct poses. We formulate this matching task as an energy minimization problem by defining a complex objective function of the appearance and the spatial arrangement of the features. Optimization of this energy is an instance of graph matching, which is in general a NP-hard problem. We describe a novel graph matching optimization technique, which we refer to as dual decomposition (DD), and demonstrate on a variety of examples that this method outperforms existing graph matching algorithms. In the majority of our examples DD is able to find the global minimum within a minute. The ability to globally optimize the objective allows us to accurately learn the parameters of our matching model from training examples. We show on several matching tasks that our learned model yields results superior to those of state-of-the-art methods. © 2008 Springer Berlin Heidelberg.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Roth, Stefan</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">FusionFlow: Discrete-continuous optimization for optical flow estimation</style></title><secondary-title><style face="normal" font="default" size="100%">26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><isbn><style face="normal" font="default" size="100%">9781424422432</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Accurate estimation of optical flow is a challenging task, which often requires addressing difficult energy optimization problems. To solve them, most top-performing methods rely on continuous optimization algorithms. The modeling accuracy of the energy in this case is often traded for its tractability. This is in contrast to the related problem of narrow-baseline stereo matching, where the top-performing methods employ powerful discrete optimization algorithms such as graph cuts and message-passing to optimize highly non-convex energies. In this paper, we demonstrate how similar non-convex energies can be formulated and optimized discretely in the context of optical flow estimation. Starting with a set of candidate solutions that are produced by fast continuous flow estimation algorithms, the proposed method iteratively fuses these candidate solutions by the computation of minimum cuts on graphs. The obtained continuous-valued fusion result is then further improved using local gradient descent. Experimentally, we demonstrate that the proposed energy is an accurate model and that the proposed discrete-continuous optimization scheme not only finds lower energy solutions than traditional discrete or continuous optimization techniques, but also leads to flow estimates that outperform the current state-of-the-art. ©2008 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vicente, Sara</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graph cut based image segmentation with connectivity priors</style></title><secondary-title><style face="normal" font="default" size="100%">26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><isbn><style face="normal" font="default" size="100%">9781424422432</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Graph cut is a popular technique for interactive image segmentation. However, it has certain shortcomings. In particular, graph cut has problems with segmenting thin elongated objects due to the &quot;shrinking bias&quot;. To overcome this problem, we propose to impose an additional connectivity prior, which is a very natural assumption about objects. We formulate several versions of the connectivity constraint and show that the corresponding optimization problems are all NP-hard. For some of these versions we propose two optimization algorithms: (i) a practical heuristic technique which we call DijkstraGC, and (ii) a slow method based on problem decomposition which provides a lower bound on the problem. We use the second technique to verify that for some practical examples DijkstraGC is able to find the global minimum. ©2008 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreas Haja</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graph-based Spatial Motion Tracking using Affine-covariant Regions</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/8943</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This thesis considers the task of spatial motion reconstruction from image sequences using a stereoscopic camera setup. In a variety of fields, such as flow analysis in physics or the measurement of oscillation characteristics and damping behavior in mechanical engineering, efficient and accurate methods for motion analysis are of great importance. This work discusses each algorithmic step of the motion reconstruction problem using a set of freely available image sequences. The presented concepts and evaluation results are of a generic nature and may thus be applied to a multitude of applications in various fields, where motion can be observed by two calibrated cameras. The first step in the processing chain of a motion reconstruction algorithm is concerned with the automated detection of salient locations (=features or regions) within each image of a given sequence. In this thesis, detection is directly performed on the natural texture of the observed objects instead of using artificial marker elements (as with many currently available methods). As one of the major contributions of this work, five well-known detection methods from the contemporary literature are compared to each other with regard to several performance measures, such as localization accuracy or the robustness under perspective distortions. The given results extend the available literature on the topic and facilitate the well-founded selection of appropriate detectors according to the requirements of specific target applications. In the second step, both spatial and temporal correspondences have to be established between features extracted from different images. With the former, two images taken at the same time instant but with different cameras are considered (stereo reconstruction) while with the latter, correspondences are sought between temporally adjacent images from the same camera instead (monocular feature tracking). With most classical methods, an observed object is either spatially reconstructed at a single time instant yielding a set of three-dimensional coordinates, or its motion is analyzed separately within each camera yielding a set of two-dimensional trajectories. A major contribution of this thesis is a concept for the unification of both stereo reconstruction and monocular tracking. Based on sets of two-dimensional trajectories from each camera of a stereo setup, the proposed method uses a graph-based approach to find correspondences not between single features but between entire trajectories instead. Thereby, the influence of locally ambiguous correspondences is mitigated significantly. The resulting spatial trajectories contain both the three-dimensional structure and the motion of the observed objects at the same time. To the best knowledge of the author, a similar concept does not yet exist in the literature. In a detailed evaluation, the superiority of the new method is demonstrated.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Rav-Acha, Alex</style></author><author><style face="normal" font="default" size="100%">Sharp, Toby</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High resolution matting via interactive trimap segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><isbn><style face="normal" font="default" size="100%">9781424422432</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques. ©2008 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Rav-Acha, Alex</style></author><author><style face="normal" font="default" size="100%">Sharp, Toby</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High resolution matting via interactive trimap segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><isbn><style face="normal" font="default" size="100%">9781424422432</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques. ©2008 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Rav-Acha, Alex</style></author><author><style face="normal" font="default" size="100%">Sharp, Toby</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High resolution matting via interactive trimap segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><isbn><style face="normal" font="default" size="100%">9781424422432</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new approach to the matting problem which splits the task into two steps: interactive trimap extraction followed by trimap-based alpha matting. By doing so we gain considerably in terms of speed and quality and are able to deal with high resolution images. This paper has three contributions: (i) a new trimap segmentation method using parametric max-flow; (ii) an alpha matting technique for high resolution images with a new gradient preserving prior on alpha; (iii) a database of 27 ground truth alpha mattes of still objects, which is considerably larger than previous databases and also of higher quality. The database is used to train our system and to validate that both our trimap extraction and our matting method improve on state-of-the-art techniques. ©2008 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">How to perform camera measurements according to the EMVA 1288 Standard.</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image segmentation by branch-and-mincut</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">PART 4</style></number><volume><style face="normal" font="default" size="100%">5305 LNCS</style></volume><pages><style face="normal" font="default" size="100%">15–29</style></pages><isbn><style face="normal" font="default" size="100%">3540886923</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Efficient global optimization techniques such as graph cut exist for energies corresponding to binary image segmentation from low-level cues. However, introducing a high-level prior such as a shape prior or a color-distribution prior into the segmentation process typically results in an energy that is much harder to optimize. The main contribution of the paper is a new global optimization framework for a wide class of such energies. The framework is built upon two powerful techniques: graph cut and branch-and-bound. These techniques are unified through the derivation of lower bounds on the energies. Being computable via graph cut, these bounds are used to prune branches within a branch-and-bound search. We demonstrate that the new framework can compute globally optimal segmentations for a variety of segmentation scenarios in a reasonable time on a modern CPU. These scenarios include unsupervised segmentation of an object undergoing 3D pose change, category-specific shape segmentation, and the segmentation under intensity/color priors defined by Chan-Vese and GrabCut functionals. © 2008 Springer Berlin Heidelberg.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image segmentation by branch-and-mincut</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">PART 4</style></number><volume><style face="normal" font="default" size="100%">5305 LNCS</style></volume><pages><style face="normal" font="default" size="100%">15–29</style></pages><isbn><style face="normal" font="default" size="100%">3540886923</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Efficient global optimization techniques such as graph cut exist for energies corresponding to binary image segmentation from low-level cues. However, introducing a high-level prior such as a shape prior or a color-distribution prior into the segmentation process typically results in an energy that is much harder to optimize. The main contribution of the paper is a new global optimization framework for a wide class of such energies. The framework is built upon two powerful techniques: graph cut and branch-and-bound. These techniques are unified through the derivation of lower bounds on the energies. Being computable via graph cut, these bounds are used to prune branches within a branch-and-bound search. We demonstrate that the new framework can compute globally optimal segmentations for a variety of segmentation scenarios in a reasonable time on a modern CPU. These scenarios include unsupervised segmentation of an object undergoing 3D pose change, category-specific shape segmentation, and the segmentation under intensity/color priors defined by Chan-Vese and GrabCut functionals. © 2008 Springer Berlin Heidelberg.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rhemann, Christoph</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Gelautz, Margrit</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving color modeling for alpha matting</style></title><secondary-title><style face="normal" font="default" size="100%">BMVC 2008 - Proceedings of the British Machine Vision Conference 2008</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper addresses the problem of extracting an alpha matte from a single photograph given a user-defined trimap. A crucial part of this task is the color modeling step where for each pixel the optimal alpha value, together with its confidence, is estimated individually. This forms the data term of the objective function. It comprises of three steps: (i) Collecting a candidate set of potential fore- and background colors; (ii) Selecting high confidence samples from the candidate set; (iii) Estimating a sparsity prior to remove blurry artifacts. We introduce novel ideas for each of these steps and show that our approach considerably improves over state-of-the-art techniques by evaluating it on a large database of 54 images with known high-quality ground truth.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jens F. Acker</style></author><author><style face="normal" font="default" size="100%">Benjamin Berkels</style></author><author><style face="normal" font="default" size="100%">Kristian Bredies</style></author><author><style face="normal" font="default" size="100%">Mamadou S. Diallo</style></author><author><style face="normal" font="default" size="100%">Marc Droske</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Matthias Holschneider</style></author><author><style face="normal" font="default" size="100%">Jaroslav Hron</style></author><author><style face="normal" font="default" size="100%">Claudia Kondermann</style></author><author><style face="normal" font="default" size="100%">Michail Kulesh</style></author><author><style face="normal" font="default" size="100%">Peter Maass</style></author><author><style face="normal" font="default" size="100%">Nadine Olischläger</style></author><author><style face="normal" font="default" size="100%">Heinz-Otto Peitgen</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">R. Dahlhaus</style></author><author><style face="normal" font="default" size="100%">J. Kurths</style></author><author><style face="normal" font="default" size="100%">Timmer, J.</style></author><author><style face="normal" font="default" size="100%">Peter Maass</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Inverse Problems and Parameter Identification in Image Processing</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical Methods in Time Series Analysis and Digital Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">111--151</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many problems in imaging are actually inverse problems. One reason for this is that conditions and parameters of the physical processes underlying the actual image acquisition are usually not known. Examples for this are the inhomogeneities of the magnetic field in magnetic resonance imaging (MRI) leading to nonlinear deformations of the anatomic structures in the recorded images, material parameters in geological structures as unknown parameters for the simulation of seismic wave propagation with sparse measurement on the surface, or temporal changes in movie sequences given by intensity changes or moving image edges and resulting from deformation, growth and transport processes with unknown fluxes. The underlying physics is mathematically described in terms of variational problems or evolution processes. Hence, solutions of the forward problem are naturally described by partial differential equations. These forward models are reflected by the corresponding inverse problems as well. Beyond these concrete, direct modeling links to continuum mechanics abstract concepts from physical modeling are successfully picked up to solve general perceptual problems in imaging. Examples are visually intuitive methods to blend between images showing multiscale structures at different resolution or methods for the analysis of flow fields.</style></abstract><custom3><style face="normal" font="default" size="100%">Understanding Complex Systems</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Kameraauswahl nach objektiven Kriterien - Der EMVA1288 Kamerastandard</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.gitverlag.com/de/print/4/18/issues/2008/3039.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andreas Haja</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Steffen Abraham</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Localization accuracy of region detectors</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings CVPR&#039;08</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MAP-Inference for Highly-Connected Graphs with DC-Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 30th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">1--10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kappes, J. H.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MAP-Inference for Highly-Connected Graphs with DC-Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition – 30th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">1–10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring momentum transport directly at the air water interface from active infrared thermography</style></title><secondary-title><style face="normal" font="default" size="100%">Geophysical Research Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Staudacher, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Görlitz, L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Method for processing an intensity image of a microscope</style></title><secondary-title><style face="normal" font="default" size="100%">Patent, Patent Number: WO2008034721A1</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karsten Roetmann</style></author><author><style face="normal" font="default" size="100%">Waldemar Schmunk</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Volker Beushausen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Micro-flow analysis by molecular tagging velocimetry and planar raman-scattering</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">419--430</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The two dimensional Molecular Tagging Velocimetry (2D-MTV) was used to measure velocity fields of the flow in a micro mixer. Instead of commonly used micro particles an optical tagging of the flow was performed by using a caged dye. The flow induces a deformation of the optically written pattern that can be tracked by laser induced fluorescence. The series of raw images gained this way were analyzed quantitatively with a novel optical flow based technique. Reference measurements have been carried out allowing to draw conclusions about the accuracy of this procedure. A comparison to the standard technique of uPIV was also conducted. Apart from measuring flow velocities in microfluidic mixing processes, the spatial distribution of concentration fields for different species were also measured. To this end, a new technique has been developed that allows spatial measurements from Planar Spontaneous Raman Scattering (PSRS). The Raman stray light of the relevant species was spectrally selected by a narrow bandpass filter and thus detected unaffectedly by the Raman stray light of other species. The successful operation of this measurement procedure in micro flows will be demonstrated exemplary for a mixing process of water and ethanol.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">M.-A. Weber</style></author><author><style face="normal" font="default" size="100%">Bachert, P.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mimicking the human expert: pattern recognition for an automated assessment of data quality in MRSI</style></title><secondary-title><style face="normal" font="default" size="100%">Magnetic Resonance in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">59</style></volume><pages><style face="normal" font="default" size="100%">1457-1466</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Enzweiler</style></author><author><style face="normal" font="default" size="100%">Dariu M. Gavrila</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Mixed Generative-Discriminative Framework for Pedestrian Classification</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Int. Conf. Comp. Vision and Patt. Recog. (CVPR)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Enzweiler</style></author><author><style face="normal" font="default" size="100%">Dariu M. Gavrila</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Monocular Pedestrian Detection: Survey and Experiments</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence, available online: IEEE Computer Society Digital Library, http://doi.ieeecomputersociety.org/10.1109/TPAMI.2008.260</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Enzweiler</style></author><author><style face="normal" font="default" size="100%">Pascal Kanter</style></author><author><style face="normal" font="default" size="100%">Dariu M. Gavrila</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Monocular Pedestrian Recognition Using Motion Parallax</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE Symposium on Intelligent Vehicles</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">792-797</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Claudia Kondermann</style></author><author><style face="normal" font="default" size="100%">A. Berthe</style></author><author><style face="normal" font="default" size="100%">U. Kertzscher</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Motion Estimation Based on a Temporal Model of Fluid Flows</style></title><secondary-title><style face="normal" font="default" size="100%">13th International Symposium on Flow Visualization</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">1-10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author><author><style face="normal" font="default" size="100%">Judith A. J. Steen</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">NITPICK: Peak Identification for Mass Spectrometry Data</style></title><secondary-title><style face="normal" font="default" size="100%">BMC Bioinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">355</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Kai Krajsek</style></author><author><style face="normal" font="default" size="100%">Pavel Pavlov</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Matthias Mühlich</style></author><author><style face="normal" font="default" size="100%">Ingo Stuke</style></author><author><style face="normal" font="default" size="100%">Cicero Mota</style></author><author><style face="normal" font="default" size="100%">Martin Böhme</style></author><author><style face="normal" font="default" size="100%">Martin Haker</style></author><author><style face="normal" font="default" size="100%">Schuchert, T.</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Til Aach</style></author><author><style face="normal" font="default" size="100%">Erhardt Barth</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">R. Dahlhaus</style></author><author><style face="normal" font="default" size="100%">J. Kurths</style></author><author><style face="normal" font="default" size="100%">Timmer, J.</style></author><author><style face="normal" font="default" size="100%">Peter Maass</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonlinear Analysis of Multi-Dimensional Signals</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical Methods in Signal Processing and Digital Image Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">231-288</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">Understanding Complex Systems</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Kai Krajsek</style></author><author><style face="normal" font="default" size="100%">Pavel Pavlov</style></author><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Matthias Mühlich</style></author><author><style face="normal" font="default" size="100%">Ingo Stuke</style></author><author><style face="normal" font="default" size="100%">Cicero Mota</style></author><author><style face="normal" font="default" size="100%">Martin Böhme</style></author><author><style face="normal" font="default" size="100%">Martin Haker</style></author><author><style face="normal" font="default" size="100%">Tobias Schucher</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Til Aach</style></author><author><style face="normal" font="default" size="100%">Erhardt Barth</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">R. Dahlhaus</style></author><author><style face="normal" font="default" size="100%">J. Kurths</style></author><author><style face="normal" font="default" size="100%">Timmer, J.</style></author><author><style face="normal" font="default" size="100%">Peter Maass</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonlinear analysis of multi-dimensional signals: local adaptive estimation of complex motion and orientation patterns</style></title><secondary-title><style face="normal" font="default" size="100%">Mathematical Methods in Time Series Analysis and Digital Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">231-288</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the general task of accurately detecting and quantifying orientations in n-dimensional signals s. The main emphasis will be placed on the estimation of motion, which can be thought of as orientation in spatiotemporal signals. Associated problems such as the optimization of matched kernels for deriving isotropic and highly accurate gradients from the signals, optimal integration of local models, and local model selection will also be addressed.</style></abstract><custom3><style face="normal" font="default" size="100%">Understanding Complex Systems</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel method for three-dimensional three-component analysis of flow close to free water surfaces</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">469--480</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Initial effort is made to establish a new technique for the measurement of three-dimensional three-component (3D3C) velocity fields close to free water surfaces. A fluid volume is illuminated by light emitting diodes (LEDs) perpendicularly to the surface. Small spherical particles are added to the fluid, functioning as a tracer. A monochromatic camera pointing to the water surface from above records the image sequences. The distance of the spheres to the surface is coded by means of a supplemented dye, which absorbs the light of the LEDs according to Beer-Lambert&#039;s law. By applying LEDs with two different wavelengths, it is possible to use particles variable in size. The velocity vectors are obtained by using an extension of the method of optical flow. The vertical velocity component is computed from the temporal brightness change. The setup is validated with a laminar falling film, which serves as a reference flow. Moreover, the method is applied to buoyant convective turbulence as an example for a non stationary, inherently 3D flow.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Griessinger, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Oject Detection with Generic Features: An Application to STED Microscopy</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Karsten Roetmann</style></author><author><style face="normal" font="default" size="100%">Volker Beushausen</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An optical flow MTV based technique for measuring microfluidic flow in the presence of diffusion and Taylor dispersion</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">439--450</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A novel technique is presented for accurately measuring flow fields in microfluidic flows from molecular tagging velocimetry (MTV). Limited optical access is frequently encountered in microfluidic systems. Therefore, in this contribution we analyze the special case of tagging a line across the thin dimension of a microchannel and subsequent imaging along this line. This represents a set-up that is applicable to a wide range of microfluidic applications. A volume illumination has to be used, resulting in an integration of the visualized dye across the flow profile. This leads to the well-known effect of Taylor dispersion. Our novel technique consists of measuring motion from digital image sequences in a gradient-based approach. A motion model is developed which explicitly deals with brightness changes due to Taylor dispersion and additional molecular diffusion of dyes. The presented approach is specific to the case of a parabolic velocity profile. In the presence of these effects, an accurate computation of motion is only possible by applying this novel motion model. Our technique is tested on simulated sequences corrupted with varying levels of noise and on actual measurements. Measurements were conducted in a microfluidic mixer of precisely known flow properties. In the same mixer, a comparative study of our MTV technique to uPIV was performed. Also, the results were compared to bulk measurements of the fluid flow velocity. The novel algorithm compared favorably and also, measurements were conducted on inhomogeneous flow configurations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Klappstein, Jens</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical-Flow based Detection of Moving Objects in Traffic Scenes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/8591/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Traffic is increasing continuously. Nevertheless the number of traffic fatalities decreased in the past. One reason for this are the passive safety systems, such as side crash protection or airbag, which have been engineered the last decades and which are standard in today&#039;s cars. Active safety systems are increasingly developed. They are able to avoid or at least to mitigate accidents. For example, the adaptive cruise control (ACC) original designed as a comfort system is developed towards an emergency brake system. Active safety requires sensors perceiving the vehicle environment. ACC uses radar or laser scanner. However, cameras are also interesting sensors as they are capable of processing visual information such as traffic signs or lane markings. In traffic moving objects (cars, bicyclists, pedestrians) play an important role. To perceive them is essential for active safety systems. This thesis deals with the detection of moving objects utilizing a monocular camera. The detection is based on the motions within the video stream (optical flow). If the ego-motion and the location of the camera with respect to the road plane are known the viewed scene can be 3D reconstructed exploiting the measured optical flow. In this thesis an overview of existing algorithms estimating the ego-motion is given. Based on it a suitable algorithm is selected and extended by a motion model. The latter one considerably increases the accuracy as well as the robustness of the estimate. The location of the camera with respect to the road plane is estimated using the optical flow on the road. The road might be temporary low-textured making it hard to measure the optical flow. Consequently, the road homography estimate will be poor. A novel Kalman filtering approach combining the estimate of the ego-motion and the estimate of the road homography leads to far better results. The 3D reconstruction of the viewed scene is performed pointwise for each measured optical flow vector. A point is reconstructed through intersection of the viewing rays which are determined by the optical flow vector. This only yields a correct result for static, i.e. non-moving, points. Further, static points fulfill four constraints: epipolar constraint, trifocal constraint, positive depth constraint, and positive height constraint. If at least one constraint is violated the point is moving. For the first time an error metric is developed exploiting all four constraints. It measures the deviation from the constraints quantitatively in a unified manner. Based on this error metric the detection limits are investigated. It is shown that overtaking objects are detected very well whereas objects being overtaken are detected hardly. Oncoming objects on a straight road are not detected by means of the available constraints. Only if one assumes that these objects are opaque and touch the ground the detection becomes feasible. An appropriate heuristic is introduced. In conclusion, the developed algorithms are a system to detect moving points robustly. The problem of clustering the detected moving points to objects is outlined. It serves as a starting point for further research activities.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Mirko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optische Methoden zur Form- und Positionserkennung von Körpern in Werkzeugmaschinen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Fried­rich-Schil­ler-Uni­ver­si­tät Je­na</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kohli, Pushmeet</style></author><author><style face="normal" font="default" size="100%">Shekhovtsov, Alexander</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Torr, Philip</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On partial optimality in multi-label MRFs</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 25th International Conference on Machine Learning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">480–487</style></pages><isbn><style face="normal" font="default" size="100%">9781605582054</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the problem of optimizing multilabel MRFs, which is in general NP-hard and ubiquitous in low-level computer vision. One approach for its solution is to formulate it as an integer linear programming and relax the integrality constraints. The approach we consider in this paper is to first convert the multi-label MRF into an equivalent binary-label MRF and then to relax it. The resulting relaxation can be efficiently solved using a maximum flow algorithm. Its solution provides us with a partially optimal labelling of the binary variables. This partial labelling is then easily transferred to the multi-label problem. We study the theoretical properties of the new relaxation and compare it with the standard one. Specifically, we compare tightness, and characterize a subclass of problems where the two relaxations coincide. We propose several combined algorithms based on the technique and demonstrate their performance on challenging computer vision problems. Copyright 2008 by the author(s)/owner(s).</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sauer, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pattern Recognition on Statistically Textured Surfaces</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Boppel, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Peak Identification for Liquid Chromatography and Mass Spectrometry</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Munder, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Gavrila, D. M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans. Intell. Transp. Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">9</style></volume><pages><style face="normal" font="default" size="100%">333-343</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vlasenko, A.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Physically Consistent Variational Denoising of Image Fluid Flow Estimates</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 30th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">406--415</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vlasenko, A.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Physically Consistent Variational Denoising of Image Fluid Flow Estimates</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition – 30th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">406–415</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Geiler, Thomas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Polarisationsbildgebung in der industriellen Qualitätskontrolle</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/8533</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this thesis the usage of polarization imaging in the field of automated optical inspection is investigated. Starting from the classical reflection models of computer vision the polarization parameters are modelled with a special focus on materials with strong volume scattering. Thereby the dependence of the polariza-tion angle on the alignment of the sample can be modelled quantitatively, whereas the behaviour of the intensity and the degree of polarization can be described only qualitatively due to the surface roughness. The influence of this roughness is studied using numerical simulations. Furthermore the measurement setup is enhanced for the industrial application. In particular the well known shifts between the raw images can be effectively compensated using a new optical flow based approach. Moreover the image quality is particularly dominated by the noise of the intensity sensor. Based on mathematical statistics, the lower bound (Cramer Rao Bound) for the measurement precision is derived. Since the dependence of the grey value variance on the grey value is taken into account for the first time, a good agreement between the theoretical consideration and the experimental data is achieved. An experimental setup which was optimised with respect to image acquisition time and stability was integrated in a production line and showed a considerable improvement in detecting defective samples while at the same time showing its suitability for series production.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Claudia Kondermann</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Postprocessing of optical flows via surface measures and motion inpainting</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">355--364</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Dense optical flow fields are required for many applications. They can be obtained by means of various global methods which employ regularization techniques for propagating estimates to regions with insufficient information. However, incorrect flow estimates are propagated as well. We, therefore, propose surface measures for the detection of locations where the full flow can be estimated reliably, that is in the absence of occlusions, intensity changes, severe noise, transparent structures, aperture problems and homogeneous regions. In this way we obtain sparse, but reliable motion fields with lower angular errors. By subsequent application of a basic motion inpainting technique to such sparsified flow fields we obtain dense fields with smaller angular errors than obtained by the original combined local global (CLG) method and the structure tensor method in all test sequences. Experiments show that this postprocessing method makes error improvements of up to 38% feasible.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jäger, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Principal Component Imagery for the Quality Monitoring of Dynamic Laser Welding Processes</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Industrial Electronics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">56:4</style></volume><pages><style face="normal" font="default" size="100%">1307-1313</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Prinzipien und Verfahren zur Aufnahme spektraler Bilddaten - Vereinfachte Bildanalyse</style></title><secondary-title><style face="normal" font="default" size="100%">QZ</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">45--48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Martin</style></author><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Range flow estimation based on photonic mixing device data</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Intelligent Systems Technologies and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">380--392</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present techniques for computing 3D velocity fields from range data acquired with cameras working on the principles of modulation based Time-Of-Flight measurement. We derive a new variant of the range flow constraint equation that directly incorporates the transformation from sensor to world coordinate system. The presented techniques are applied to a number of range sequences with ground truth and the acquired motion estimation results are discussed.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reliable Low-Level Image Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Habilitation thesis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Department Informatik, University of Hamburg</style></publisher><pub-location><style face="normal" font="default" size="100%">Hamburg</style></pub-location><abstract><style face="normal" font="default" size="100%">What information give &lt;em&gt;discrete images&lt;/em&gt; about the &lt;em&gt;continuous world&lt;/em&gt;?

Image analysis uses discrete methods to make statements about the continuous real world. Since an in
finite amount of information is lost by digitization, it is not obviuous whether or when this approa
ch will succeed: Can one prove that certain properties of interest will be preserved, despite the in
formation loss?

This habilitation thesis considers theories which explicitly connect continuous and discrete models,
 such as Shannon&#039;s famous sampling theorem and a recently discovered geometric sampling theorem. Thi
s analysis reveals important consequences regarding the necessary image quality (e.g. resolution and
 signal-to-noise-ratio) and the resulting limits of observation. These findings are subsequently app
lied to a large number of low-level image analysis problems (such edge and corner detection, segment
ation, local estimation, and noise normalization), which leads to significantly improved algorithms
that perform robustly and accurately in accordance to the predictions of theory.&lt;/div&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">König, T.</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Monigatti, F.</style></author><author><style face="normal" font="default" size="100%">Kenneth C. Parker</style></author><author><style face="normal" font="default" size="100%">Patterson, T.</style></author><author><style face="normal" font="default" size="100%">Judith Jebanthirajah Steen</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Proteome Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">3708-3717</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">O. Friedrich</style></author><author><style face="normal" font="default" size="100%">C. Weber</style></author><author><style face="normal" font="default" size="100%">M. Both</style></author><author><style face="normal" font="default" size="100%">Wegner, F. von</style></author><author><style face="normal" font="default" size="100%">J. S. Chamberlain</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Rainer H. A. Fink</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sarcomere Structure and Motor-Protein Function in an Animal Model of Duchenne Muscular Dystrophy (mdx mouse)</style></title><secondary-title><style face="normal" font="default" size="100%">87th Annual Meeting of the German Physiological Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Seeing the Objects Behind the Parts: Learning Compositional Models for Visual Recognition</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.amazon.com/Seeing-Objects-Behind-Parts-Compositional/dp/3639021444/ref=sr_1_1?ie=UTF8&amp;amp;s=books&amp;amp;qid=1232659136&amp;amp;sr=1-1</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">VDM Verlag</style></publisher><isbn><style face="normal" font="default" size="100%">978-3-639-02144-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Helmstaedter, M.</style></author><author><style face="normal" font="default" size="100%">Denk, W.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Gerhard Rigoll</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">142-152</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Signifikanter Umbruch zeichnet sich ab - Aktuelle Entwicklungen in der Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.vdma-verlag.com/home/p427.html</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">VDMA, Frankfurt</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Huhn, Florian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Simple Instrument for the Measurement of the Slope and Height Distributions of Small Scale Wind-Driven Water Waves</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Institute for Environmental Physics, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new optical wave gauge was built to measure simultaneously two statistical parameters of wind induced water surface waves, namely the surface slope and surface amplitude probability distribution functions. The new instrument was tested in a linear wind wave flume with a water depth of 10 cm. The surface slope is determined using the refraction of light at the water surface. The wave amplitude is measured using the absorption of infrared light in the water column. The wave gauge consists of a point-like dichromatic light source which is positioned under the flume (Ulbricht sphere with high-power LEDs, (RED = 632nm and IR = 850nm) and a camera above the flume that looks vertically through the water into the light source. No other optical components are needed. The light source is pulsed and the camera is triggered. The images show light speckles whose positions are a measure for the wave slope. The relative intensities are a measure for the wave amplitude. These quantities are derived from the digital images by means of image processing and simple geometrical considerations. The influence of different analysis methods on the wave slope and amplitude distribution was studied. For the characterization of the new linear wind wave flume in the Institut für Umweltphysik (IUP), Heidelberg, mean square slope and root mean square wave height were measured for a wind speed up to 6.7 m/s and a fetch between 0.80m and 2.40m. The findings agree with comparable measurements in other linear wind wave flumes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefania Petra</style></author><author><style face="normal" font="default" size="100%">Schröder, A.</style></author><author><style face="normal" font="default" size="100%">Wieneke, B.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On Sparsity Maximization in Tomographic Particle Image Reconstruction</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 30th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">294--303</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schröder, A.</style></author><author><style face="normal" font="default" size="100%">Wieneke, B.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On Sparsity Maximization in Tomographic Particle Image Reconstruction</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition – 30th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">294–303</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Martin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatiotemporal Analysis of Range Imagery</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/8879/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The present thesis handles the topic of how to determine the three dimensional motion field from a corresponding sequence of range images. We investigate signals given by range cameras that are based on the time-of-flight principle for which they employ the novel optoelectronic photonic-mixer-device (PMD). Its signal comprises information about the range, the mean radiant flux and its modulation amplitude. We discuss how to take advantage of this wealth of information. The estimation of a motion field from image sequences is an ill-posed inverse problem which can not be solved in general. Moreover, the spatiotemporal signal of a PMD-camera is corrupted by several kind of, partially rather specific, errors of systematic and statistical nature depending explicitly on time and space (motion-artifacts). We analyze those errors and develop a method to correct for systematic errors in the range signal. By means of a novel two-state-channel-smoothing we improve range images corrupted by noise and outliers. We use and extend the structure tensor approach to come for the first time to an improved motion estimate that exploits the PMD-signal and provides an inherent measure for its confidence. The presented algorithms were developed under the premise to be of a computational complexity that not forbids their application within an embedded system. They are tested on synthetic and real images and image sequences.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatiotemporal Measurement of Short Wind-Driven Water Waves</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/8897</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Spatio-temporal measurements of wind-driven short-gravity capillary waves are reported for a wide range of experimental conditions, including wind, rain and surface slicks. The experiments were conducted in a linear wind wave flume and for the water surface elevation eta(x,y,t) both components of the slope field s = grad eta were measured optically. For this the color imaging slope gauge (CISG) was realized, comprising a range of wavenumbers k = sqrt(kx^2 + ky^2) from 60 to 4500 rad/m. The instrument was improved to achieve a sampling rate of 312.5 Hz, which now allows for the computation of 3D wavenumber-frequency spectra S(kx, ky, omega). Using a new calibration method it was possible to correct for the intrinsic nonlinearities of the instrument in the slope range up to ±1. In addition, the Modulation Transfer Function (MTF) was measured and employed for the contrast restoration of the data. The results are generally consistent with former measurements. But, the shape of the saturation spectra in the vicinity of k &gt; 1000 rad/m stands in contradiction to former investigations where a sharp spectral cutoff (propto k^(-2) or k^(-3)) is commonly reported. The new MTF corrected spectra show just a gentle decrease (between k^(-0.5) and k^(-1)) for k &gt;1000 rad/m, which has implications for the modeling of the energy fluxes in the wave field. Concerning the dispersion relation, a first approach for a quantitative evaluation of the wavenumber-frequency spectrum is shown. This includes estimates of the surface tension and the Doppler shift due to the surface shear flow and the wave-wave modulations. The wave measurements were accompanied by synchronized and spatially coinciding measurements of the surface temperature by means of infrared imagery. The temperature data is mapped onto an animated graphical model of the reconstructed surface elevation using a new interactive visualization tool. This allows for an investigation of intermittent small scale processes that are influencing the transfer of heat and gases at the air-water interface, such as microscale wave breaking, small scale Langmuir circulations, and the impact of rain drops.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jäger, M.</style></author><author><style face="normal" font="default" size="100%">Humbert, S.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sputter Tracking for the Automatic Monitoring of Industrial Laser Welding Processes</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Industrial Electronics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">55</style></volume><pages><style face="normal" font="default" size="100%">2177-2184</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Claudia Kondermann</style></author><author><style face="normal" font="default" size="100%">Mester, R.</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A statistical confidence measure for optical flows</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ECCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">5304</style></volume><pages><style face="normal" font="default" size="100%">290--301</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Confidence measures are crucial to the interpretation of any optical flow measurement. Even though numerous methods for estimating optical flow have been proposed over the last three decades, a sound, universal, and statistically motivated confidence measure for optical flow measurements is still missing. We aim at filling this gap with this contribution, where such a confidence measure is derived, using statistical test theory and measurable statistics of flow fields from the regarded domain. The new confidence measure is computed from merely the results of the optical flow estimator and hence can be applied to any optical flow estimation method, covering the range from local parametric to global variational approaches. Experimental results using state-of-the-art optical flow estimators and various test sequences demonstrate the superiority of the proposed technique compared to existing confidence measures.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Holger Rapp</style></author><author><style face="normal" font="default" size="100%">Mario Frank</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Theoretical and Experimental Investigation of the Systematic Errors and Statistical Uncertainties of Time-of-Flight Cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Intelligent Systems Technologies and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">402-413</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Holger Rapp</style></author><author><style face="normal" font="default" size="100%">Mario Frank</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Intelligent Systems Technologies and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">402--413</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The following paper presents a model to predict the systematic errors and statistical uncertainties of Time-Of-Flight (TOF) 3D imaging systems. The experimental data obtained with a custom build test setup show that the SD of the depth signal rises approximately quadratically with the depth. The most significant systematic depth error is periodic with an amplitude of around 50mm. It is provoked by the inharmonic correlation function. The inhomogeneity in each pixel (fixed pattern) accounts for a depth error of about 20mm, while illumination and reflectivity variations cause depth errors of less than 10mm, provided that no overflows occur.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Meine, H.</style></author><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author><author><style face="normal" font="default" size="100%">Stelldinger, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Topological Sampling Theorem for Robust Boundary Reconstruction
and Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Discrete Applied Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">157</style></volume><pages><style face="normal" font="default" size="100%">524-541</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, B.</style></author><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Variational Approach to Adaptive Correlation for Motion Estimation
in Particle Image Velocimetry&quot;</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 30th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">335-344</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, Bernhard</style></author><author><style face="normal" font="default" size="100%">Yuan, Jing</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 30th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">335--344</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, Bernhard</style></author><author><style face="normal" font="default" size="100%">Yuan, Jing</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Variational Approach to Adaptive Correlation for Motion Estimation in Particle Image Velocimetry</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition – 30th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">5096</style></volume><pages><style face="normal" font="default" size="100%">335–344</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, Bernhard</style></author><author><style face="normal" font="default" size="100%">Yuan, Jing</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Correlation Approach to Flow Measurement with Window Adaption</style></title><secondary-title><style face="normal" font="default" size="100%">14th International Symposium on Applications of Laser Techniques to Fluid Mechanics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">1.1.3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, Bernhard</style></author><author><style face="normal" font="default" size="100%">Yuan, Jing</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Correlation Approach to Flow Measurement with Window Adaption</style></title><secondary-title><style face="normal" font="default" size="100%">14th International Symposium on Applications of Laser Techniques to Fluid Mechanics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">1.1.3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Wieneke, B.</style></author><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Correlation Approach to Flow Measurement with Window
Adaption</style></title><secondary-title><style face="normal" font="default" size="100%">14th International Symposium on Applications of Laser Techniques
to Fluid Mechanics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">1.1.8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gosch, Christian</style></author><author><style face="normal" font="default" size="100%">Fundana, Ketut</style></author><author><style face="normal" font="default" size="100%">Heyden, Anders</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">View Point Tracking of Rigid Object Based on Shape Sub-Manifolds</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision -- ECCV 2008</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5302</style></volume><pages><style face="normal" font="default" size="100%">251--263</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gosch, Christian</style></author><author><style face="normal" font="default" size="100%">Fundana, Ketut</style></author><author><style face="normal" font="default" size="100%">Heyden, Anders</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">View Point Tracking of Rigid Object Based on Shape Sub-Manifolds</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision – ECCV 2008</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">5302</style></volume><pages><style face="normal" font="default" size="100%">251–263</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jäger, M.</style></author><author><style face="normal" font="default" size="100%">Knoll, C.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Weakly Supervised Learning of a Classifier for Unusual Event Detection</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">1700-1708</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ullrich Köthe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">What Can We Learn from Discrete Images about the Continuous World</style></title><secondary-title><style face="normal" font="default" size="100%">Discrete Geometry for Computer Imagery</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4992</style></volume><pages><style face="normal" font="default" size="100%">4-19</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zukunftsperspektiven der industriellen Bildverarbeitung - Was kann die akademische Forschung beitragen?</style></title><secondary-title><style face="normal" font="default" size="100%">Optik &amp; Photonik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">28-33</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hoiem, Derek</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Winn, John</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D LayoutCRF for multi-view object class recognition and segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><isbn><style face="normal" font="default" size="100%">1424411807</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce an approach to accurately detect and segment partially occluded objects in various viewpoints and scales. Our main contribution is a novel framework for combining object-level descriptions (such as position, shape, and color) with pixel-level appearance, boundary, and occlusion reasoning. In training, we exploit a rough 3D object model to learn physically localized part appearances. To find and segment objects in an image, we generate proposals based on the appearance and layout of local parts. The proposals are then refined after incorporating object-level information, and overlapping objects compete for pixels to produce a final description and segmentation of objects in the scene. A further contribution is a novel instance penalty, which is handled very efficiently during inference. We experimentally validate our approach on the challenging PASCAL&#039;06 car database. © 2007 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Banerjee</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">S. Schabel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An active thermographic technique for highly resolved heat transport measurements in paper drying</style></title><secondary-title><style face="normal" font="default" size="100%">61st Appita Annual Conference Proceedings</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">APPITA</style></publisher><pages><style face="normal" font="default" size="100%">161-167</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Claudia Kondermann</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An adaptive confidence measure for optical flows based on linear subspace projections</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 29th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4713</style></volume><pages><style face="normal" font="default" size="100%">132--141</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Confidence measures are important for the validation of optical flow fields by estimating the correctness of each displacement vector. There are several frequently used confidence measures, which have been found of at best intermediate quality. Hence, we propose a new confidence measure based on linear subspace projections. The results are compared to the best previously proposed confidence measures with respect to an optimal confidence. Using the proposed measure we are able to improve previous results by up to 31%.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">D. Banerjee</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aktive Thermografie für die Untersuchung von Transportprozessen</style></title><secondary-title><style face="normal" font="default" size="100%">Thermografie-Kolloquium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Deutsche Gesellschaft für Zerstörungsfreie Prüfung e.V.</style></publisher><pages><style face="normal" font="default" size="100%">1--8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author><author><style face="normal" font="default" size="100%">Saussen, B.</style></author><author><style face="normal" font="default" size="100%">Steen, H.</style></author><author><style face="normal" font="default" size="100%">Judith A. J. Steen</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">amsrpm: Robust Point Matching in Retention Time Alignment of LC/MS Data with R</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Statistical Software</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.jstatsoft.org/v18/i04/paper</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">1-12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Marco Hering</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Angewandte statistische Optik in der Weißlicht-Interferometrie: Räumliches Phasenschieben und Einfluss optisch rauer Oberflächen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/7470/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This thesis describes a novel one-shot white-light interferometer for three-dimensional sensing. The method is based on spatial phase shifting which renders mechanical phase shifting unneces-sary. Due to the absence of any mechanical transducers, its static optical setup is well suited for applications outside the laboratory. Its one-shot ability is of great interest for contactless and high precision quality inspection as well as for medical applications. Also, it allows a quanti-tative monitoring of dynamic surface processes. The first part of this thesis describes the development of the optical setup in context of physical limitations and technical requirements, followed by a detailed discussion of single components and their influence onto the accuracy of the system. Furthermore, the statistical properties of speckle patterns, which appear for diffusely scattering samples, are derived. Thus, existing theories, relating the measurement uncertainty of white-light interferometry to the influence of speckle, are extended. By means of simulations and experimental techniques presented in this thesis, it is possible to verify the theoretical predictions and to clarify the consequences. Especially in context of high numerical apertures, these results have to be taken into account to improve the optical setup as well as the performance of signal processing algorithms.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Boykov, Yuri</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Applications of parametric maxflow in computer vision</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The maximum flow algorithm for minimizing energy functions of binary variables has become a standard tool in computer vision. In many cases, unary costs of the energy depend linearly on parameter λ. In this paper we study vision applications for which it is important to solve the max-flow problem for different λ&#039;s. An example is a weighting between data and regularization terms in image segmentation or stereo: it is desirable to vary it both during training (to learn λ from ground truth data) and testing (to select best λ using high-knowledge constraints, e.g. user input). We review algorithmic aspects of this parametric maximum flow problem previously unknown in vision, such as the ability to compute all breakpoints of λ and corresponding optimal configurations in finite time. These results allow, in particular, to minimize the ratio of some geometric functionals, such as flux of a vector field over length (or area). Previously, such functionals were tackled with shortest path techniques applicable only in 2D. We give theoretical improvements for &quot;PDE cuts&quot; [5]. We present experimental results for image segmentation, 3D reconstruction, and the cosegmentation problem. ©2007 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Boykov, Yuri</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Applications of parametric maxflow in computer vision</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The maximum flow algorithm for minimizing energy functions of binary variables has become a standard tool in computer vision. In many cases, unary costs of the energy depend linearly on parameter λ. In this paper we study vision applications for which it is important to solve the max-flow problem for different λ&#039;s. An example is a weighting between data and regularization terms in image segmentation or stereo: it is desirable to vary it both during training (to learn λ from ground truth data) and testing (to select best λ using high-knowledge constraints, e.g. user input). We review algorithmic aspects of this parametric maximum flow problem previously unknown in vision, such as the ability to compute all breakpoints of λ and corresponding optimal configurations in finite time. These results allow, in particular, to minimize the ratio of some geometric functionals, such as flux of a vector field over length (or area). Previously, such functionals were tackled with shortest path techniques applicable only in 2D. We give theoretical improvements for &quot;PDE cuts&quot; [5]. We present experimental results for image segmentation, 3D reconstruction, and the cosegmentation problem. ©2007 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">C. M. Zechmann</style></author><author><style face="normal" font="default" size="100%">Baudendistel, K. T.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification</style></title><secondary-title><style face="normal" font="default" size="100%">Magnetic Resonance in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">57</style></volume><pages><style face="normal" font="default" size="100%">150-159</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Ur, J. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Classification of multispectral ASTER imagery in the archaeological survey for settlement sites of the Near East</style></title><secondary-title><style face="normal" font="default" size="100%">Proc 10th International Symposium on Physical Measurements and Signature in Remote Sensing (ISPMRS 07), Davos, Switzerland</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kannan, Anitha</style></author><author><style face="normal" font="default" size="100%">Winn, John</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Clustering appearance and shape by learning jigsaws</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">657–664</style></pages><isbn><style face="normal" font="default" size="100%">9780262195683</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Patch-based appearance models are used in a wide range of computer vision applications. To learn such models it has previously been necessary to specify a suitable set of patch sizes and shapes by hand. In the jigsaw model presented here, the shape, size and appearance of patches are learned automatically from the repeated structures in a set of training images. By learning such irregularly shaped &#039;jigsaw pieces&#039;, we are able to discover both the shape and the appearance of object parts without supervision. When applied to face images, for example, the learned jigsaw pieces are surprisingly strongly associated with face parts of different shapes and scales such as eyes, noses, eyebrows and cheeks, to name a few. We conclude that learning the shape of the patch not only improves the accuracy of appearance-based part detection but also allows for shape-based part detection. This enables parts of similar appearance but different shapes to be distinguished; for example, while foreheads and cheeks are both skin colored, they have markedly different shapes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kannan, Anitha</style></author><author><style face="normal" font="default" size="100%">Winn, John</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Clustering appearance and shape by learning jigsaws</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Neural Information Processing Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">657–664</style></pages><isbn><style face="normal" font="default" size="100%">9780262195683</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Patch-based appearance models are used in a wide range of computer vision applications. To learn such models it has previously been necessary to specify a suitable set of patch sizes and shapes by hand. In the jigsaw model presented here, the shape, size and appearance of patches are learned automatically from the repeated structures in a set of training images. By learning such irregularly shaped &#039;jigsaw pieces&#039;, we are able to discover both the shape and the appearance of object parts without supervision. When applied to face images, for example, the learned jigsaw pieces are surprisingly strongly associated with face parts of different shapes and scales such as eyes, noses, eyebrows and cheeks, to name a few. We conclude that learning the shape of the patch not only improves the accuracy of appearance-based part detection but also allows for shape-based part detection. This enables parts of similar appearance but different shapes to be distinguished; for example, while foreheads and cheeks are both skin colored, they have markedly different shapes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">C. Weber</style></author><author><style face="normal" font="default" size="100%">C. M. Zechmann</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Zamecnik, R.</style></author><author><style face="normal" font="default" size="100%">Hendricks, D.</style></author><author><style face="normal" font="default" size="100%">Waldherr, R.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Delorme, S.</style></author><author><style face="normal" font="default" size="100%">Bachert, P.</style></author><author><style face="normal" font="default" size="100%">Ikinger, U.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer</style></title><secondary-title><style face="normal" font="default" size="100%">Der Urologe</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">1252</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Complex motion in environmental physics and live sciences</style></title><secondary-title><style face="normal" font="default" size="100%">Complex Motion</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3417</style></volume><pages><style face="normal" font="default" size="100%">92--105</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image sequence processing techniques are an essential tool for the experimental investigation of dynamical processes such as exchange, growth, and transport processes. These processes constitute much more complex motions than normally encountered in computer vision. In this paper, optical flow based motion analysis is extended into a generalized framework to estimate the motion field and the parameters of dynamic processes simultaneously. Examples from environmental physics and live sciences illustrate how this framework helps to tackles some key scientific questions that could not be solved without taking and analyzing image sequences.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Mester, R.</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Erhardt Barth</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Complex Motion, Proceedings of the 1st Workshop, Günzburg, October 2004</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3417</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">J. M. Buhmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Compositional Object Recognition, Segmentation, and Tracking in Video</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4679</style></volume><pages><style face="normal" font="default" size="100%">318--333</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">C. Tropea</style></author><author><style face="normal" font="default" size="100%">J. Foss</style></author><author><style face="normal" font="default" size="100%">A. Yarin</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Data acquisition by imaging detectors</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Experimental Fluid Mechanics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">1419--1436</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Imaging sensors convert radiative energy into an electrical signal and such sensors are available that cover the wide spectrum from gamma rays to the infrared. They accumulate an electrical signal during the exposure time and convert all the signals of an array of detectors into a time-serial analog or digital data stream. The dominate and most successful devices to perform this task are charge coupled devices (CCD). However directly addressable imaging sensors on the basis of CMOS fabrication technology are becoming more and more promising because the image acquisition, digitalization and preprocessing can be integrated on a single chip; hence yielding very fast frame rates. This chapter provides a comprehensive survey of the available imaging sensors, details the parameters that control their performance and gives practical tips to select the best camera for different imaging tasks.</style></abstract><section><style face="normal" font="default" size="100%">24</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Michael Klar</style></author><author><style face="normal" font="default" size="100%">Markus Jehle</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">C. Tropea</style></author><author><style face="normal" font="default" size="100%">J. Foss</style></author><author><style face="normal" font="default" size="100%">A. Yarin</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Data analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Experimental Fluid Mechanics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">1437--1491</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">From the beginning of science, visual observation has played a major role. At that time, the only way to document the results of an experiment was by verbal description and manual drawings. The next major step was the invention of photography more than one and a half centuries ago, which enabled experimental results to be documented objectively. In experimental fluid mechanics, flow visualization techniques gave direct insight into complex flows, but it was very difficult and time consuming to extract quantitative measurements from photographs and films. Nowadays, we are in the middle of a second revolution sparked by the rapid progress in both photonics and computer technology. Sensitive solid-state cameras are available that acquire digital image data, and standard personal computers and workstations have become powerful enough to process these data. These technologies are now available to any scientist or engineer. As a consequence, image processing has expanded and continues to expand rapidly from a few specialized applications into a standard scientific tool. This chapter gives a brief presentation of some of the most important general image processing techniques that are required to process image data in experimental fluid mechanics. The second section (Sect. 25.2) deals with motion analysis. The most important methods are introduced and classified according to the fundamental principles, assumptions and approximations upon which they are based.</style></abstract><section><style face="normal" font="default" size="100%">25</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Mémin, E.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">J.~Math.~Imag.~Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">67-80</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sellen, Abigail</style></author><author><style face="normal" font="default" size="100%">Fogg, Andrew</style></author><author><style face="normal" font="default" size="100%">Aitken, Mike</style></author><author><style face="normal" font="default" size="100%">Hodges, Steve</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Wood, Ken</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Do life-logging technologies support memory for the past? An experimental study using sensecam</style></title><secondary-title><style face="normal" font="default" size="100%">Conference on Human Factors in Computing Systems - Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Capture</style></keyword><keyword><style  face="normal" font="default" size="100%">Episodic or autobiographical memory</style></keyword><keyword><style  face="normal" font="default" size="100%">Images</style></keyword><keyword><style  face="normal" font="default" size="100%">Life-logging</style></keyword><keyword><style  face="normal" font="default" size="100%">Personal digital archives</style></keyword><keyword><style  face="normal" font="default" size="100%">SenseCam</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">81–90</style></pages><isbn><style face="normal" font="default" size="100%">1595935932</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We report on the results of a study using SenseCam, a &quot;life- logging&quot; technology in the form of a wearable camera, which aims to capture data about everyday life in order to support people&#039;s memory for past, personal events. We find evidence that SenseCam images do facilitate people&#039;s ability to connect to their past, but that images do this in different ways. We make a distinction between &quot;remembering&quot; the past, and &quot;knowing&quot; about it, and provide evidence that SenseCam images work differently over time in these capacities. We also compare the efficacy of user-captured images with automatically captured images and discuss the implications of these findings and others for how we conceive of and make claims about life-logging technologies. © Copyright 2007 ACM.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Scholz</style></author><author><style face="normal" font="default" size="100%">T. Wiersbinski</style></author><author><style face="normal" font="default" size="100%">Paul Ruhnau</style></author><author><style face="normal" font="default" size="100%">Daniel Kondermann</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">R. Hain</style></author><author><style face="normal" font="default" size="100%">Volker Beushausen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Double-pulse planar-LIF investigations using fluorescence motion analysis for mixture formation investigation</style></title><secondary-title><style face="normal" font="default" size="100%">7th International Symposium on Particle Image Velocimetry Rome, 11. - 14.Sept.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Criminisi, A</style></author><author><style face="normal" font="default" size="100%">Blake, A</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Shotton, J</style></author><author><style face="normal" font="default" size="100%">Torr, P. H.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Dense stereo</style></keyword><keyword><style  face="normal" font="default" size="100%">Gaze correction</style></keyword><keyword><style  face="normal" font="default" size="100%">Image-based rendering</style></keyword><keyword><style  face="normal" font="default" size="100%">Video-conferencing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><volume><style face="normal" font="default" size="100%">71</style></volume><pages><style face="normal" font="default" size="100%">89–110</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new algorithm is proposed for efficient stereo and novel view synthesis. Given the video streams acquired by two synchronized cameras the proposed algorithm synthesises images from a virtual camera in arbitrary position near the physical cameras. The new technique is based on an improved, dynamic-programming, stereo algorithm for efficient novel view generation. The two main contributions of this paper are: (i) a new four state matching graph for dense stereo dynamic programming, that supports accurate occlusion labelling; (ii) a compact geometric derivation for novel view synthesis by direct projection of the minimum cost surface. Furthermore, the paper presents an algorithm for the temporal maintenance of a background model to enhance the rendering of occlusions and reduce temporal artefacts (flicker); and a cost aggregation algorithm that acts directly in the three-dimensional matching cost space. The proposed algorithm has been designed to work with input images with large disparity range, a common practical situation. The enhanced occlusion handling capabilities of the new dynamic programming algorithm are evaluated against those of the most powerful state-of-the-art dynamic programming and graph-cut techniques. Four-state DP is also evaluated against the disparity-based Middlebury error metrics and its performance found to be amongst the best of the efficient algorithms. A number of examples demonstrate the robustness of four-state DP to artefacts in stereo video streams. This includes demonstrations of cyclopean view synthesis in extended conversational sequences, synthesis from a freely translating virtual camera and, finally, basic 3D scene editing. © 2006 Springer Science + Business Media, LLC.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kai Degreif</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Handler, R. A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating the viscous shear stress at the water surface from active thermography</style></title><secondary-title><style face="normal" font="default" size="100%">Transport at the Air Sea Interface --- Measurements, Models and Parameterizations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><pages><style face="normal" font="default" size="100%">223--239</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A novel technique is presented that makes it possible to measure the viscous shear stress from active thermography. With a CO2 laser, patterns are written to the sea surface. This temperature structure is distorted by the linear velocity profile in the viscous boundary layer. Due to the non-zero penetration depth of both the laser and the infrared camera, this vertical velocity profile can be resolved. By resolving the velocity profile, the viscous shear stress can be extracted from the recorded image sequences. At the same time, the flow field at the water surface can be measured accurately. Estimating both quantities is only possible by modeling the imaging process as well as the velocity profile in the boundary layer. The model parameters can then be computed in a standard parameter estimation framework. This novel technique was tested both on simulated data and on measurements conducted in a small annular wind-wave flume. The friction velocity computed in this fashion compared favorably to independent measurements. Although not tested yet, this technique should be equally applicable to field measurements.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Hara</style></author><author><style face="normal" font="default" size="100%">VanInwegen, E.</style></author><author><style face="normal" font="default" size="100%">J. Wendelbo</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Handler, R. A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of air-sea gas and heat fluxes from infrared imagery based on near surface turbulence models</style></title><secondary-title><style face="normal" font="default" size="100%">Transport at the Air Sea Interface --- Measurements, Models and Parameterizations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Water surface infrared images were obtained during the GASEX2001 experiment in the South Equatorial Pacific waters and during the laboratory experiment in the AEOLOTRON wind wave tank at University of Heidelberg in October 2004. The infrared imagery during these experiments reveals coexistence of roller type turbulence and intermittent breaking events. Previous interpretations of the infrared images relied on the surface renewal model, in which the water surface is assumed to be occasionally renewed by bursts of turbulent eddies reaching the water surface. A new complementary model (eddy renewal model) based on stationary and spatially periodic turbulent eddies is developed to reinterpret the infrared images of near surface turbulence. The model predicts warm elongated patches bounded by cold streaks aligned with mean wind, being consistent with field and laboratory infrared images. The model yields bulk temperature estimates and mean heat flux estimates that are very close to those based on the surface renewal model.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Roth, S.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of a convex relaxation to a quadratic assignment matching approach for relational object views</style></title><secondary-title><style face="normal" font="default" size="100%">Image Vision Comp.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">1301--1314</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Roth, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of a convex relaxation to a quadratic assignment matching approach for relational object views</style></title><secondary-title><style face="normal" font="default" size="100%">Image Vision Comp.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">1301–1314</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of Vector-Valued Clinical Image Data Using Probabilistic Graphical Models: Quantification and Pattern Recognition</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Holger Rapp</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Experimental and Theoretical Investigation of Correlating TOF-Camera Systems</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/7666</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Fluid flow estimation through integration of physical flow configurations</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 29th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">92--101</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The measurement of fluid flows is an emerging field for optical flow computation. In a number of such applications, a tracer is visualized with modern digital cameras. Due to the projective nature of the imaging process, the tracer is integrated across a velocity profile. In this contribution, a novel technique is presented that explicitly models brightness changes due to this integration. Only through this modeling is an accurate estimation of the flow velocities feasible. Apart from an accurate measurement of the fluid flow, also the underlying velocity profile can be reconstructed. Applications from shear flow, microfluidics and a biological applications are presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lenz, H.-J.</style></author><author><style face="normal" font="default" size="100%">Decker, R.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">From eigenspots to fisherspots -- latent spaces in the nonlinear detection of spot patterns in a highly variable background</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in data analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">33</style></volume><pages><style face="normal" font="default" size="100%">255-262</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">Studies in Classification, Data Analysis, and Knowledge Organization</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Blake, A</style></author><author><style face="normal" font="default" size="100%">Criminisi, A</style></author><author><style face="normal" font="default" size="100%">Cross, G</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, V</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fusion of stereo, colour and contrast</style></title><secondary-title><style face="normal" font="default" size="100%">Springer Tracts in Advanced Robotics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.research.microsoft.com/vision/cambridge</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">28</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmähling, J.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Generalizing the Abbott-Firestone curve by two new surface descriptors</style></title><secondary-title><style face="normal" font="default" size="100%">Wear</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">262</style></volume><pages><style face="normal" font="default" size="100%">1360-1371</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karim, R.</style></author><author><style face="normal" font="default" size="100%">Bergtholdt, M.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition -- 29th DAGM Symposium</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4713</style></volume><pages><style face="normal" font="default" size="100%">395-404</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LCNS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karim, R.</style></author><author><style face="normal" font="default" size="100%">Bergtholdt, M.</style></author><author><style face="normal" font="default" size="100%">Kappes, J. H.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition – 29th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LCNS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4713</style></volume><pages><style face="normal" font="default" size="100%">395-404</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Y. Renard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An Improved Method for Peak Identification in Proteomic Mass Spectrometry Data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Withopf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improved training algorithm for tree-like classifiers and its application to vehicle detection</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE Intelligent Transportation Systems Conference (ITSC)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">642--647</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We propose a new training algorithm for tree classifiers and cascades for object detection and compare it to a standard algorithm for cascade training. Our experiments show that the proposed algorithm significantly reduces the number of features needed per stage by incorporating the output of the previous stage as a weak learner into the next stage. This approach also speeds up the classification while maintaining the same detection accuracy. The analysis of the features selected by the algorithm provides further insights into its functioning.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Christopher Popp</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Handler, R. A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The influence of intermittency on air/water gas transfer measurements</style></title><secondary-title><style face="normal" font="default" size="100%">Transport at the Air Sea Interface --- Measurements, Models and Parameterizations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper theoretically investigates the influence of intermittency on determining average transfer velocities using different measuring techniques. It is shown that all measuring techniques can significantly be biased by intermittency. Mass balance and eddy correlation measurements are only biased when the concentration difference between the air and the water is spatially or temporally inhomogeneous over the measurement interval. Mean transfer velocities calculated either from mean boundary layer thicknesses or from thermographic techniques, which compute the mean transfer velocity either from concentration differences of from time constants, are biased toward lower values. The effects can be large and a simple stochastic bimodal model is used to estimate the effect.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gall, Jürgen</style></author><author><style face="normal" font="default" size="100%">Potthoff, Jürgen</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Rosenhahn, Bodo</style></author><author><style face="normal" font="default" size="100%">Hans-Peter Seidel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications</style></title><secondary-title><style face="normal" font="default" size="100%">J.~Math.~Imag.~Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1--18</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gall, Jürgen</style></author><author><style face="normal" font="default" size="100%">Potthoff, Jürgen</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Rosenhahn, Bodo</style></author><author><style face="normal" font="default" size="100%">Seidel, Hans-Peter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications</style></title><secondary-title><style face="normal" font="default" size="100%">J. Math. Imag. Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1–18</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mario Frank</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigation of a 3D Camera</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Kalibrierung der Color Imaging Slope Gauge &amp; Auswertung der Wellenmessungen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">J. M. Buhmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning the Compositional Nature of Visual Objects</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">1--8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">LogCut - Efficient graph cut optimization for markov random fields</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Markov Random Fields (MRFs) are ubiquitous in low-level computer vision. In this paper, we propose a new approach to the optimization of multi-labeled MRFs. Similarly to α-expansion it is based on iterative application of binary graph cut. However, the number of binary graph cuts required to compute a labelling grows only logarithmically with the size of label space, instead of linearly. We demonstrate that for applications such as optical flow, image restoration, and high resolution stereo, this gives an order of magnitude speed-up, for comparable energies. Iterations are performed by &quot;fusion&quot; of solutions, done with QPBO which is related to graph cut but can deal with non-submodularity. At convergence, the method achieves optima on a par with the best competitors, and sometimes even exceeds them. ©2007 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Garbe, C. S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring and Modeling Fluid Dynamic Processes using Digital Image Sequence Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Habilitation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdHabilitation thesis</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring and Modeling Fluid Dynamic Processes using Digital Image Sequence Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Welk, M.</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Feddern, C.</style></author><author><style face="normal" font="default" size="100%">Burgeth, B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Median and related local filters for tensor-valued images</style></title><secondary-title><style face="normal" font="default" size="100%">Signal Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">87</style></volume><pages><style face="normal" font="default" size="100%">291-308</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Welk, M.</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author><author><style face="normal" font="default" size="100%">Florian Becker</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Feddern, C.</style></author><author><style face="normal" font="default" size="100%">Burgeth, B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Median and related local filters for tensor-valued images</style></title><secondary-title><style face="normal" font="default" size="100%">Signal Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">87</style></volume><pages><style face="normal" font="default" size="100%">291-308</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Minimizing nonsubmodular functions with graph cuts - A review</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">energy minimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Markov random fields</style></keyword><keyword><style  face="normal" font="default" size="100%">Min cut/max flow</style></keyword><keyword><style  face="normal" font="default" size="100%">Quadratic pseudo-Boolean optimization</style></keyword><keyword><style  face="normal" font="default" size="100%">Texture restoration</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">7</style></number><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">1274–1279</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Optimization techniques based on graph cuts have become a standard tool for many vision applications. These techniques allow to minimize efficiently certain energy functions corresponding to pairwise Markov Random Fields (MRFs). Currently, there is an accepted view within the computer vision community that graph cuts can only be used for optimizing a limited class of MRF energies (e.g., submodular functions). In this survey, we review some results that show that graph cuts can be applied to a much larger class of energy functions (in particular, nonsubmodular functions). While these results are well-known in the optimization community, to our knowledge they were not used in the context of computer vision and MRF optimization. We demonstrate the relevance of these results to vision on the problem of binary texture restoration. © 2007 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model Selection in Optical Flow-Based Motion Estimation by Means of Residual Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Model Selection in Optical Flow-Based Motion Estimation by Means of Residual Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Görlitz, L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modern Concepts for Semi-Supervised Learning and Multidimensional Image Processing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Petrich, W.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy</style></title><secondary-title><style face="normal" font="default" size="100%">Analytical and Bioanalytical Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">387</style></volume><pages><style face="normal" font="default" size="100%">1801-1807</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sigg, C.</style></author><author><style face="normal" font="default" size="100%">B. Fischer</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">Roth, V.</style></author><author><style face="normal" font="default" size="100%">J. M. Buhmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonnegative CCA for Audiovisual Source Separation</style></title><secondary-title><style face="normal" font="default" size="100%">International Workshop on Machine Learning for Signal Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">253--258</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paul Ruhnau</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical Stokes Flow Estimation: An Imaging-Based Control Approach</style></title><secondary-title><style face="normal" font="default" size="100%">Exp.~in Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">61--78</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ruhnau, P.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical Stokes Flow Estimation: An Imaging-Based Control Approach</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. in Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">61–78</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Szummer, Martin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimizing binary MRFs via extended roof duality</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><isbn><style face="normal" font="default" size="100%">1424411807</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many computer vision applications rely on the efficient optimization of challenging, so-called non-submodular, binary pairwise MRFs. A promising graph cut based approach for optimizing such MRFs known as &quot;roof duality&quot; was recently introduced into computer vision. We study two methods which extend this approach. First, we discuss an efficient implementation of the &quot;probing&quot; technique introduced recently by Boros et al. [5]. It simplifies the MRF while preserving the global optimum. Our code is 400-700 faster on some graphs than the implementation of [5]. Second, we present a new technique which takes an arbitrary input labeling and tries to improve its energy. We give theoretical characterizations of local minima of this procedure. We applied both techniques to many applications, including image segmentation, new view synthesis, superresolution, diagram recognition, parameter learning, texture restoration, and image deconvolution. For several applications we see that we are able to find the global minimum very efficiently, and considerably outperform the original roof duality approach. In comparison to existing techniques, such as graph cut, TRW, BP, ICM, and simulated annealing, we nearly always find a lower energy. © 2007 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Lempitsky, Victor</style></author><author><style face="normal" font="default" size="100%">Szummer, Martin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimizing Binary MRFs via Extended Roof Duality Technical Report MSR-TR-2007-46</style></title><secondary-title><style face="normal" font="default" size="100%">Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/vision/cambridge/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Many computer vision applications rely on the efficient optimization of challenging, so-called non-submodular, binary pairwise MRFs. A promising graph cut based approach for optimizing such MRFs known as &quot;roof duality&quot; was recently introduced into computer vision. We study two methods which extend this approach. First, we discuss an efficient implementation of the &quot;probing&quot; technique introduced recently by Boros et al. [8]. It simplifies the MRF while preserving the global optimum. Our code is 400-700 faster on some graphs than the implementation of [8]. Second , we present a new technique which takes an arbitrary input labeling and tries to improve its energy. We give theoretical characterizations of local minima of this procedure. We applied both techniques to many applications, including image segmentation, new view synthesis, super-resolution, diagram recognition, parameter learning, texture restoration, and image deconvolution. For several applications we see that we are able to find the global minimum very efficiently, and considerably outperform the original roof duality approach. In comparison to existing techniques , such as graph cut, TRW, BP, ICM, and simulated annealing, we nearly always find a lower energy.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Pattern Recognition – 29th DAGM Symposium</style></title><secondary-title><style face="normal" font="default" size="100%">LCNS</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4713</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><secondary-authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4713</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4713</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pattern Recognition in the Quantitative Analysis of Vector-Valued Image Data: Diagnostic Systems and Applications</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Preusser, T.</style></author><author><style face="normal" font="default" size="100%">Marc Droske</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Martin Rumpf</style></author><author><style face="normal" font="default" size="100%">Alexandru Telea</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A phase field method for joint denoising, edge detection, and motion estimation in image sequence processing.</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM Journal of Applied Mathematics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">68</style></volume><pages><style face="normal" font="default" size="100%">599-618</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The estimation of optical flow fields from image sequences is incorporated in a Mumford/Shah approach for image denoising and edge detection. Possibly noisy image sequences are considered as input and a piecewise smooth image intensity, a piecewise smooth motion field, and a joint discontinuity set are obtained as minimizers of the functional. The method simultaneously detects image edges and motion field discontinuities in a rigorous and robust way. It is able to handle information on motion that is concentrated on edges. Inherent to it is a natural multiscale approximation that is closely related to the phase field approximation for edge detection by Ambrosio and Tortorelli. We present an implementation for two-dimensional image sequences with finite elements in space and time. This leads to three linear systems of equations, which have to be solved in a suitable iterative minimization procedure. Numerical results and different applications underline the robustness of the approach presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Baus</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Phasenmessende Streifenreflektometrie mit Stereo-Monitor-Anordnung zur Vermessung von nicht stetigen spiegelnden Präzisionsflächen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lalonde, Jean François</style></author><author><style face="normal" font="default" size="100%">Hoiem, Derek</style></author><author><style face="normal" font="default" size="100%">Efros, Alexei A</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Winn, John</style></author><author><style face="normal" font="default" size="100%">Criminisi, Antonio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Photo clip art</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the ACM SIGGRAPH Conference on Computer Graphics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3D scene reasoning</style></keyword><keyword><style  face="normal" font="default" size="100%">Blending and compositing</style></keyword><keyword><style  face="normal" font="default" size="100%">Computational photography</style></keyword><keyword><style  face="normal" font="default" size="100%">Image databases</style></keyword><keyword><style  face="normal" font="default" size="100%">Object insertion</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://graphics.cs.cmu.edu/projects/photoclipart/</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a system for inserting new objects into existing photographs by querying a vast image-based object library, pre-computed using a publicly available Internet object database. The central goal is to shield the user from all of the arduous tasks typically involved in image compositing. The user is only asked to do two simple things: 1) pick a 3D location in the scene to place a new object; 2) select an object to insert using a hierarchical menu. We pose the problem of object insertion as a data-driven, 3D-based, context-sensitive object retrieval task. Instead of trying to manipulate the object to change its orientation, color distribution, etc. to fit the new image, we simply retrieve an object of a specified class that has all the required properties (camera pose, lighting, resolution, etc) from our large object library. We present new automatic algorithms for improving object segmentation and blending, estimating true 3D object size and orientation, and estimating scene lighting conditions. We also present an intuitive user interface that makes object insertion fast and simple even for the artistically challenged. © 2007 ACM.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Trittler, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Processing of Interferometric Data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">König, T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quality Control in Mass Spectrometry</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, Martin</style></author><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Range flow estimation based on photonic mixing device data</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">ZESS, Univ.\ Siegen</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Withopf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Reliable Real-Time Vehicle Detection and Tracking</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/98745398X</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Mathematik und Informatik, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Saussen, B.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Retention Time Domain Registration of Liquid Chromatography/Mass Spectrometry Data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Görlitz, L.</style></author><author><style face="normal" font="default" size="100%">Singh, M.</style></author><author><style face="normal" font="default" size="100%">Schützbach, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Schnelle 3D-Vermessung von Partikeln in Rasterelektronenmiskroskopen mit Hilfe eines Rücksteuerdetektors</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Selection of Local Optical Flow Models by Means of Residual Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4713</style></volume><pages><style face="normal" font="default" size="100%">72-81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Andres</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Selection of local optical flow models by means of residual analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 29th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">72--81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This contribution presents a novel approach to the challenging problem of model selection in motion estimation from sequences of images. New light is cast on parametric models of local optical flow. These models give rise to parameter estimation problems with highly correlated errors in variables (EIV). Regression is hence performed by equilibrated total least squares. The authors suggest to adaptively select motion models by testing local empirical regression residuals to be in accordance with the probability distribution that is theoretically predicted by the EIV model. Motion estimation with residual-based model selection is examined on artificial sequences designed to test specifically for the properties of the model selection process. These simulations indicate a good performance in the exclusion of inappropriate models and yield promising results in model complexity control.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Görlitz, L.</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">M.-A. Weber</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4713</style></volume><pages><style face="normal" font="default" size="100%">224-233</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Signal and Image Approximation with Level-Set Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">81</style></volume><pages><style face="normal" font="default" size="100%">137-160</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Signal and Image Approximation with Level-Set Constraints</style></title><secondary-title><style face="normal" font="default" size="100%">Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">81</style></volume><pages><style face="normal" font="default" size="100%">137-160</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Steidl, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous Optical Flow Estimation and Decomposition</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM J.~Scientific Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">2283-2304</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Steidl, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous Optical Flow Estimation and Decomposition</style></title><secondary-title><style face="normal" font="default" size="100%">SIAM J. Scientific Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">2283-2304</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Jörg H. Kappes</style></author><author><style face="normal" font="default" size="100%">Bergtholdt, M.</style></author><author><style face="normal" font="default" size="100%">Pekar, V.</style></author><author><style face="normal" font="default" size="100%">Dries, S.</style></author><author><style face="normal" font="default" size="100%">Bystrov, D.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spine Detection and Labeling Using a Parts-Based Graphical Model</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4584</style></volume><pages><style face="normal" font="default" size="100%">122-133</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LCNS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmidt, S.</style></author><author><style face="normal" font="default" size="100%">Kappes, J. H.</style></author><author><style face="normal" font="default" size="100%">Bergtholdt, M.</style></author><author><style face="normal" font="default" size="100%">Pekar, V.</style></author><author><style face="normal" font="default" size="100%">Dries, S.</style></author><author><style face="normal" font="default" size="100%">Bystrov, D.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spine Detection and Labeling Using a Parts-Based Graphical Model</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LCNS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4584</style></volume><pages><style face="normal" font="default" size="100%">122-133</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hayn, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical analysis of spatio-temporal patterns in global NOX satellite data</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Holger Rapp</style></author><author><style face="normal" font="default" size="100%">Mario Frank</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A theoretical and experimental investigation of the systematic errors and statistical uncertainties of time-of-flight cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.\ Dyn3D Workshop, Heidelberg, Sept. 11, 2007</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">ZESS, Univ.\ Siegen</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jäger, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Time Series Analysis and Classification with State-Space Models for Industrial Processes and the Life Sciences</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Petra, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">A. Schröder</style></author><author><style face="normal" font="default" size="100%">B. Wieneke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tomographic Image Reconstruction in Experimental Fluid Dynamics: Synopsis and Problems</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 6th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 07)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 5-Sept. 9</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Ed Acad Romane, Bucuresti</style></publisher><pub-location><style face="normal" font="default" size="100%">Constanta, Romania</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Handler, R. A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Transport at the Air Sea Interface --- Measurements, Models and Parameterizations</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://hci.iwr.uni-heidelberg.de/publications/dip/2007/TASI/index.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paul Ruhnau</style></author><author><style face="normal" font="default" size="100%">Stahl, A.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Measurement Science and Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">755-763</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ruhnau, P.</style></author><author><style face="normal" font="default" size="100%">Stahl, A.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Measurement Science and Technology</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">755-763</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Weber, S.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Herman, G.</style></author><author><style face="normal" font="default" size="100%">Kuba, A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Reconstruction with DC-Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Advances in Discrete Tomography and Its Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Birkhäuser</style></publisher><pub-location><style face="normal" font="default" size="100%">Boston</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Muehl, S.</style></author><author><style face="normal" font="default" size="100%">Sherratt, A. G.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ooghe, B. et al.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Virtual Survey on North Mesopotamian Tell Sites by Means of Satellite Remote Sensing</style></title><secondary-title><style face="normal" font="default" size="100%">Broadening Horizons: Multidisciplinary Approaches to Landscape Study</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Cambridge Scholars Publishing</style></publisher><pages><style face="normal" font="default" size="100%">5-29</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Falkenroth</style></author><author><style face="normal" font="default" size="100%">Kai Degreif</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Handler, R. A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualisation of oxygen concentration fields in the mass boundary layer by fluorescence quenching</style></title><secondary-title><style face="normal" font="default" size="100%">Transport at the Air Sea Interface --- Measurements, Models and Parameterizations</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Laser-Induced Fluorescence (LIF) is applied to observe directly the mechanism of gas exchange in the aqueous viscous boundary layer at a free water surface. In order to make dissolved oxygen visible, a new class of fluorescent dyes is used with a life time in the order of microseconds so that the quenching constant for dissolved oxygen is sufficiently high for sensitive measurements. Depth profiles of the O2 concentration near the water surface are obtained by a vertical laser sheet at a rate of 185 frames per second. This technique is capable of a measurement sector up to several centimetres down from the water surface with a resolution in the order of 50-100 um. For a small circular wind/wave facility a correlation between wind speed and gas-exchange rates calculated from the extrapolated mean boundary-layer thickness are presented as well as the results of parallel measurements with a budget method for other gases with known Schmidt numbers.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Falkenroth</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualisation of Oxygen Concentration Profiles in the Aqueous Boundary Layer</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/7672</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Fakultät für Chemie und Geowissenschaften, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In environment studies as well as for technical application, the study of air-water gas exchange is crucial. For process studies, a novel visualisation technique of oxygen concentrations in water was realised with high spatial resolution. To resolve turbulent processes in water, also the temporal resolution was pushed to the limit of a imaging frame rate of 185 Hz. For this purpose, the well-established method of laser-induced fluorescence (LIF) was extended introducing in this type of studies a new phosphorescent ruthenium dye that is more than 15 times more sensitive to oxygen than the previously used indicator dye. The chemical synthesis of this metal-ligand complex MLC was adapted to a preparation without intermediate steps. The challenge of this imaging technique for small-scale interactions was to resolve a very thin boundary layer extending less than a millimetre below the water surface. An image processing algorithm was developed that allow the automatic detection of the exact location of the air-water phase boundary within the resolution of 25 um/pixel. Only by this step, an accurate direct determination of an important parameter for gas-exchange studies, the boundary-layer thickness, is feasible. The developed methods were applied to systematic gas-transfer measurements mostly with surfactants, conducted in a range of wind speeds between 0.8-7 m/s in a circular wind-wave facility. The measured gas-transfer velocities compared extremely well to exchange rates derived from mass-balance methods. The novel visualisation technique drastically increased the poor signal quality inherent to standard LIF techniques. This enabled accurate measurements of gas-transfer velocities from aqueous concentration profiles for the first time.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Christopher Popp</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Active thermography: a local and fast method to investigate heat and gas exchange between ocean and atmosphere</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/2006/heidelberg/up.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karsten Roetmann</style></author><author><style face="normal" font="default" size="100%">Waldemar Schmunk</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Volker Beushausen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analyse Mikrofluidischer Strömungen mit Molecular Tagging Velocimetry und Planarer Ramanstreuung</style></title><secondary-title><style face="normal" font="default" size="100%">Tagungsband Lasermethoden in der Strömungsmesstechnik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors></contributors><titles><title><style face="normal" font="default" size="100%">Autocollage</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Müller, N.</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Schmid, C.</style></author><author><style face="normal" font="default" size="100%">Soatto, S.</style></author><author><style face="normal" font="default" size="100%">Tomasi, C.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM</style></title><secondary-title><style face="normal" font="default" size="100%">Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Computer Society</style></publisher><pages><style face="normal" font="default" size="100%">96-103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Nagy, A.</style></author><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Kuba, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography</style></title><secondary-title><style face="normal" font="default" size="100%">Discrete Geometry for Computer Imagery (DGCI 2006)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4245</style></volume><pages><style face="normal" font="default" size="100%">146-156</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Kuba, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Binary Tomography with Deblurring</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Image Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4040</style></volume><pages><style face="normal" font="default" size="100%">375-388</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lect.~Not.~Comp.~Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">C. M. Zechmann</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Zamecnik, P.</style></author><author><style face="normal" font="default" size="100%">Ikinger, U.</style></author><author><style face="normal" font="default" size="100%">Waldherr, R.</style></author><author><style face="normal" font="default" size="100%">Röll, S.</style></author><author><style face="normal" font="default" size="100%">Delorme, S.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bachert, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 16th ISMRM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Neff, T.</style></author><author><style face="normal" font="default" size="100%">C. M. Zechmann</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">H. Handels</style></author><author><style face="normal" font="default" size="100%">G. Bebis</style></author><author><style face="normal" font="default" size="100%">et al.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.</style></title><secondary-title><style face="normal" font="default" size="100%">Bildverarbeitung für die Medizin 2006 - Algorithmen, Systeme, Anwendungen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.efmi-wg-mip.net/service/bvm2006</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">51-55</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">Informatik Aktuell</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carlsohn, M. F.</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Kercek, A.</style></author><author><style face="normal" font="default" size="100%">Leitner, R.</style></author><author><style face="normal" font="default" size="100%">Polder, G.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lukac, R.</style></author><author><style face="normal" font="default" size="100%">Plataniotis, K.N.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Color image processing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">CRC Press</style></publisher><volume><style face="normal" font="default" size="100%">7(17)</style></volume><pages><style face="normal" font="default" size="100%">393-419</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">Spectral Imaging and Applications</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Pal, C.</style></author><author><style face="normal" font="default" size="100%">McCallum, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning.</style></title><secondary-title><style face="normal" font="default" size="100%">ICPR 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">828-832</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of energy minimization algorithms for highly connected graphs</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">3952 LNCS</style></volume><pages><style face="normal" font="default" size="100%">1–15</style></pages><isbn><style face="normal" font="default" size="100%">3540338349</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Algorithms for discrete energy minimization play a fundamental role for low-level vision. Known techniques include graph cuts, belief propagation (BP) and recently introduced tree-reweighted message passing (TRW). So far, the standard benchmark for their comparison has been a 4-connected grid-graph arising in pixel-labelling stereo. This minimization problem, however, has been largely solved: recent work shows that for many scenes TRW finds the global optimum. Furthermore, it is known that a 4-connecled grid-graph is a poor stereo model since it does not take occlusions into account. We propose the problem of stereo with occlusions as a new test bed for minimization algorithms. This is a more challenging graph since it has much larger connectivity, and it also serves as a better stereo model. An attractive feature of this problem is that increased connectivity does not result in increased complexity of message passing algorithms. Indeed, one contribution of this paper is to show that sophisticated implementations of BP and TRW have the same time and memory complexity as that of 4-connecled grid-graph stereo. The main conclusion of our experimental study is that for our problem graph cut outperforms both TRW and BP considerably. TRW achieves consistently a lower energy than BP. However, as connectivity increases the speed of convergence of TRW becomes slower. Unlike 4-connected grids, the difference between the energy of the best optimization method and the lower bound of TRW appears significant. This shows the hardness of the problem and motivates future research. © Springer-Verlag Berlin Heidelberg 2006.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of energy minimization algorithms for highly connected graphs</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">3952 LNCS</style></volume><pages><style face="normal" font="default" size="100%">1–15</style></pages><isbn><style face="normal" font="default" size="100%">3540338349</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Algorithms for discrete energy minimization play a fundamental role for low-level vision. Known techniques include graph cuts, belief propagation (BP) and recently introduced tree-reweighted message passing (TRW). So far, the standard benchmark for their comparison has been a 4-connected grid-graph arising in pixel-labelling stereo. This minimization problem, however, has been largely solved: recent work shows that for many scenes TRW finds the global optimum. Furthermore, it is known that a 4-connecled grid-graph is a poor stereo model since it does not take occlusions into account. We propose the problem of stereo with occlusions as a new test bed for minimization algorithms. This is a more challenging graph since it has much larger connectivity, and it also serves as a better stereo model. An attractive feature of this problem is that increased connectivity does not result in increased complexity of message passing algorithms. Indeed, one contribution of this paper is to show that sophisticated implementations of BP and TRW have the same time and memory complexity as that of 4-connecled grid-graph stereo. The main conclusion of our experimental study is that for our problem graph cut outperforms both TRW and BP considerably. TRW achieves consistently a lower energy than BP. However, as connectivity increases the speed of convergence of TRW becomes slower. Unlike 4-connected grids, the difference between the energy of the best optimization method and the lower bound of TRW appears significant. This shows the hardness of the problem and motivates future research. © Springer-Verlag Berlin Heidelberg 2006.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heiler, M.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Controlling Sparseness in Non-negative Tensor Factorization</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision -- ECCV 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3951</style></volume><pages><style face="normal" font="default" size="100%">56-67</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Minka, Tom</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/vision/cambridge/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">994–1000</style></pages><isbn><style face="normal" font="default" size="100%">0769525970</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce the term cosegmentation which denotes the task of segmenting simultaneously the common parts of an image pair. A generative model for cosegmentation is presented. Inference in the model leads to minimizing an energy with an MRF term encoding spatial coherency and a global constraint which attempts to match the appearance histograms of the common parts. This energy has not been proposed previously and its optimization is challenging and NP-hard. For this problem a novel optimization scheme which we call trust region graph cuts is presented. We demonstrate that this framework has the potential to improve a wide range of research: Object driven image retrieval, video tracking and segmentation, and interactive image editing. The power of the framework lies in its generality, the common part can be a rigid/non-rigid object (or scene), observed from different viewpoints or even similar objects of the same class. © 2006 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. Hader</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data Mining auf multidimensionalen und komplexen Daten in der industriellen Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Ur, J. A.</style></author><author><style face="normal" font="default" size="100%">Sherratt, A. G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detection of ancient settlement mounds - Archaeological survey based on the SRTM terrain model</style></title><secondary-title><style face="normal" font="default" size="100%">Photgrammetric Engineering &amp; Remote Sensing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">321-327</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">U. Kertzscher</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Direct estimation of the wall shear rate using parametric motion models in 3D</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 28th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">4174</style></volume><pages><style face="normal" font="default" size="100%">434--443</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new optical-flow-based technique to estimate the wall shear rate using a special illumination technique that makes the brightness of particles dependent on the distance from the wall. The wall shear rate is derived directly (that means, without previous calculation of the velocity vector field) from two of the components of the velocity gradient tensor which in turn describes the kinematics of fluid flows up to the first order. By incorporating this into a total least squares framework, we can apply a further extension of the structure tensor technique. Results obtained both from synthetical and real data are shown, and reveal a substantial improvement compared to conventional techniques.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lerch, K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discontinuity Preserving Filtering of Spectral Images</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stahl, A.</style></author><author><style face="normal" font="default" size="100%">Paul Ruhnau</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Distributed Parameter Approach to Dynamic Image Motion</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV 2006, International Workshop on The Representation and Use of Prior Knowledge in Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">LNCS, Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kraus, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DOASIS a framework design for DOAS</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Technische Informatik, Univ. Mannheim</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdDissertation</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. Kraus</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">DOASIS a framework design for DOAS</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Technische Informatik, Univ.\ Mannheim</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Eine neuartige Methode zur raumzeitlichen Analyse von Strömungen in Grenzschichten</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/2006/heidelberg/up.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. P. Lichy</style></author><author><style face="normal" font="default" size="100%">Bachert, P.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">M.-A. Weber</style></author><author><style face="normal" font="default" size="100%">Debus, J.</style></author><author><style face="normal" font="default" size="100%">Schulz-Ertner, D.</style></author><author><style face="normal" font="default" size="100%">Kauczor, H.-U.</style></author><author><style face="normal" font="default" size="100%">Schlemmer, H.-P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Einsatz der 1H-MR-spektroskopischen Bildgebung in der Strahlentherapie: Cholin als Marker für die Bestimmung der relativen Wahrscheinlichkeit eines Tumorprogresses nach Bestrahlung glialer Hirntumoren</style></title><secondary-title><style face="normal" font="default" size="100%">Zeitung für Röntgenforschung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">178</style></volume><pages><style face="normal" font="default" size="100%">627-633</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roth, V.</style></author><author><style face="normal" font="default" size="100%">Björn Ommer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploiting Low-level Image Segmentation for Object Recognition</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Symposium of the DAGM</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">4174</style></volume><pages><style face="normal" font="default" size="100%">11--20</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kai Degreif</style></author><author><style face="normal" font="default" size="100%">Joachim Kuss</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gas exchange measurements: the chemically enhanced gas transfer of carbon dioxide at the water surface</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/2006/heidelberg/up.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kai Degreif</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gas exchange measurements: transition of the boundary conditions from a flat to a rough water surface</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/2006/heidelberg/up.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Falkenroth</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Imaging concentration profiles of water boundary layer by Double-Dye LIF and inverse modelling</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/2006/heidelberg/up.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Imaging System for combined slope/height measurements of short wind waves : ISHG</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/2006/heidelberg/up.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Heinrich Hühnerfuss</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Martin Gade</style></author><author><style face="normal" font="default" size="100%">Gerald M. Korenowski</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Infrared imaging: a novel tool to investigate the influence of surface slicks on air-sea gas transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Marine Surface Films: Chemical Characteristics, Influence on Air-Sea Interactions, and Remote Sensing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">239--252</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The influence of surface films on air-sea gas exchange at low and moderate wind speeds is investigated. Observations were made in the small Heidelberg circular wind-wave facility and in coastal and offshore waters south of Cape Cod, New England. The passive controlled flux technique was used to investigate the micro turbulence very near the water surface, which controls the rate of transfer of momentum, heat, and mass across the air-sea interface. The analysis of infrared image sequences allows the estimation of the net heat flux at the water surface, the skin-bulk temperature difference across the thermal sublayer and thus the heat transfer velocity. Using Schmidt number scaling, estimates of the gas transfer velocity are obtained. Experimental evidence shows that increased surface film concentrations suppress near surface turbulence and thus decrease the gas exchange compared to a slick-free ocean interface. If a surfactant is present, turbulent mixing is dampened and direct renewal of the surface is inhibited. A surface slick changes the hydrodynamic boundary conditions in that the length scales of near surface turbulence controlling air sea gas exchange are modified. The micro-scale temperature fluctuations at the water surface indicate that at low wind speeds the transport process is dominated by large-scale turbulence, whereas at higher wind speeds the smallest observed scales dominate the transport.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Withopf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning algorithm for real-time vehicle tracking</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE Intelligent Transportation Systems Conference ITSC &#039;06</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pages><style face="normal" font="default" size="100%">516--521</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This article presents a learning algorithm for real-time object tracking in video sequences which uses an improvement of a feature selection method known from object detection. But in contrast to trackers based on object detection methods, our approach explicitly selects the features which are best suited to track an object, which are different from the best features for object detection. The used features are constructed from pairs of image patches and related to Haar features. Besides the automatic selection of features according to their discriminative (tracking) power, the advantage of this approach is that the resulting tracker is very fast, allowing it to run in addition to a detector to robustify the object position estimation and to compensate for dropouts of the detector. A comparison of the proposed tracking algorithm with other tracking methods is presented which shows the accuracy of the proposed algorithm</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">J. M. Buhmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Compositional Categorization Models</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the European Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3953</style></volume><pages><style face="normal" font="default" size="100%">316--329</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bergtholdt, Martin</style></author><author><style face="normal" font="default" size="100%">Kappes, Jörg H.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning of Graphical Models and Efficient Inference for Object Class Recognition</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. DAGM 2006</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LCNS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">375-388</style></volume><pages><style face="normal" font="default" size="100%">375-388</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heiler, M.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming</style></title><secondary-title><style face="normal" font="default" size="100%">J. 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Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">70</style></volume><pages><style face="normal" font="default" size="100%">257-277</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Sochen, Nir</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">ijcv</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">66</style></volume><pages><style face="normal" font="default" size="100%">67-81</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel method for spatio-temporal analysis of flows within the water-side viscous boundary layer</style></title><secondary-title><style face="normal" font="default" size="100%">12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paul Ruhnau</style></author><author><style face="normal" font="default" size="100%">Stahl, A.</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.~DAGM 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">375-388</style></volume><pages><style face="normal" font="default" size="100%">375-388</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ruhnau, P.</style></author><author><style face="normal" font="default" size="100%">Stahl, A.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. DAGM 2006</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">375-388</style></volume><pages><style face="normal" font="default" size="100%">375-388</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Karsten Roetmann</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An optical flow based technique for the non-invasive measurement of microfluidic flows</style></title><secondary-title><style face="normal" font="default" size="100%">12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">M. P. Lichy</style></author><author><style face="normal" font="default" size="100%">Bachert, P.</style></author><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Schlemmer, H. P.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal Classification of Long Echo Time in vivo Magnetic Resonance Spectra in the Detection of Recurrent Brain Tumor</style></title><secondary-title><style face="normal" font="default" size="100%">NMR in Biomedicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">599-609</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Criminisi, Antonio</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author><author><style face="normal" font="default" size="100%">Cross, Geoffrey</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Probabilistic fusion of stereo with color and contrast for bilayer segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Transactions on Pattern Analysis and Machine Intelligence</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">3D/stereo scene analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">Image processing and computer vision</style></keyword><keyword><style  face="normal" font="default" size="100%">Parameter learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/vision/cambridge</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">1480–1492</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, contrast, and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color model that is learned on-the-fly and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead, the stereo match likelihood is marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output. © 2006 IEEE.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sieg, S.</style></author><author><style face="normal" font="default" size="100%">Stutz, B.</style></author><author><style face="normal" font="default" size="100%">Schmidt, T.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Maier, W. F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A QCAR-approach to materials modelling</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Molecular Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">611-619</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wieler, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Single tone frequency estimation from very few sampling points</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatio-temporal analysis of flows close to water surfaces</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/7060/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In order to examine the air-water gas exchange, a detailed knowledge is needed about the flow field within and beneath the water-side viscous boundary layer. Therefore a novel measurement technique is developed for the spatio-temporal analysis of flows close to free water surfaces. A fluid volume is illuminated by LEDs. Small spherical particles are added to the fluid, functioning as a tracer. A camera pointing to the water surface from above records the image sequences. The distance of the spheres to the surface is coded by means of a supplemented dye, which absorbs the light of the LEDs. By using LEDs flashing with two different wavelengths, it is possible to use particles variable in size. The velocity vectors are obtained by using an extension of the method of optical flow. The vertical velocity component is computed from the temporal change of brightness. Using 3D parametric motion models the shear stress at surfaces can be estimated directly, without previous calculation of the vector fields. Hardware and algorithmics are tested in several ways: A laminar falling film serves as reference flow. The predicted parabolic profile of this stationary flow can be reproduced very well. Buoyant convective turbulence acts as an example for an instationary inherently 3D flow. The direct estimation of the wall shear rate is applied to sequences recorded in the context of biofluidmechanics, revealing a substantial improvement compared to conventional techniques.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Felix Vogel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spectroscopic Techniques for Gas-Exchange Measurements</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmähling, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical characterization of technical surface microstructure</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shotton, Jamie</style></author><author><style face="normal" font="default" size="100%">Winn, John</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Criminisi, Antonio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">aeroplanes) and articulated objects (eg body</style></keyword><keyword><style  face="normal" font="default" size="100%">bikes</style></keyword><keyword><style  face="normal" font="default" size="100%">cow)</style></keyword><keyword><style  face="normal" font="default" size="100%">faces</style></keyword><keyword><style  face="normal" font="default" size="100%">g grass</style></keyword><keyword><style  face="normal" font="default" size="100%">highly structured (eg cars</style></keyword><keyword><style  face="normal" font="default" size="100%">trees)</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/vision/cambridge/recognition/.</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">3951 LNCS</style></volume><pages><style face="normal" font="default" size="100%">1–15</style></pages><isbn><style face="normal" font="default" size="100%">3540338322</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper proposes a new approach to learning a discriminative model of object classes, incorporating appearance, shape and context information efficiently. The learned model is used for automatic visual recognition and semantic segmentation of photographs. Our discriminative model exploits novel features, based on textons, which jointly model shape and texture. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating these classifiers in a conditional random field. Efficient training of the model on very large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy are demonstrated on three different databases: i) our own 21-object class database of photographs of real objects viewed under general lighting conditions, poses and viewpoints, ii) the 7-class Corel subset and iii) the 7-class Sowerby database used in [1]. The proposed algorithm gives competitive results both for highly textured (e.g. grass, trees), highly structured (e.g. cars, faces, bikes, aeroplanes) and articulated objects (e.g. body, cow). © Springer-Verlag Berlin Heidelberg 2006.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rosenbaum, Thomas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Thermografische Messung der Temperatur metallischer Oberflächen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><publisher><style face="normal" font="default" size="100%">Technische Universität Dresden</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rosenbaum, T.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Gerlach, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Thermographical measurement of temperatures on metallic surfaces</style></title><secondary-title><style face="normal" font="default" size="100%">Sensor+Test 2006 Proceedings (OPTO 2006, IRS2 2006), Nürnberg, 30 May - 1 June 2006, AMA Fachverband für Sensorik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pages><style face="normal" font="default" size="100%">327--330</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schmähling, J.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Hoffmann, D. M. P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A three-dimensional measure of surface roughness based on mathematical morphology</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Machine Tools and Manufacture</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">46 (14)</style></volume><pages><style face="normal" font="default" size="100%">1764-1769</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirk, David S</style></author><author><style face="normal" font="default" size="100%">Sellen, Abigail J</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Wood, Kenneth R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Understanding photowork</style></title><secondary-title><style face="normal" font="default" size="100%">Conference on Human Factors in Computing Systems - Proceedings</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Browsing</style></keyword><keyword><style  face="normal" font="default" size="100%">Content-based image retrieval</style></keyword><keyword><style  face="normal" font="default" size="100%">Digital photo albums</style></keyword><keyword><style  face="normal" font="default" size="100%">Photowork</style></keyword><keyword><style  face="normal" font="default" size="100%">Searching</style></keyword><keyword><style  face="normal" font="default" size="100%">Use of images</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">761–770</style></pages><isbn><style face="normal" font="default" size="100%">1595931783</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we introduce the notion of &quot;photowork&quot; as the activities people perform with their digital photos after capture but prior to end use such as sharing. Surprisingly, these processes of reviewing, downloading, organizing, editing, sorting and filing have received little attention in the literature yet they form the context for a large amount of the &#039;search&#039; and &#039;browse&#039; activities so commonly referred to in studies of digital photo software. Through a deeper understanding of photowork using field observation and interviews, we seek to highlight its significance as an interaction practice. At the same time, we discover how &quot;search&quot; as it is usually defined may have much less relevance than new ways of browsing for the design of new digital photo tools, in particular, browsing in support of the photowork activities we describe. Copyright 2006 ACM.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christopher Popp</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Untersuchung von Austauschprozessen an der Wasseroberfläche aus Infrarot-Bildsequenzen mittels frequenzmodulierter Wärmeeinstrahlung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/6489</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The study presents a new technique for measuring the gas transfer rate at a free water surface with high temporal and spatial resolution, a method suitable for field experiments. The approach is based on the Controlled Flux Technique (CFT) using heat as a proxy tracer for gas exchange. A periodically varying heat flux at water surface is used. The heat flux is generated by a 100 W CO2-laser, while a highly sensitive infrared camera measures the temperature response of the water surface. Heat - and consequently gas transfer rates - can be deduced from the analysis of the spectral amplitude attenuation at water surface. Furthermore, the approach presented allows direct insight into the transport mechanisms of gas exchange processes by an analysis of the phase shift observed at water surface. A first series of measurements at the Heidelberg wind wave facility AEOLOTRON shows that the spectral amplitude attenuation is in keeping with model predictions. The calculated transfer rates for CO2 are also well in accordance with results from other laboratory and field experiments. Contrary to the amplitude attenuation, the calculated phase shifts differ signicantly from model predictions, especially for high wind speeds. This effect, observed for the first time, can be interpreted as being caused by the intermittence of transfer processes. This would indicate that stochastic processes play a larger role in gas transfer than assumed so far.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kai Degreif</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Untersuchungen zum Gasaustausch - Entwicklung und Applikation eines zeitlich aufgelösten Massenbilanzverfahrens</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/6120</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The study presents a novel technique for measuring time resolved gas transfer rates at the water surface. By applying this new mass balance technique neither absolute concentrations of the trace gases nor the measurement of their concentrations in the water phase is required. Gas fluxes are calculated exclusively by the temporal change of the air-sided concentrations in an evasion experiment. The high temporal resolution of this procedure allows fast measurements under different conditions. Systematic measurements of gas exchange rates are now feasible within hours. By using UV-spectroscopy, simultaneous concentration measurements of volatile aromatics in the air and water phases verified the results obtained by the presented technique. Within the framework of the UV-measurements a method was developed to determine the air-sided concentrations of the aromatics precisely, even at low spectral resolution of the spectrometer. The differential optical absorption spectroscopy was successfully applied to a wide concentration range of these tracers. Reliable gas transfer velocities are now also available for very low wind speeds and friction velocities. The transition of the Schmidt number exponent from a flat to a rough water surface was reproduced. The transition begins at unexpectedly low wind speeds and extends over a wide range of wind speeds. For the first time the effect of chemically enhanced gas-exchange was demonstrated under a variety of chemical and physical conditions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alexandru Telea</style></author><author><style face="normal" font="default" size="100%">Tobias Preußer</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Marc Droske</style></author><author><style face="normal" font="default" size="100%">Martin Rumpf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A variational approach to joint denoising, edge detection and motion estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 28th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://numod.ins.uni-bonn.de/research/papers/public/PrDrGa06.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">525--535</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Falkenroth</style></author><author><style face="normal" font="default" size="100%">Alexandra G. Herzog</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualization of air-water gas exchange using novel fluorescent dyes</style></title><secondary-title><style face="normal" font="default" size="100%">12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karsten Roetmann</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Volker Beushausen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">2D-Molecular Tagging Velocimetry zur Analyse Mikrofluidischer Strömungen</style></title><secondary-title><style face="normal" font="default" size="100%">Tagungsband Lasermethoden in der Strömungsmesstechnik (GALA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><pages><style face="normal" font="default" size="100%">26/1--26/10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Adaptive Reconstruction of Discrete-Valued Objects from few Projections</style></title><secondary-title><style face="normal" font="default" size="100%">Electr. Notes in Discr. Math.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">365-384</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Nielsen, R.</style></author><author><style face="normal" font="default" size="100%">Christopher Popp</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Air-Sea Gas Transfer; Schmidt Number dependency and intermittency</style></title><secondary-title><style face="normal" font="default" size="100%">Presented at: International Liege Colloquium on Ocean Dynamics, Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">König, T.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Kücherer, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the Application of Multiscale Motion Estimation in Intravascular Elastography</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">B. Michael Kelm</style></author><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen</style></title><secondary-title><style face="normal" font="default" size="100%">VDI-Berichte</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><volume><style face="normal" font="default" size="100%">1883</style></volume><pages><style face="normal" font="default" size="100%">457-466</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><custom3><style face="normal" font="default" size="100%">VDI-Berichte</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jäger, M.</style></author><author><style face="normal" font="default" size="100%">Knoll, C.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatisierte Klassifikation von Laserschwei\DFprozessen durch Nutzung von 3D Signalverarbeitungs-Algorithmen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Robert Bosch GmbH, Schwieberdingen and IWR, Uni Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hissmann, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian Estimation for White Light Interferometry</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/5742</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hissmann, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayesian surface estimation for white light interferometry</style></title><secondary-title><style face="normal" font="default" size="100%">Optical Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">1-9</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kolmogorov, V</style></author><author><style face="normal" font="default" size="100%">Criminisi, A</style></author><author><style face="normal" font="default" size="100%">Blake, A</style></author><author><style face="normal" font="default" size="100%">Cross, G</style></author><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bi-layer segmentation of binocular stereo video</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/vision/cambridge</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">II</style></volume><pages><style face="normal" font="default" size="100%">407–414</style></pages><isbn><style face="normal" font="default" size="100%">0769523722</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper describes two algorithms capable of real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from colour/contrast or from stereo alone is known to be error-prone. Here, colour, contrast and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended 6-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive colour model that is learned on the fly, and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead the stereo match likelihood is marginalised over foreground and background hypotheses, and fused with a contrast-sensitive colour model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar perfomance, substantially better than stereo or colour/contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildgebendes System zur simultanen Neigungs- und Höhenmessung an kleinskaligen Wind-Wasserwellen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Hornegger, J.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Klette, R.</style></author><author><style face="normal" font="default" size="100%">Kozera, R.</style></author><author><style face="normal" font="default" size="100%">Noakes, L.</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Binary Tomography by Iterating Linear Programs</style></title><secondary-title><style face="normal" font="default" size="100%">Geometric Properties from Incomplete Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">(13 pages)</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Schmidt, M.</style></author><author><style face="normal" font="default" size="100%">Roland Rocholz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined optical slope/height measurements of short wind waves: principles and calibration</style></title><secondary-title><style face="normal" font="default" size="100%">Meas. Sci. Technol.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">10</style></number><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">1937--1944</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A novel short wave imaging technique is described. For the first time, it is capable of measuring the wave height and wave slope simultaneously with unprecedented accuracy. A telecentric optical system is used to image the waves so that the image magnification does not change with the wave height and the slope calibration is much less dependent on the position of the image. A telecentric illumination system contains an area-extended LED light source that is placed in the focal plane of a second lens below the water channel. In this way, the wave slope can be coded by the position-dependent intensity of the light source. LEDs with two different wavelengths in the red and near infrared part of the spectrum are used. Because the water column absorbs the two wavelengths differently, the difference in the observed intensities gives the wave height. The paper details the principle of the technique and the calibration procedures.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Neumann, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Steidl, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined SVM-based Feature Selection and Classification</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Learning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><volume><style face="normal" font="default" size="100%">61</style></volume><pages><style face="normal" font="default" size="100%">129-150</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Klar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of an endoscopic 3D Particle-Tracking Velocimetry system and its application in flow measurements within a gravel layer</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/5961</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this thesis a novel method for 3-D flow measurements within a permeable gravel layer is developed. Two fiberoptic endoscopes are used in a stereoscopic arrangement to acquire image sequences of the flow field within a single gravel pore. The images are processed by a 3-D Particle-Tracking Velocimetry (3-D PTV) algorithm, which yields the three-dimensional reconstruction of Lagrangian particle trajectories. The underlying image processing algorithms are significantly enhanced and adapted to the special conditions of endoscopic imagery. This includes methods for image preprocessing, robust camera calibration, image segmentation and particle-tracking. After a performance and accuracy analysis, the measurement technique is applied in extensive systematic investigations of the flow within a gravel layer in an experimental flume at the Federal Waterways Engineering and Research Institute in Karlsruhe. In addition to measurements of the pore flow within three gravel pores, an extended experimental setup enables the simultaneous observation of the near-bed 3-D flow field in the turbulent open-channel flow above the gravel layer and of grain motions in a sand layer beneath the gravel layer. The interaction of the free surface flow and the pore flow can be analyzed for the first time with a high temporal and spatial resolution. The experiments are part of a research project initiated by an international cooperation called Filter and Erosion Research Club (FERC). The longterm goal of this project is to quantify the influence of turbulent velocity and pressure fluctuations on the bed stability of waterways. The obtained experimental data provide new insight into the damping behaviour of a gravel bed and can be used for comparison with numerical, analytical and phenomenological models.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Görlitz, L.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Staudacher, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detektion von Partikeln in Intensitätsbildern mit Hilfe eines morphologischen Skalenraumes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Robert-Bosch GmbH, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schwarz, T. S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of a depth resolving boundary layer visualiziation für gas exchange at free water surfaces</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this thesis, a measurement setup is introduced which makes it possible to directly measure two-dimensional, vertical concentration of gases in the water sided boundary layer using Laser-Induced Fluorescence (LIF). While it is impossible to gain knowledge of the physical processes involved in gas exchange using measurements of transfer rates and mass balances, the introduced method makes it possible to directly visualize the physical processes of matter transport in the boundary layer. The measurement method is based on two basic principles: First a fluorescence indicator is used, whose fluorescence intensity is proportional to the local pH-value, thus allowing a spatial resolved measurement of the concentrations of dissolved alkaline or acidic gases can directly be visualized. Second, to create a depth resolution, a second, absorbing dye is added, whose absoprtion maximum lies inside the fluorescence spectrum, so that spectra from different depths show changes in their spectral shape due to the different light path lengths through the absorber. Thus the measured spectrum is the superposition of all depth spectra, which provide the basis of a linear inverse problem. Models for the reconstruction of the depth information will be introduced in the course of this thesis, and the solvability will be analyzed. As the stability of the solution of the inverse problem is almost exclusively determined by the invertibility of the basis function matrix, a confocal microscope was constructed, which allowed the direct measurement of depth spectra. Through this it was made possible to numerically analyze and evaluate the conditioning of the matrix invertibility.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digital Image Processing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><edition><style face="normal" font="default" size="100%">6</style></edition><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kumar, Sanjiv</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digital tapestry [automatic image synthesis]</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/ http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1467321%5Cnhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467321</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">589–596</style></pages><isbn><style face="normal" font="default" size="100%">0-7695-2372-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper addresses the novel problem of automatically synthesizing an output image from a large collection of different input images. The synthesized image, called a digital tapestry, can be viewed as a visual summary or a virtual &#039;thumbnail&#039; of all the images in the input collection. The problem of creating the tapestry is cast as a multi-class labeling problem such that each region in the tapestry is constructed from input image blocks that are salient and such that neighboring blocks satisfy spatial compatibility. This is formulated using a Markov random field and optimized via the graph cut based expansion move algorithm. The standard expansion move algorithm can only handle energies with metric terms, while our energy contains non-metric (soft and hard) constraints. Therefore we propose two novel contributions. First, we extend the expansion move algorithm for energy functions with non-metric hard constraints. Secondly, we modify it for functions with &quot;almost&quot; metric soft terms, and show that it gives good results in practice. The proposed framework was tested on several consumer photograph collections, and the results are presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digitale Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><edition><style face="normal" font="default" size="100%">6</style></edition><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bruhn, Andrés</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time</style></title><secondary-title><style face="normal" font="default" size="100%">Scale-Space 2005</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3459</style></volume><pages><style face="normal" font="default" size="100%">279–290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Ruhnau, P.</style></author><author><style face="normal" font="default" size="100%">Mémin, E.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Scale-Space 2005</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3459</style></volume><pages><style face="normal" font="default" size="100%">267–278</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Hornegger, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discrete Tomography By Convex-Concave Regularization and D.C. Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Discr. Appl. Math.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">151</style></volume><pages><style face="normal" font="default" size="100%">229-243</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kohlberger, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Bruhn, A.</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Domain decomposition for variational optical flow computation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans. Image Proc.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">8</style></number><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">1125-1137</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Neumann, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Steidl, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Wavelet Adaption for Hybrid Wavelet-Large Margin Classifiers</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><number><style face="normal" font="default" size="100%">11</style></number><volume><style face="normal" font="default" size="100%">38</style></volume><pages><style face="normal" font="default" size="100%">1815-1830</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Hara</style></author><author><style face="normal" font="default" size="100%">J. Wendelbo</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of air-sea gas and heat fluxes from infrared imagery and surface wave measurements</style></title><secondary-title><style face="normal" font="default" size="100%">Presented at: International Liège Colloquium on Ocean Dynamics, Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Kai Degreif</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Heidelberg Aeolotron: new perspectives for laboratory investigations of small-scale air-sea interaction</style></title><secondary-title><style face="normal" font="default" size="100%">Poster presented at: International Liege Colloquium on Ocean Dynamics, Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Banerjee</style></author><author><style face="normal" font="default" size="100%">S. Schabel</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Spiegel, W. von</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High resolution optical measurement techniques in paper drying</style></title><secondary-title><style face="normal" font="default" size="100%">Inpaper International - 9-10</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">16--22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Heinrich Hühnerfuss</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Martin Gade</style></author><author><style face="normal" font="default" size="100%">Gerald M. 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Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">C08S17</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Raisch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aktive Konturen zur Objektsegmentierung in stark verrauschten Bildsequenzen und zur Segmentierung von Bonddrähten in der industriellen Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/972877436</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Univ.\ Mannheim</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Klar</style></author><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Detert, M.</style></author><author><style face="normal" font="default" size="100%">Jirka G. H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of subsurface gravel layer flow caused by turbulent open-channel flow using 3D PTV and pressure sensor techniques</style></title><secondary-title><style face="normal" font="default" size="100%">BAW-Workshop Soil and Bed Stability - Interaction Effects between Geotechnics and Hydraulic Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giebel, J.</style></author><author><style face="normal" font="default" size="100%">Gavrila, D. M.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pajdla, T.</style></author><author><style face="normal" font="default" size="100%">Matas, J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A Bayesian Framework for Multi-cue 3D Object Tracking</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision – ECCV 2004</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3024</style></volume><pages><style face="normal" font="default" size="100%">241-252</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Michael Klar</style></author><author><style face="normal" font="default" size="100%">H.-J. Köhler</style></author><author><style face="normal" font="default" size="100%">Heibaum, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bewegungsdetektion und Geschwindigkeitsanalyse in Bildfolgen zur Untersuchung von Sedimentverlagerungen</style></title><secondary-title><style face="normal" font="default" size="100%">Mitteilungen des Instituts für Grundbau und Bodenmechanik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">77</style></volume><pages><style face="normal" font="default" size="100%">371-392</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Hornegger, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Klette, R.</style></author><author><style face="normal" font="default" size="100%">Žunić, J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Binary Tomography by Iterating Linear Programs from Noisy Projections</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA&#039;04)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">3322</style></volume><pages><style face="normal" font="default" size="100%">38–51</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Classification</style></title><secondary-title><style face="normal" font="default" size="100%">Practical Handbook on Image Processing for Scientific and Technical Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><edition><style face="normal" font="default" size="100%">2nd</style></edition><publisher><style face="normal" font="default" size="100%">CRC Press</style></publisher><pages><style face="normal" font="default" size="100%">509-519</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author><author><style face="normal" font="default" size="100%">Wormit, M.</style></author><author><style face="normal" font="default" size="100%">Bachert, P.</style></author><author><style face="normal" font="default" size="100%">M. P. Lichy</style></author><author><style face="normal" font="default" size="100%">Schlemmer, H.-P.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Classification of in vivo magnetic resonance spectra</style></title><secondary-title><style face="normal" font="default" size="100%">Classification in ubiquitous challenge: Proceedings of the GfKl 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">362-369</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yuan, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Kohlberger, T.</style></author><author><style face="normal" font="default" size="100%">Ruhnau, P.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Set-Based Estimation of Image Flows</style></title><secondary-title><style face="normal" font="default" size="100%">ICPR 2004 – 17th Int. Conf. on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Aug. 23-26</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Cambridge, UK</style></pub-location><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">124-127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Volker Hilsenstein</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design and Implementation of a Passive Stereo-Infrared Imaging System for the Surface Reconstruction of Water Waves</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/4601</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">To quantify air-sea exchange processes, an understanding of how they are influenced by water waves is necessary. This work presents a passive, infrared stereo-imaging system for the reconstruction of a wavy water surface. The system does not require a sub-merged light source, so it is suitable for field operation. The structure of the thesis is as follows. Previous work on water wave imaging is reviewed and the major problems with stereo-based reconstruction of water surfaces are identified: transparency, lack of texture and specular reflections. It is shown that many of these problems can be avoided by imaging at infrared wavelengths. Following a review of infrared radiometry, the key ingredients of surface reconstruction using the stereo principle are explained, including camera calibration, epipolar geometry and disparity estimation. A description of the stereo infrared camera system used in this work is given. An experimental validation of the system was performed at the Heidelberg wind-wave channel. Several stereo infrared image sequences of water waves recorded at this facility are used to demonstrate that a dense surface reconstruction of water waves is possible using this system. The accuracy of the reconstruction is experimentally assessed using a flat water surface as a reference plane.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Senet, Christian M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamics of dispersive boundaries: the determination of spatial hydrographic parameter maps from optical sea surface image sequences</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Universität Hamburg, FB Informatik</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Criminisi, A</style></author><author><style face="normal" font="default" size="100%">Shotton, J</style></author><author><style face="normal" font="default" size="100%">Blake, A</style></author><author><style face="normal" font="default" size="100%">Torr, P</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient dense stereo and novel-view synthesis for gaze manipulation in one-to-one teleconferencing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.research.microsoft.comi http://jamie.shotton.org/work/publications/TechRep2003-59.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new algorithm is proposed for novel-view synthesis, with particular application to teleconferencing. Given the video streams acquired by two cameras placed on either side of a computer monitor, the proposed algorithm synthesises images from a virtual camera in arbitrary position (typically located within the monitor area) to facilitate eye contact. The new technique is based on an improved, dynamic-programming, stereo algorithm for efficient novel-view generation. The two main contributions of this paper are: i) a new four-layer matching graph for dense-stereo dynamic-programming, that supports accurate occlusion labeling; ii) a compact geometric derivation for novel-view synthesis by direct projection of the minimum-cost surface. Furthermore, the paper presents an algorithm for the temporal maintenance of a background model to enhance the rendering of occlusions and reduce temporal artefacts (flicker); and a cost aggregation algorithm that acts directly in three-dimensional matching cost space. The proposed algorithm has been designed to work with input images with large disparity range, a common situation in one-to-one video-conferencing. The enhanced oc-clusion-handling capabilities of the new DP algorithm are evaluated against those of the most powerful state-of-the-art dynamic-programming and graph-cut techniques. A number of examples demonstrate the robustness of the algorithm to artefacts in stereo video streams. This includes demonstrations of cyclopean view synthesis in extended conversational sequences, synthesis from a freely translating virtual camera and, finally, basic 3D scene editing.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Helmut Herrmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ein System zur schnellen Entwicklung von Bildverarbeitungsalgorithmen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Univ.\ Mannheim</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of motion and parameters of heat transport from thermography</style></title><secondary-title><style face="normal" font="default" size="100%">Quantitative Infrared Thermography</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Christopher Popp</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exchange processes at the ocean surface: their role in coupling atmosphere and ocean, a contribution to the SOLAS project</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Munich, 22.-26.03.2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://old.dpg-tagungen.de/archive/2004/up_1.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Der Stoffaustausch zwischen Atmosphäre und Ozean ist trotz seiner Bedeutung für die globalen Stoffkreisläufe immer noch wenig verstanden und experimentell schwer zugänglich. In den letzten Jahre wurde eine neue Technik entwickelt, die es mittels aktiver Thermographie erlaubt, die Antwortfunktion des Transports durch die wasserseitige viskose Grenzschicht sowohl im Labor als auch im Feld zu messen. Damit ist es nicht nur möglich, innerhalb weniger Minuten die Transfergeschwindigkeit für Gase zu messen, sondern auch verschiedene Modelle zu unterscheiden und die Schmidtzahlabhängigkeit zu bestimmen. Dies bedeutet für zukünftige Messungen im Feld, dass Messungen der Konzentrationen von beliebigen Tracern im Wasser und der Atmosphäre ausreichend sind, um Flussraten relevanter Tracer zwischen Atmosphäre und Ozean zu bestimmen. Es sind keine aufwendigen und unsicheren direkten Flussraten (z.B. durch Eddykorrelations- oder -akkumulationsmessungen) mehr notwendig. Die neue Technik soll ein wesentliches Bindeglied zwischen den atmosphärischen und ozeanographischen Untersuchungen des internationalen SOLAS-Projekt werden.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kai Degreif</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gas exchange experiments using time resolved UV-spectroscopy</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Munich, 22.-26.03.2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.dpg-verhandlungen.de/2004/up_15.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The air sea gas exchange plays a key role in the development of the earth&#039;s climate. The oceans can act as a source or sink for climate relevant gases such as carbon dioxide, methane or dimethyl sulphide. Hydrodynamics in the viscous boundary layer at the free ocean surface and therefore the gas transfer across the interface are not well understood yet. UV-spectroscopy provides a direct and fast measuring method for gaseous or volatile tracers in the air and water phase. A wide range of hydrodynamic parameters, i.e. Schmidt numbers and solubilities is feasible with volatile hydrocarbons such as benzene or fluorobenzene. Multiple tracers may be observed simultaneously with the same non-invasive measuring technique. Laboratory experiments are performed at a 120 cm diameter circular wind-wave-facility in Heidelberg. A detailed study of gas transfer rate&#039;s dependence on the mean squared surface wave slope s^2, friction velocity u*, as well as on the Schmidt number Sc and solubility of the tracers will be performed. Also the influence of surface films or an increased viscosity is going to be examined. Preliminary results and further prospects are presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nielsen, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gasaustausch - Entwicklung und Ergebnis eines schnellen Massenbilanzverfahrens zur Messung der Austauschparameter</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/5032</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this thesis a novel, fast and highly accurate technique was developed for measuring the transfer velocity of gases across the air-water interface. The method is based on a simultaneous measurement of concentrations of gas tracers, both on the air and water side. It is possible to induce very high mass fluxes across the interface by increasing the water sided concentration. This leads to a significant increase in the air sided concentration of the tracer, which can be measured within a couple of minutes. By conducting measurements in this scheme, transfer velocities are recovered 10 to 100 times faster than would be possible by measuring the water sided concentration alone. Due to this significant reduction in measurement time, the novel technique makes it possible to conduct several experiments directly one after the other. For the first time was it possible to carry out a series of measurements in one day. The relative accuracy of measuring transfer velocities was better than 1%, which made it possible to determine the Schmidt number exponent precisely. The transition of the Schmidt number exponent from a flat to a rough water surface could be assessed for the first time. This allowed for a parametrization with the facet model. With respect to wind speed dependence a surprising result was achieved: The Schmidt number exponent transitions much slower than previously expected.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">&quot;GrabCut&quot; - Interactive foreground extraction using iterated graph cuts</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Transactions on Graphics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Alpha Matting</style></keyword><keyword><style  face="normal" font="default" size="100%">Foreground extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">Graph Cuts</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Editing</style></keyword><keyword><style  face="normal" font="default" size="100%">Interactive Image Segmentation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">309–314</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for &quot;border matting&quot; has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HD-WHOI Measurements October 2004 CISG</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Institute for Environmental Physics, University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Keuchel, J.</style></author><author><style face="normal" font="default" size="100%">Heiler, M.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hierarchical Image Segmentation based on Semidefinite Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc. 26th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3175</style></volume><pages><style face="normal" font="default" size="100%">120-128</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bruhn, Andrés</style></author><author><style face="normal" font="default" size="100%">Jakob, Tobias</style></author><author><style face="normal" font="default" size="100%">Fischer, Markus</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author><author><style face="normal" font="default" size="100%">Brüning, Ulrich</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">High performance cluster computing with 3-D nonlinear diffusion filters</style></title><secondary-title><style face="normal" font="default" size="100%">Real-Time Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">41–51</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigation of transport processes across the sea surface microlayer by infrared imagery</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">C8</style></number><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">C08S13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Heat is used as a proxy tracer for gases to study the transport processes across the sea surface microlayer. Infrared imaging techniques permit fast measurements of heat transfer velocities and give an insight into the transport mechanisms across the thermal sublayer. The observed fluctuations of the sea surface temperature suggest that surface renewal is the major turbulent transport mechanism at medium and high wind speeds. The scale space analysis of the temperature patterns at the sea surface with respect to their contribution to the skin-bulk temperature difference shows the turbulent nature of the transport process. Large-scale turbulence dominates the transport at low friction velocities, whereas small-scale turbulence is more dominant at higher wind friction. The skin-bulk temperature difference is estimated by fitting the measured sea surface temperature distribution with a PDF function based on a surface renewal model. Periodic heat flux switching in the wind-wave flume delivers independent estimates of surface and bulk temperature and verifies the statistical approach, whereas at very low wind speeds and film-covered surfaces the statistical method underestimates the skin-bulk temperature difference across the thermal sublayer. The large scatter of the transfer velocities when plotted versus wind speed indicates that not only the wind shear but also other processes such as the wave field and surfactants influences near-surface turbulence and thus air-water gas transfer.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bjoern H. Menze</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Klassifikation von Magnetresonanzspektren</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Fuß</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Kombinierte Höhen- und Neigungsmessung von winderzeugten Wasserwellen am Heidelberger Aeolotron</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/4820</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this thesis a new technique is presented, which is able to simultanously measure large and small scale water waves with high spatial resolution. This is achieved by a combined height and slope measurement in conjunction with digital image processing techniques. For the slope measurements a well established technique was improved. Utilizing light refraction at the water surface the slope of small scale waves is determined. This allows to sense the small scale structure of the water surface determined by capillary waves. To avoid systematic errors a detailed calibration procedure was developed. For absolute height measurements on coarser scales, a stereo technique based on the slope images was developed. The technical requirements and their implementation are described in detail and the applicability to water waves is shown. A combination of both techniques yields a scale overlapping method to measure water waves. Thus, simultanous measurements of gravity and capillary waves with high spatial resolution are enabled.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Hornegger, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections</style></title><secondary-title><style face="normal" font="default" size="100%">Methods of Information in Medicine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">320–326</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schlosser, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung von Diffusionskonstanten</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christopher J. Zappa</style></author><author><style face="normal" font="default" size="100%">William E. Asher</style></author><author><style face="normal" font="default" size="100%">Jessup, A. T.</style></author><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">S. R. Long</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Microbreaking and the enhancement of air-water transfer velocity</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">C08S16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The role of microscale wave breaking in controlling the air-water transfer of heat and gas is investigated in a laboratory wind-wave tank. The local heat transfer velocity, k_H , is measured using an active infrared technique and the tank-averaged gas transfer velocity, k_G , is measured using conservative mass balances. Simultaneous, colocated infrared and wave slope imagery show that wave-related areas of thermal boundary layer disruption and renewal are the turbulent wakes of microscale breaking waves, or microbreakers. The fractional area coverage of microbreakers, A _B , is found to be 0.1-0.4 in the wind speed range 4.2-9.3 m s-1 for cleaned and surfactant-influenced surfaces, and k_H and k_G are correlated with A _B . The correlation of k_H with A_B is independent of fetch and the presence of surfactants, while that for k_G with A_B depends on surfactants. Additionally, A_B is correlated with the mean square wave slope, &lt;S 2&gt;, which has shown promise as a correlate for k_G in previous studies. The ratio of k_H measured inside and outside the microbreaker wakes is 3.4, demonstrating that at these wind speeds, up to 75% of the transfer is the direct result of microbreaking. These results provide quantitative evidence that microbreaking is the dominant mechanism contributing to air-water heat and gas transfer at low to moderate wind speeds.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Matthias Gebhard</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multidimensionale Segmentierung in Bildfolgen und Quantifizierung dynamischer Prozesse</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/4392/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this interdisciplinary work I developed digital image processing methods for quantitative analysis of dynamics. The applications focused on biology and medicine. Confocal microscopes can resolve biological structures by the use of fluorescent markers. Due to a low signal to noise ratio the processing of noise reduction techniques was an important task. I developed a segmentation method in 2D and 3D based on deformable models. In 2D, I was able to show that the attraction range of the active B-spline contour could be increased in combination with a special external field. This improvement to the classical parametric active contour is especially important when the initialization of the curve is far beyond the object. In 3D, I introduced a method for simultaneously segmenting multiple objects in one image and adapted this approach to the special case of cell division. A cluster algorithm was applied to assign the extracted edge points to the different objects. With these methods, I was able to quantify the volume and area expansion of the membrane over time. To track multiple segmented objects over time, I have developed a particle-tracking algorithm, based on a Fuzzy decision kernel. Several applications show the benefit of the developed methods.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Sochen, Nir</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pajdla, T.</style></author><author><style face="normal" font="default" size="100%">Matas, J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision – ECCV 2004</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3024</style></volume><pages><style face="normal" font="default" size="100%">74-86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Restle</style></author><author><style face="normal" font="default" size="100%">Hissmann, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonparametric Smoothing of Height maps using ``Confidence&#039;&#039; values</style></title><secondary-title><style face="normal" font="default" size="100%">Optical Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">0049</style></number><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">866-871</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kohlberger, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Bruhn, A.</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Pajdla, T.</style></author><author><style face="normal" font="default" size="100%">Matas, J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision – ECCV 2004</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3024</style></volume><pages><style face="normal" font="default" size="100%">205-216</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Practical Handbook on Image Processing for Scientific and Technical Applications</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><edition><style face="normal" font="default" size="100%">2</style></edition><publisher><style face="normal" font="default" size="100%">CRC Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image processing is fast becoming a valuable tool for analyzing multidimensional data in all areas of natural science. Since the publication of the best-selling first edition of this handbook, the field of image processing has matured in many of its aspects from ad hoc, empirical approaches to a sound science based on established mathematical and physical principles. The Practical Handbook on Image Processing for Scientific and Technical Applications, Second Edition builds a sound basic knowledge of image processing, provides a critically evaluated collection of the best algorithms, and demonstrates those algorithms with real-world applications from many fields. It covers all aspects of image processing, from image formation to image analysis, and gives an up-to-date review of advanced concepts. Organized according to the hierarchy of tasks, each chapter includes a summary, an outline of the background the task requires, and a section of practical tips that help you avoid common errors and save valuable research time.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Detert, M.</style></author><author><style face="normal" font="default" size="100%">G. H. Jirka</style></author><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Michael Klar</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">H.-J. Köhler</style></author><author><style face="normal" font="default" size="100%">Wenka, T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pressure fluctuations within subsurface gravel bed caused by turbulent open-channel flow</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of River Flow 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">A. A. Balkema Publishers</style></publisher><pages><style face="normal" font="default" size="100%">695-701</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heck, D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Proximity Graphs for Nonlinear Dimension Reduction</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Strzodka, R.</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Real-time motion estimation and visualization on graphics cards</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings IEEE Visualization 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><pages><style face="normal" font="default" size="100%">545--552</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a tool for real-time visualization of motion features in 2D image sequences. The motion is estimated through an eigenvector analysis of the spatio-temporal structure tensor at every pixel location. This approach is computationally demanding but allows reliable velocity estimates as well as quality indicators for the obtained results. We use a 2D color map and a region of interest selector for the visualization of the velocities. On the selected velocities we apply a hierarchical smoothing scheme which allows the choice of the desired scale of the motion field. We demonstrate several examples of test sequences in which some persons are moving with different velocities than others. These persons are visually marked in the real-time display of the image sequence. The tool is also applied to angiography sequences to emphasize the blood flow and its distribution. An efficient processing of the data streams is achieved by mapping the operations onto the stream architecture of standard graphics cards. The card receives the images and performs both the motion estimation and visualization, taking advantage of the parallelism in the graphics processor and the superior memory bandwidth. The integration of data processing and visualization also saves on unnecessary data transfers and thus allows the real-time analysis of 320×240 images. We expect that on the newest generation of graphics hardware our tool could run in real time for the standard VGA format.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">Kuhl, S.</style></author><author><style face="normal" font="default" size="100%">S. Beirle</style></author><author><style face="normal" font="default" size="100%">Bucsela, E.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author><author><style face="normal" font="default" size="100%">Gleason, J.</style></author><author><style face="normal" font="default" size="100%">T. Wagner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Retrieval and analysis of stratospheric NO$_2$ from the Global Ozone Monitoring Experiment</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">D4</style></number><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">D04315, 1--11</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We describe the retrieval of stratospheric NO2 vertical column densities from the Global Ozone Monitoring Experiment (GOME) aboard the ERS-2 satellite. Different differential optical absorption spectroscopy (DOAS) evaluations are compared in order to investigate uncertainties caused by the diffuser plate. An improved version of our algorithm to separate the tropospheric and stratospheric fraction of NO2 from GOME satellite data is described and is used to extract a long term data set of stratospheric NO2 (1996-2000). In addition, the average seasonal variation in the global distribution is determined, which allows us to monitor and investigate specific aspects of stratospheric chemistry, in particular the interhemispheric comparison of stratospheric NO2. In contrast to other satellite observations (e.g., SAGE II, OSIRIS), GOME observations of stratospheric NO2 include the lower stratosphere. In general, our observations are in agreement with previous measurements and confirm the current knowledge of stratospheric nitrogen chemistry.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Küsters, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultane Tiefen- und Flussbestimmung pflanzlicher Oberflächen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/4818</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The subject of this thesis is the stereo based 3d survey of deformable objects. This includes the calculation of spatial structure and deformations of plant surfaces. The position in space and the movement field are simultaneously estimated as depth and optical flow in multi camera image sequences. This is realized by a near baseline stereo approach. Temporal multi camera sequences are taken as a 4d data set. A linear model is used to calculate depth. The brightness change constraint equation (BCCE) is extended by disparity terms. Parameters are estimated with a local differential total least squares method, the structure tensor approach, simultaneously yielding depth and flow information. An additional extension of the BCCE allows the simultaneous estimation of flow divergence and thus depth motion. The accuracy of this techniques is quantified on synthetic and real sequences. The results show the typical behavior for the structure tensor approach, high noise stability and accuracy. As a botanical application, a method for measuring of local relative area changes of moving curved surfaces is developed. The temporal course of those growth rate measurements shows a clear diurnal rhythm. Limiting the evaluations to those of static multi camera sequences allows the 3d survey of tree canopies as a smoothed envelope. To accommodate this for extended populations, a method is developed which creates a fusion of partial 3d reconstructions. This is applied in the high resolution reconstruction of the rainforest canopy in the Biosphere 2 Center, Arizona.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Klar</style></author><author><style face="normal" font="default" size="100%">Markus Jehle</style></author><author><style face="normal" font="default" size="100%">Detert, M.</style></author><author><style face="normal" font="default" size="100%">G. H. Jirka</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">H.-J. Köhler</style></author><author><style face="normal" font="default" size="100%">Wenka, T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simultaneous 3D PTV and micro-pressure sensor equipment for flow analysis in subsurface gravel layer</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of River Flow 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">A. A. Balkema Publishers</style></publisher><pages><style face="normal" font="default" size="100%">703--712</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kirchner, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial extensions to self-modeling curve resolution</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Feistner, L.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistische Karten in der Magnetresonanzspektroskopie</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, X.</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Studying dynamical processes of air-sea exchanges with air-water interface image techniques</style></title><secondary-title><style face="normal" font="default" size="100%">Recent Research Developments in Fluid Dynamics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www-pord.ucsd.edu/~xzhang/publication/fd1.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">57--87</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A surface renewal model to analyze infrared image sequences of the ocean surface for the study of air-sea heat and gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">C8</style></number><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">1-18</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Thermographic techniques are presented that directly measure the temperature difference across the thermal boundary layer at the sea surface, the probability density function of surface renewal, the net heat flux, and the heat transfer velocity during nighttime. The techniques are based on a model of surface renewal. Through the use of digital image processing techniques, temporally and spatially highly resolved measurements are feasible, limited only by the thermal imager. We present laboratory measurements from the Heidelberg Aeolotron and field measurements from the GasExII cruise taken at a spatial resolution of 3 mm and temporal resolution of 10 ms. The net heat flux estimates of the thermographic techniques and micrometeorological methods agree with an error less than 5% for conditions in which the surface renewal model is applicable. Experimental evidence is presented for the probability density function of surface renewal to be best described by a logarithmic normal distribution. At moderate and high wind speeds when the influence of surface films is not significant, surface renewal seems to be an adequate model for air-water heat exchange.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Neumann, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Steidl, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">SVM-based Feature Selection by Direct Objective Minimisation</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc. 26th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">3175</style></volume><pages><style face="normal" font="default" size="100%">212-219</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. Hader</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Weihs, C.</style></author><author><style face="normal" font="default" size="100%">Gaul, W.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Two-Stage Classification with Automatic Feature Selection for an Industrial Application</style></title><secondary-title><style face="normal" font="default" size="100%">Classification, the ubiquitous challenge: Proceedings of GfKl 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">137-144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ruhnau, P.</style></author><author><style face="normal" font="default" size="100%">Kohlberger, T.</style></author><author><style face="normal" font="default" size="100%">Nobach, H.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ruck, B.</style></author><author><style face="normal" font="default" size="100%">Leder, A.</style></author><author><style face="normal" font="default" size="100%">Dopheide</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Optical Flow Estimation for Particle Image Velocimetry</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Lasermethoden in der Strömungsmeßtechnik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Deutsche Gesellschaft für Laser-Anemometrie GALA e.V.</style></publisher><pub-location><style face="normal" font="default" size="100%">Karlsruhe</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">30</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Vergeichende Analyse moderner Bildsensoren für die optische Messtechnik</style></title><secondary-title><style face="normal" font="default" size="100%">Sensoren und Messsysteme 2004</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><volume><style face="normal" font="default" size="100%">1829</style></volume><pages><style face="normal" font="default" size="100%">317--324</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">VDI-Berichte</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>34</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Felix Vogel</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Visualisierung von Gasaustauschprozessen durch Lumineszenzspektroskopie mit Ruthenium als Phosphoreszenzstoff</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Miniforschung vom 01.03. -- 05.04.2004</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Vom Bild zur Information</style></title><secondary-title><style face="normal" font="default" size="100%">Ruperto Carola -- Forschungsmagazin der Universität Heidelberg</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><number><style face="normal" font="default" size="100%">0051</style></number><volume><style face="normal" font="default" size="100%">03.2004</style></volume><pages><style face="normal" font="default" size="100%">9-12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Vom Bild zur Information</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2004</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Stöhr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of Flow and Transport in Refractive Index Matched Porous Media</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/3733</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In the present work a novel method for the measurement of flow and transport in porous media has been developped. Through the employment of particularly applicative solids, liquids and fluorescent dyes and the application of a method for the highly precise matching of refractive indices, the dynamics of the dye distribution inside a threedimensional porous medium could be determined with a high temporal and spatial resolution using planar laser-induced fluorescence. For the data analysis specifically adapted algorithms for image preprocessing have been developed and a method for local parameter estimation has been adapted and significantly enhanced for the present application. The performed measurements represent the first simultaneous estimation of the longitudinal and both transversal hydrodynamic dispersion coefficients. Whereas for the longitudinal dispersion a previously known power-law could be confirmed, the significantly different behavior of the transversal dispersion in vertical and horizontal direction has been observed for the first time. Furthermore the measurements provide the first direct evidence for the existence of stagnant zones in the liquid phase, which have an important effect on the dispersion and are a potential explanation for the power-law behavior. Finally the described technique was used for the first highly resolved visualization of the flow of two immiscible liquids in a threedimensional porous medium.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hissmann, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bayessche Schätzung von Höhenkarten aus der Wei\DF licht-Interferometrie</style></title><secondary-title><style face="normal" font="default" size="100%">Oberflächenmesstechnik 2003</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><pages><style face="normal" font="default" size="100%">187--196</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">VDI-Berichte vol. 1806</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Smolyar, N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildgebende Spektroskopie an Pflanzenblättern</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/4268/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this work, new methods of image spectroscopy were developed that were used for studying spectral characteristics and spatial distribution of water and chlorophyll in plant leaves. Two image spectrometers for VIS and NIR areas using CCD cameras were built: a prismatic CCD spectrometer for receiving imagespectra with spectral and spatial coordinate; a multispectral CCD image spectrometer for investigation of spectral images for discrete wavelengths using a set of bandpass filters. In comparison with serial spectrometers, the developed image spectrometer has better characteristics. A innovative method for the processing of the spectral images was developed, allowing the analysis of a spectral images sequence and spectra of separate structural elements of inhomogeneous objects. The influence of multiple light scattering on optical characteristics of plant leaves was investigated. A method to determine the true value of absorption was developed considering the interaction of multiple light scattering and absorption. Separated spectra for plant leaves tissue and vein were investigated. A spectral technique to find detailed distribution of water and chlorophyll in leaves were developed. For the first time a profile images of water distribution on plant leaves were obtained. Four water states during physiological processes was shown and analyzed. The developed methods of image spectroscopy have a general character and can be used for studying non-uniform mirco- and macroobjects.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Keuchel, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">11</style></number><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">1364–1379</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mary C. Scholes</style></author><author><style face="normal" font="default" size="100%">Patricia A. Matrai</style></author><author><style face="normal" font="default" size="100%">Meinrat O. Andreae</style></author><author><style face="normal" font="default" size="100%">Keith A. Smith</style></author><author><style face="normal" font="default" size="100%">Martin R. Manning</style></author><author><style face="normal" font="default" size="100%">Paulo Artaxo</style></author><author><style face="normal" font="default" size="100%">Leonard A. Barrie</style></author><author><style face="normal" font="default" size="100%">Timothy S. Bates</style></author><author><style face="normal" font="default" size="100%">James H. Butler</style></author><author><style face="normal" font="default" size="100%">Paolo Ciccioli</style></author><author><style face="normal" font="default" size="100%">Stanislaw A. Cieslik</style></author><author><style face="normal" font="default" size="100%">Robert J. Delmas</style></author><author><style face="normal" font="default" size="100%">Frank J. Dentener</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Guy P. Brasseur</style></author><author><style face="normal" font="default" size="100%">Ronald G. Prinn</style></author><author><style face="normal" font="default" size="100%">Alexander A. P. Pszenny</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Biosphere-Atmosphere Interactions</style></title><secondary-title><style face="normal" font="default" size="100%">Atmospheric Chemistry in a Changing World, An Integration and Synthesis of a Decade of Tropospheric Chemistry Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">19--71</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">2</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Björn Ommer</style></author><author><style face="normal" font="default" size="100%">J. M. Buhmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Compositionality Architecture for Perceptual Feature Grouping</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2683</style></volume><pages><style face="normal" font="default" size="100%">275--290</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Hornegger, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discrete Tomography By Convex-Concave Regularization and D.C. Programming</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">December</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">15</style></number><publisher><style face="normal" font="default" size="100%">Dept. Math. and Comp. Science</style></publisher><pub-location><style face="normal" font="default" size="100%">University of Mannheim, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Comp. Science Series, Technical Report</style></work-type><notes><style face="normal" font="default" size="100%">\em Discr. Appl. Math.\/, accepted for publication</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kohlberger, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Bruhn, A.</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Michaelis, B.</style></author><author><style face="normal" font="default" size="100%">Krell, G.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Domain Decomposition for Parallel Variational Optical Flow Computation</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc. 25th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2781</style></volume><pages><style face="normal" font="default" size="100%">196–203</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kohlberger, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Bruhn, A.</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Domain Decomposition for Variational Optical Flow Computation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">07/2003</style></number><publisher><style face="normal" font="default" size="100%">Dept. Math. and Comp. Science</style></publisher><pub-location><style face="normal" font="default" size="100%">University of Mannheim, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Comp. Science Series, Technical Report</style></work-type><notes><style face="normal" font="default" size="100%">\em IEEE Trans. Image Processing\/, revised and resubmitted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Neumann, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Steidl, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">March</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">5</style></number><publisher><style face="normal" font="default" size="100%">Dept. Math. and Comp. Science</style></publisher><pub-location><style face="normal" font="default" size="100%">University of Mannheim, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Comp. Science Series, Technical Report</style></work-type><notes><style face="normal" font="default" size="100%">\emphPattern Recognition, revised and resubmitted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. Hader</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Schader, M.</style></author><author><style face="normal" font="default" size="100%">Gaul, W.</style></author><author><style face="normal" font="default" size="100%">Vichi, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Density Clustering</style></title><secondary-title><style face="normal" font="default" size="100%">Between Data Science and Applied Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">39-48</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of complex motion from thermographic image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">SPIE Proc.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">5073</style></volume><pages><style face="normal" font="default" size="100%">303--317</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this contribution a novel technique for computing complexmotion involving heat transport processes will be presented. Theproposed technique is a local gradient based approach, combiningtransport models with motion analysis. It allows for thesimultaneous estimation of both motion and parameter of anunderlying transport model. Since the analysis is based on thermalimage sequences, estimates are computed to a high temporal andspatial resolution, limited only by the resolution and frame rateof the employed IR camera. This novel technique was utilized onexchange processes at the atmosphere/ocean boundary, wheresignificant parameters of heat transfer could be measured and atransport model verified. Using the presented algorithms, surfaceflows as well as convergences and divergences on air-waterinterfaces can be measured accurately. Apart from applications inoceanography and botany, relevant benefits of the proposedtechnique to NDT will be presented. It is possible to compensatefor motion to reach accuracies much better than 1/10th of a pixel.Through the direct estimation of locally resolved diffusivities inmaterials, insights can be gained about defects present. Byestimating not only isotropic diffusion but also the whole matrixof anisotropic diffusion, the technique is highly relevant tomeasurements of composite materials.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimation of surface flow and net heat flux from infrared image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Mathematical Imaging and Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">159--174</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The study of dynamical processes at the sea surface interface using infrared image sequence analysis has gained tremendous popularity in recent years. Heat is transferred by similar transport mechanisms as gases relevant to global climatic changes. These similarities lead to the use of infrared cameras to remotely visualize and quantitatively estimate parameters of the underlying processes. Relevant parameters that provide important evidence about the models of air-sea gas transfer are the temperature difference across the thermal sub layer, the probability density function of surface renewal and the flow field at the surface. Being a driving force in air sea interactions, it is of equal importance to measure heat fluxes. In this paper we will present algorithms to measure the above parameters of air-sea gas transfer during night-time and show how to combine physical modeling and quantitative digital image processing algorithms to identify transport models. The image processing routines rely on an extension of optical flow computations to incorporate brightness changes in a total least squares (TLS) framework. Statistical methods are employed to support a model of gas transfer and estimate its parameters. Measurements in a laboratory environment were conducted and results verified with ground truth data gained from traditional measurement techniques.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Agrell, E.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Cawse, J. N.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring a space of materials: spatial sampling design and subset selection</style></title><secondary-title><style face="normal" font="default" size="100%">Experimental Design for Combinatorial and High Throughput Materials Development</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Wiley</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">1</style></notes><section><style face="normal" font="default" size="100%">13</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Norbert Kirchgeßner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Extraktion physiologischer Koordinatensysteme von Pflanzenwurzeln und -blättern aus Bildsequenzen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/3482</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The present work develops a method for analyzing the growth of plant leaves and roots in natural object coordinates using image processing techniques. The method is based on the structure tensor method that has already been used for the acquisition of growth charts in image coordinates. The main advantages of growth measurements in physiological coordinates is the possibility to compare the results of different measurements and the direct interpretation of the results. A method for extracting the coordinate axes is developed both for leaves and for roots. The center line of a root is identified as the physiological coordinate axis by means of a method based on active contours. In the case of leaves, the physiological coordinate axes are represented by their veins. The latter are searched for by use of a tracking algorithm which relies on matching methods. All physiological coordinate axes are represented in the form of B-splines. The growth charts are sampled from the B-spline positions, thus providing a coordinate transformation into the corresponding physiological coordinate system. The transformation has subpixel accuracy. Thus, it significantly stays behind the spatial resolution of the growth charts. The accomplishment of the present method provides a powerful tool for growth analysis of plant leaves and roots.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Neumann, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Steidl, G.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Petkov, N.</style></author><author><style face="normal" font="default" size="100%">Westenberg, M.A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Feasible Adaption Criteria for Hybrid Wavelet – Large Margin Classifiers</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Computer Analysis of Images and Patterns (CAIP&#039;03)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2756</style></volume><pages><style face="normal" font="default" size="100%">588–595</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">B. Michaelis</style></author><author><style face="normal" font="default" size="100%">G. Krell</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Image sequence analysis in environmental and live sciences</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 25th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2781</style></volume><pages><style face="normal" font="default" size="100%">608--617</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image sequence processing techniques are essential to study dynamical processes such as exchange, growth, and transport processes. In this survey paper, a generalized framework for the estimation of the parameters of dynamic processes including motion fields is presented. Some examples from environmental and live sciences illustrate how this framework helped to tackles some key questions that could not be solved without taking and analyzing image sequences.</style></abstract><notes><style face="normal" font="default" size="100%">invited</style></notes><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">N. Spichtinger</style></author><author><style face="normal" font="default" size="100%">A. Stohl</style></author><author><style face="normal" font="default" size="100%">G. Held</style></author><author><style face="normal" font="default" size="100%">S. Beirle</style></author><author><style face="normal" font="default" size="100%">T. Wagner</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intercontinental transport of nitrogen oxide pollution plumes</style></title><secondary-title><style face="normal" font="default" size="100%">Atmos. Chem. Phys.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">387--393</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linear Multi-View Reconstruction for Translating Cameras</style></title><secondary-title><style face="normal" font="default" size="100%">Nada.Kth.Se</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nada.kth.se/ carstenr/papers/paper_ssab03.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a linear multi view reconstruction algorithm for translating cameras with fixed internal parameters. The main advantages of this method are a) points and camera centers are computed simultaneously from one linear system containing all image data b) the allowance of arbitrary missing data. We show that the key to linearize the SFM problem is the infinite homography which comprises of the cameras&#039; calibration and rotation. This insight unifies reconstruction methods for calibrated cameras, e.g. Olien-sis [9], and uncalibrated cameras, e.g. Rother-Carlsson [10]. A further contribution of this paper is the summary and comparison of different approaches to determine the infinite homography.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.nada.kth.se/carstenr</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">1210–1217</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a new linear method for reconstructing simultaneously 3D features (points, lines and planes) and cameras from many perspective views by solving a single linear system. It assumes that a real or virtual reference plane is visible in all views. We call it the Direct Reference Plane (DRP) method. It is well known that the projection relationship between uncalibrated cameras and 3D features is non-linear in the absence of a reference plane. With a known reference plane, points and cameras have a linear relationship, as shown in [16]. The main contribution of this paper is that lines and cameras, as well as, planes and cameras also have a linear relationship. Consequently, all 3D features and all cameras can be reconstructed simultaneously from a single linear system, which handles missing image measurements naturally. A further contribution is an extensive experimental comparison, using real data, of different reference plane and non-reference plane reconstruction methods. For difficult reference plane scenarios, with point or line features, the DRP method is superior to all compared methods. Finally, an extensive list of reference plane scenarios is presented, which shows the wide applicability of the DRP method.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Schüle, T.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Hornegger, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections</style></title><secondary-title><style face="normal" font="default" size="100%">Bildverarbeitung für die Medizin 2003</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pages><style face="normal" font="default" size="100%">41–45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weber, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Hornegger, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Linear Programming Relaxation for Binary Tomography with Smoothness Priors</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA&#039;03)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May 14-16</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Palermo, Italy</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Stöhr</style></author><author><style face="normal" font="default" size="100%">Roth, K.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurement of 3D pore-scale flow in index-matched porous media</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">35</style></volume><pages><style face="normal" font="default" size="100%">159--166</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present the experimental analysis of fluid flow at the pore-scale of a transparent porous medium with matched refractive indices of the solid and liquid phases. The planar laser-induced fluorescence (PLIF) technique described is the first to simultaneously visualize the 3D pore-scale flow of two immiscible liquid phases in porous media. Through the application of a highly precise index matching method and the employment of up-to-date CCD imaging hardware, the system features a high spatial resolution and sampling rate. The method was used to study the dispersion of a tracer dye in single-phase flow and the displacement of oil by water in an imbibition process.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.google.com/url?sa=t&amp;rct=j&amp;q=&amp;esrc=s&amp;source=web&amp;cd=4&amp;cad=rja&amp;uact=8&amp;ved=0CDUQFjAD&amp;url=http%3A%2F%2Fwww.nada.kth.se%2Futbildning%2Fforsk.utb%2Favhandlingar%2Fdokt%2Frother.pdf&amp;ei=AyX_VPKmIomeNqeOgpgL&amp;usg=AFQjCNHCmc75P5EHYWLtBUaHtUAs4yOnJw&amp;bvm=bv.</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">9172834226</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Reconstructing a 3-dimensional scene from a set of 2-dimensional images is a fundamental problem in computer vision. A systemcapable of performing this task can be used in many applications in robotics, architecture, archaeology, biometrics, human computer interaction and the movie and entertainment industry. Most existing reconstruction approaches exploit one source of information to tackle the problem. This is the motion of the camera, the 2D images are taken from different viewpoints.We exploit an additional information source, the reference plane,whichmakes it possible to reconstruct difficult scenes where other methods fail. A real scene plane may serve as the reference plane. Furthermore, there are many alternative techniques to obtain virtual reference planes. For instance, orthogonal directions in the scene provide a virtual reference plane, the plane at infinity, or images taken with a parallel projection camera. A collection of known and novel reference plane scenarios is presented in this thesis. The main contribution of the thesis is a novel multi-view reconstruction approach us- ing a reference plane. The technique is applicable to three different feature types, points, lines and planes. The novelty of our approach is that all cameras and all features (off the reference plane) are reconstructed simultaneously from a single linear system of im- age measurements. It is based on the novel observation that cameras and features have a linear relationship if a reference plane is known. In the absence of a reference plane, this relationship is non-linear. Thus many previous methods must reconstruct features and cameras sequentially. Another class of methods, popular in the literature, is factorization, but, in contrast to our approach, this has the serious practical drawback that all features are required to be visible in all views. Extensive experiments show that our approach is superior to all previously suggested reference plane and non-reference plane methods for difficult reference plane scenarios. Furthermore, the thesis studies scenes which do not have a unique reconstruction, so- called critical configurations. It is proven that in the presence of a reference plane the set of critical configurations is small. Finally, the thesis introduces a complete, automatic multi-view reconstruction system based on the reference plane approach. The input data is a set of images and the output a 3D point reconstruction together with the corresponding cameras.</style></abstract><work-type><style face="normal" font="default" size="100%">phd</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heiler, M.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Natural Statistics for Natural Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE Int. Conf. Computer Vision (ICCV 2003)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct. 13-16</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Nice, France</style></pub-location><pages><style face="normal" font="default" size="100%">1259-1266</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. Eisele</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Luc Van Gool</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A new approach for defect detection in X-ray CT images</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2449</style></volume><pages><style face="normal" font="default" size="100%">345-352</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture notes in computer science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author><author><style face="normal" font="default" size="100%">Kalkenings, R.</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Observational studies of parameters influencing air--sea gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Geophysical Research Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">09328</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Restle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimierung der Wei\DFlichtinterferometrie für Applikationen der industriellen Qualitätskontrolle</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hermann Gröning</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Radiometrische Kalibrierung und Charakterisierung von CCD- uund CMOS-Bildsensoren und Monokulares 3D-Tracking in Echtzeit</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/3589</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this presented thesis a CCD and a CMOS image sensor are characterized relative to their fundamental radiometric properties. A new technic for a monocular 3D-Tracking is realized. Dark currents of both sensors are examined. The linearity and the global total variance of noise of the CCD image sensor are determined and the fixed-pattern noise is corrected. The absolute quantum efficiency is calculated from calibrated data of the spectral radiance of the integration sphere. Additionally the signal noise ratio and the relative error are determined. The response of the CMOS sensor is modeled and can be computed from the calibrated data of the spectral radiance with known data of the quantum efficiency. The global total variance of noise is measured. The signal noise ratio and the relative error are computed with these data.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bruhn, Andrés</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author><author><style face="normal" font="default" size="100%">Feddern, Christian</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Petkov, N.</style></author><author><style face="normal" font="default" size="100%">Westenberg, M.A.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Real-Time Optic Flow Computation with Variational Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Computer Analysis of Images and Patterns (CAIP&#039;03)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2756</style></volume><pages><style face="normal" font="default" size="100%">222-229</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Shape Statistics in Kernel Space for Variational Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">1929--1943</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Shape Statistics in Kernel Space for Variational Image Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">9</style></number><volume><style face="normal" font="default" size="100%">36</style></volume><pages><style face="normal" font="default" size="100%">1929–1943</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Uttenweiler</style></author><author><style face="normal" font="default" size="100%">C. Weber</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Rainer H. A. Fink</style></author><author><style face="normal" font="default" size="100%">Schaar, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatiotemporal anisotropic diffusion filtering to improve signal-to-noise ratios and object restoration in fluorescence microscopic image sequences.</style></title><secondary-title><style face="normal" font="default" size="100%">J Biomed Opt</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1117/1.1527627</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><publisher><style face="normal" font="default" size="100%">Ruprecht-Karls-Universität Heidelberg, Institut für Physiologie und Pathophysiologie Medical Biophysics, Im Neuenheimer Feld 326, 69120 Heidelberg, Germany. dietmar.uttenweiler@urz.uni-heidelberg.de</style></publisher><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">40--47</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present an approach for significantly improving the quantitative analysis of motion in noisy fluorescence microscopic image sequences. The new partial differential equation based method is a general extension of a 2-D nonlinear anisotropic diffusion filtering scheme to a specially adapted 3D nonlinear anisotropic diffusion filtering scheme, with two spatial image dimensions and the time t in the image sequence as the third dimension. Motion in image sequences is considered as oriented, line-like structures in the spatiotemporal x,y,t domain, which are determined by the structure tensor method. Image enhancement is achieved by a structure adopted smoothing kernel in three dimensions, thereby using the full 3D information inherent in spatiotemporal image sequences. As an example for low signal-to-noise ratio (SNR) microscopic image sequences we have applied this method to noisy in vitro motility assay data, where fluorescently labeled actin filaments move over a surface of immobilized myosin. With the 3D anisotropic diffusion filtering the SNR is significantly improved (by a factor of 3.8) and closed object structures are reliably restored, which were originally degraded by noise. Generally, this approach is very valuable for all applications where motion has to be measured quantitatively in low light level fluorescence microscopic image sequences of cellular, subcellular, and molecular processes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical Shape Knowledge in Variational Motion Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Image and Vision Comp.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">77-86</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Subgraph Matching with Semidefinite Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA&#039;03)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May 14-16</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Palermo, Italy</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A surface renewal model to analyze infrared image sequences for the study of air-sea heat and gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Geophysical Research Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><pages><style face="normal" font="default" size="100%">11893</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. Hader</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">System Concept for Image Sequence Classification in Laser Welding</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2781</style></volume><pages><style face="normal" font="default" size="100%">212-219</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards objective performance analysis for estimation of complex motion: analytic motion modeling, filter optimization, and test sequences</style></title><secondary-title><style face="normal" font="default" size="100%">In Proceedings of IEEE International Conference on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">./pdf/2003/barth_ICIP2003.pdf:PDF</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Sochen, Nir</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Griffin, L.D.</style></author><author><style face="normal" font="default" size="100%">Lillholm, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space Methods in Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2695</style></volume><pages><style face="normal" font="default" size="100%">388--400</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Sochen, Nir</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Griffin, L.D.</style></author><author><style face="normal" font="default" size="100%">Lillholm, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space Methods in Computer Vision</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2695</style></volume><pages><style face="normal" font="default" size="100%">388–400</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kohlberger, T.</style></author><author><style face="normal" font="default" size="100%">Mémin, E.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Griffin, L.D.</style></author><author><style face="normal" font="default" size="100%">Lillholm, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Dense Motion Estimation Using the Helmholtz Decomposition</style></title><secondary-title><style face="normal" font="default" size="100%">Scale Space Methods in Computer Vision</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNCS</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2695</style></volume><pages><style face="normal" font="default" size="100%">432–448</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bruhn, Andrés</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author><author><style face="normal" font="default" size="100%">Feddern, Christian</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Optic Flow Computation in Real-Time</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">89</style></number><publisher><style face="normal" font="default" size="100%">Dept. Math. and Comp. Science</style></publisher><pub-location><style face="normal" font="default" size="100%">Saarland University, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Fachrichtung 6.1 – Mathematik, Technical Report</style></work-type><notes><style face="normal" font="default" size="100%">\em IEEE Trans. Image Processing\/, revised and resubmitted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thomas Luhmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">3D-Blattbewegung und Wachstum</style></title><secondary-title><style face="normal" font="default" size="100%">Nahbereichsphotogrammetrie in der Praxis, Beispiele und Problemlösungen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/96618503X</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Wichmann</style></publisher><pages><style face="normal" font="default" size="100%">267--270</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Klar</style></author><author><style face="normal" font="default" size="100%">P. Stybalkowski</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thomas Luhmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">3D-Strömungsmessung in Kiesporen</style></title><secondary-title><style face="normal" font="default" size="100%">Nahbereichsphotogrammetrie in der Praxis, Beispiele und Problemlösungen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/96618503X</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Wichmann</style></publisher><pages><style face="normal" font="default" size="100%">247--250</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hinterberger, Walter</style></author><author><style face="normal" font="default" size="100%">Scherzer, Otmar</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of Optical Flow Models in the Framework of Calculus of Variations</style></title><secondary-title><style face="normal" font="default" size="100%">Numer. Funct. Anal. Optimiz.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">1/2</style></number><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">69–89</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. Eisele</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automated defect detection and evaluation in X-ray CT images</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">www.ub.uni-heidelberg.de/archiv/3106</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Keuchel, J.</style></author><author><style face="normal" font="default" size="100%">Naumann, S.</style></author><author><style face="normal" font="default" size="100%">Heiler, M.</style></author><author><style face="normal" font="default" size="100%">Siegmund, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic Land Cover Analysis for Tenerife by Supervised Classification using Remotely Sensed Data</style></title><secondary-title><style face="normal" font="default" size="100%">Remote Sensing of Environment</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Subitted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Thiel, W.</style></author><author><style face="normal" font="default" size="100%">van Gunsteren, W. F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chemical library subset selection algorithms: a unified derivation using spatial statistics</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Computer Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">414-428</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">S. R. Long</style></author><author><style face="normal" font="default" size="100%">J. Klinke</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. S. Saltzman</style></author><author><style face="normal" font="default" size="100%">M. A. Donelan</style></author><author><style face="normal" font="default" size="100%">R. Wanninkhof</style></author><author><style face="normal" font="default" size="100%">W. M. Drennan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A closer look at short waves generated by wave interactions with adverse currents</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">American Geophysical Union</style></publisher><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">121--128</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Geophysical Monograph</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bruhn, Andrés</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">van Gool, L.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Combining the Advantages of Local and Global Optic Flow Methods</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc. 24th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Zürich, Switzerland</style></pub-location><volume><style face="normal" font="default" size="100%">2449</style></volume><pages><style face="normal" font="default" size="100%">454–462</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Luc Van Gool</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Dense parameter fields from total least squares</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 24th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">LNCS 2449</style></volume><pages><style face="normal" font="default" size="100%">379--386</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A method for the interpolation of parameter fields estimated by total least squares is presented. This is applied to the study of dynamic processes where the motion and further values such as divergence or brightness changes are parameterised in a partial differential equation. For the regularisation we introduce a constraint that restricts the solution only in the subspace determined by the total least squares procedure. The performance is illustrated on both synthetic and real test data.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bruhn, Andrés</style></author><author><style face="normal" font="default" size="100%">Jakob, Tobias</style></author><author><style face="normal" font="default" size="100%">Fischer, Markus</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author><author><style face="normal" font="default" size="100%">Brüning, Ulrich</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">van Gool, L.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Designing 3–D Nonlinear Diffusion Filters for High Performance Cluster Computing</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc. 24th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Zürich, Switzerland</style></pub-location><volume><style face="normal" font="default" size="100%">2449</style></volume><pages><style face="normal" font="default" size="100%">290–297</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Tischhäuser, Florian</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">295–313</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digital Image Processing</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/963597833</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">5th</style></edition><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digitale Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/963601830</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">Fünfte</style></edition><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christopher J. Zappa</style></author><author><style face="normal" font="default" size="100%">William E. Asher</style></author><author><style face="normal" font="default" size="100%">Jessup, A. T.</style></author><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">S. R. Long</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. S. Saltzman</style></author><author><style face="normal" font="default" size="100%">M. A. Donelan</style></author><author><style face="normal" font="default" size="100%">R. Wanninkhof</style></author><author><style face="normal" font="default" size="100%">W. M. Drennan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Effect of microscale wave breaking on air-water gas transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">American Geophysical Union</style></publisher><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">23--29</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Geophysical Monograph</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Beurer, Matthias</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung von Algorithmen zur Analyse interferometrischer Aufnahmen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Entwicklung von Algorithmen zur Analyse interferometrischer Aufnahmen für die schnelle, hochpräzise 3D-Messtechnik: Die vorliegende Diplomarbeit beschäftigt sich mit der interferometrischen Vermessung von optisch glatten Oberflächen. Im Gegensatz zu phasenschiebenden Methoden oder der Weißlichtinterferometrie, die zur Vermessung mehrere phasenverschobene Aufnahmen des Messobjektes benötigen, konzentriert sich die Arbeit auf die Auswertung eines einzelnen Interferogramms. Die Bildaufnahme ist so mit einer kürzeren Messzeit und einem geringeren technischen Aufwand verbunden. Das Ziel der Auswertung ist die Ermittlung einfacher Oberflächenparameter wie Neigung oder Höhe - nicht die vollständige Rekonstruktion der Oberflächentopologie. Damit kann auf die ansonsten meist notwendige, rechenaufwendige und fehleranfällige Phasenentfaltung verzichtet werden. Die entwickelten Algorithmen basieren auf der Riesztransformation. Diese stellt in der Bildverarbeitung eine neue und vielfach noch unbekannte Methode zur Extraktion lokaler Eigenschaften aus Streifenbildern dar, die sich besonders durch ihre Isotropie-Eigenschaft auszeichnet. Als praxisrelevante Anwendungen werden die Bestimmung der Neigung, der Verkippung und der absoluten Höhe von ebenen Flächen vorgestellt, sowie die Vermessung von Objektstufen. Die letzten beiden Anwendungen werden durch die Verwendung von Weißlicht ermöglicht und sind in der klassischen Einzelbildauswertung nicht realisierbar.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">John L. Barron</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Estimating expansion rates from range data sequences</style></title><secondary-title><style face="normal" font="default" size="100%">15th International Conference on Vision Interface</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><pages><style face="normal" font="default" size="100%">339 - 346</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">John L. Barron</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluating the range flow motion constraint.</style></title><secondary-title><style face="normal" font="default" size="100%">ICPR</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><pages><style face="normal" font="default" size="100%">517--</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cavallo, Antonio</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Four dimensional particle tracking in biological dynamic processes</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/2471/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Here is presented a general approach to data analysis in multidimensional space using the run length encoding process and volume field method. A study on volume field was carried in order to test the limits on the method and testing against simple Gaussian objects (spots) was made: quantitative measurement was then carried using an angle distribution parameter between the computed results and the expected values. An application of volume field method was tested against results from simulations of chromosome territories using the spherical loop domain (SCD) and the quantitative comparison was made in order to clarify real applications in biologic related field. Two experiment were made, using the run length encoding method. From biological samples subjected to in vivo labeling an then to FISH hybridization, we analyzed the sub-chromosomal movements to see if there were modifications to the underlying structures: we quantify these displacements and compared to other measurements made with other methods. A time recorded HeLA cell sample in vivo labeled was analyzed and his internal marked chromosomes were tracked to analyze displacements related to the activities: here, using the run length encoding, spots were tracked and extracted. A successive Monte Carlo minimization algorithm was used in order to reduce systematic errors.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Martin Brocke</style></author><author><style face="normal" font="default" size="100%">H. Eisele</style></author><author><style face="normal" font="default" size="100%">S. Hader</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">W. Happold</style></author><author><style face="normal" font="default" size="100%">Florian Raisch</style></author><author><style face="normal" font="default" size="100%">J. Restle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Für Anspruchsvolle - Multidimensionale Bildverarbeitung in der Produktion</style></title><secondary-title><style face="normal" font="default" size="100%">Qualität und Zuverlässigkeit</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">1154-1159</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tobias Dierig</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gewinnung von Tiefenkarten aus Fokusserien</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/2461</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">N. Spichtinger</style></author><author><style face="normal" font="default" size="100%">A. Stohl</style></author><author><style face="normal" font="default" size="100%">G. Held</style></author><author><style face="normal" font="default" size="100%">S. Beirle</style></author><author><style face="normal" font="default" size="100%">T. Wagner</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intercontinental transport of nitrogen oxide pollution plumes</style></title><secondary-title><style face="normal" font="default" size="100%">acpd</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">2151--2165</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. S. Saltzman</style></author><author><style face="normal" font="default" size="100%">M. A. Donelan</style></author><author><style face="normal" font="default" size="100%">R. Wanninkhof</style></author><author><style face="normal" font="default" size="100%">W. M. Drennan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">On the investigations of statistical properties of the micro turbulence at the ocean surface</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">American Geophysical Union</style></publisher><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">51--57</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Geophysical Monograph</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Küsters, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A linear model for simultaneous estimation of 3D motion and depth</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedins of IEEE Workshop on Motion and Video Computing 2002, Orlando</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Carlsson, Stefan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linear multi view reconstruction and camera recovery using a reference plane</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Critical configurations</style></keyword><keyword><style  face="normal" font="default" size="100%">Duality</style></keyword><keyword><style  face="normal" font="default" size="100%">Missing data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multiple views</style></keyword><keyword><style  face="normal" font="default" size="100%">Planar parallax</style></keyword><keyword><style  face="normal" font="default" size="100%">Projective reconstruction</style></keyword><keyword><style  face="normal" font="default" size="100%">Reference plane</style></keyword><keyword><style  face="normal" font="default" size="100%">Structure from motion</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">2-3</style></number><volume><style face="normal" font="default" size="100%">49</style></volume><pages><style face="normal" font="default" size="100%">117–141</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a linear algorithm for simultaneous computation of 3D points and camera positions from multiple perspective views based on having a reference plane visible in all views. The reconstruction and camera recovery is achieved in a single step by finding the null-space of a matrix built from image data using Singular Value Decomposition. Contrary to factorization algorithms this approach does not need to have all points visible in all views. This paper investigates two reference plane configurations: Finite reference planes defined by four coplanar points and infinite reference planes defined by vanishing points. A further contribution of this paper is the study of critical configurations for configurations with four coplanar points. By simultaneously reconstructing points and views we can exploit the numerical stabilizing effect of having wide spread cameras with large mutual baselines. This is demonstrated by reconstructing the outside and inside (courtyard) of a building on the basis of 35 views in one single Singular Value Decomposition.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Carlsson, Stefan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linear multi view reconstruction with missing data</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Affine and Projective Cameras</style></keyword><keyword><style  face="normal" font="default" size="100%">Linear Multiple View Reconstruction</style></keyword><keyword><style  face="normal" font="default" size="100%">Missing data</style></keyword><keyword><style  face="normal" font="default" size="100%">Structure from motion</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">2351</style></volume><pages><style face="normal" font="default" size="100%">209–324</style></pages><isbn><style face="normal" font="default" size="100%">9783540437444</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">General multi view reconstruction from affine or projective cameras has so far been solved most efficiently using methods of factorizing image data matrices into camera and scene parameters. This can be done directly for affine cameras [18] and after computing epipolar geometry for projective cameras [17]. A notorious problem has been the fact that these factorization methods require all points to be visible in all views. This paper presents alternative algorithms for general affine and projective views of multiple points where a) points and camera centers are computed as the nullspace of one linear system constructed from all the image data b) only three points have to be visible in all views. The latter requirement increases the flexibility and usefulness of 3D reconstruction from multiple views. In the case of projective views and unknown epipolar geometry, an additional algorithm is presented which initially assumes affine views and compensates iteratively for the perspective effects. In this paper affine cameras are represented in a projective framework which is novel and leads to a unified treatment of parallel and perspective projection in a single framework. The experiments cover a wide range of different camera motions and compare the presented algorithms to factorization methods, including approaches which handle missing data.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Tobias Dierig</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Würtz, R. P.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Lappe, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Local models for dynamic processes in image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Dynamic Perception</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Aka GmbH</style></publisher><pages><style face="normal" font="default" size="100%">59--64</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Maldague, X. P.</style></author><author><style face="normal" font="default" size="100%">Rozlosnik, A. E.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring important parameters for air-sea heat exchange</style></title><secondary-title><style face="normal" font="default" size="100%">ThermoSense</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">SPIE</style></publisher><volume><style face="normal" font="default" size="100%">4710</style></volume><pages><style face="normal" font="default" size="100%">171--182</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. S. Saltzman</style></author><author><style face="normal" font="default" size="100%">M. A. Donelan</style></author><author><style face="normal" font="default" size="100%">R. Wanninkhof</style></author><author><style face="normal" font="default" size="100%">W. M. Drennan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring the sea surface heat flux and probability distribution of surface renewal events</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">American Geophysical Union</style></publisher><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">109-114</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Geophysical Monograph</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Klar</style></author><author><style face="normal" font="default" size="100%">P. Stybalkowski</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A miniaturized 3-D particle-tracking velocimetry system to measure the pore flow within a gravel layer</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 11th Int. Symp. Applications of Laser Techniques to Fluid Mechanics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://in3.dem.ist.utl.pt/lxlaser2002/papers.asp</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">2.3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Luc Van Gool</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Mixed OLS-TLS for the estimation of dynamic processes with a linear source term</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 24th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><volume><style face="normal" font="default" size="100%">2449</style></volume><pages><style face="normal" font="default" size="100%">463--471</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a novel technique to eliminate strong biases in parameter estimation were part of the data matrix is not corrupted by errors. Problems of this type occur in the simultaneous estimation of optical flow and the parameter of linear brightness change as well as in range flow estimation. For attaining highly accurate optical flow estimations under real world situations as required by a number of scientific applications, the standard brightness change constraint equation is violated. Very often the brightness change has to be modelled by a linear source term. In this problem as well as in range flow estimation, part of the data term consists of an exactly known constant. Total least squares (TLS) assumes the error in the data terms to be identically distributed, thus leading to strong biases in the equations at hand. The approach presented in this paper is based on a mixture of ordinary least squares (OLS) and total least squares, thus resolving the bias encountered in TLS alone. Apart from a thorough performance analysis of the novel estimator, a number of applications are presented.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gee, P. J.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Schuler, L. D.</style></author><author><style face="normal" font="default" size="100%">van Gunsteren, W. F.</style></author><author><style face="normal" font="default" size="100%">Duchardt, E.</style></author><author><style face="normal" font="default" size="100%">Schwalbe, H.</style></author><author><style face="normal" font="default" size="100%">Albert, M.</style></author><author><style face="normal" font="default" size="100%">Seebach, D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A molecular dynamics simulation study of the conformational preferences of oligo-(3- hydroxyalcanoic acids) in chloroform solution</style></title><secondary-title><style face="normal" font="default" size="100%">Helv. Chim. Acta</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">85</style></volume><pages><style face="normal" font="default" size="100%">618-632</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">van Gool, L.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc. 24th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Zürich, Switzerland</style></pub-location><volume><style face="normal" font="default" size="100%">2449</style></volume><pages><style face="normal" font="default" size="100%">472–480</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Martin Brocke</style></author><author><style face="normal" font="default" size="100%">H. Eisele</style></author><author><style face="normal" font="default" size="100%">S. Hader</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">W. Happold</style></author><author><style face="normal" font="default" size="100%">Florian Raisch</style></author><author><style face="normal" font="default" size="100%">J. Restle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multidimensionale Bildverarbeitung in der Produktion</style></title><secondary-title><style face="normal" font="default" size="100%">QZ</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.qz-online.de/qz-zeitschrift/archiv/artikel/multidimensionale-bildverarbeitung-in-der-produktion-fuer-anspruchsvolle-338129.html</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">1154--1159</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new approach to vanishing point detection in architectural environments</style></title><secondary-title><style face="normal" font="default" size="100%">Image and Vision Computing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Architecture</style></keyword><keyword><style  face="normal" font="default" size="100%">Camera calibration</style></keyword><keyword><style  face="normal" font="default" size="100%">Geometric constraints</style></keyword><keyword><style  face="normal" font="default" size="100%">Vanishing lines</style></keyword><keyword><style  face="normal" font="default" size="100%">Vanishing points</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">9-10</style></number><volume><style face="normal" font="default" size="100%">20</style></volume><pages><style face="normal" font="default" size="100%">647–655</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A man-made environment is characterized by many parallel lines and orthogonal edges. In this article, a new method for detecting the three mutually orthogonal directions of such an environment is presented. Since real-time performance is not necessary for architectural applications, such as building reconstruction, a computationally intensive approach was chosen. However, this enables us to avoid one fundamental error of most other existing techniques. Compared to theirs, our approach is furthermore more rigorous, since all conditions given by three mutually orthogonal directions are identified and utilized. We assume a partly calibrated camera with unknown focal length and unknown principal point. By examining these camera parameters, which can be determined from orthogonal directions, falsely detected vanishing points may be rejected. © 2002 Elsevier Science B.V. All rights reserved.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Heyden, A.</style></author><author><style face="normal" font="default" size="100%">Sparr, G.</style></author><author><style face="normal" font="default" size="100%">Johansen, P.</style></author><author><style face="normal" font="default" size="100%">Nielsen, M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonlinear Shape Statistics in Mumford-Shah Based Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision -- ECCV 2002)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">2351</style></volume><pages><style face="normal" font="default" size="100%">93--108</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Heyden, A.</style></author><author><style face="normal" font="default" size="100%">Sparr, G.</style></author><author><style face="normal" font="default" size="100%">Nielsen, M.</style></author><author><style face="normal" font="default" size="100%">Johansen, P.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonlinear Shape Statistics in Mumford-Shah Based Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision – ECCV 2002)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><volume><style face="normal" font="default" size="100%">2351</style></volume><pages><style face="normal" font="default" size="100%">93–108</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel insights into heat transfer across the aqueous boundary layer by infrared imagery and its application to air-sea exchange processes</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of Eurotherm 71 on Visualization, Imaging and Data Analysis In Convective Heat and Mass Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">E. J. Bock</style></author><author><style face="normal" font="default" size="100%">J. B. Edson</style></author><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author><author><style face="normal" font="default" size="100%">T. Hara</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">W. R. McGilles</style></author><author><style face="normal" font="default" size="100%">S. P. McKenna</style></author><author><style face="normal" font="default" size="100%">R. K. Nelson</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">B. M. Uz</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">M. A. Donelan</style></author><author><style face="normal" font="default" size="100%">W. M. Drennan</style></author><author><style face="normal" font="default" size="100%">R. Wanninkhof</style></author><author><style face="normal" font="default" size="100%">E. S. Saltzman</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Overview of the CoOP experiments: physical and chemical measurements parameterizing air-sea gas transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">American Geophysical Union</style></publisher><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">39--44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Geophysical Monograph</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Roth, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Performance Evaluation of a Convex Relaxation Approach to the Quadratic Assignment of Relational Object Views</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Feb.</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">02/2002</style></number><publisher><style face="normal" font="default" size="100%">Dept. Math. and Comp. Science</style></publisher><pub-location><style face="normal" font="default" size="100%">University of Mannheim, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Comp. Science Series, Technical Report</style></work-type><notes><style face="normal" font="default" size="100%">\em Image and Vision Comp.\/, submitted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. S. Saltzman</style></author><author><style face="normal" font="default" size="100%">M. A. Donelan</style></author><author><style face="normal" font="default" size="100%">R. Wanninkhof</style></author><author><style face="normal" font="default" size="100%">W. M. Drennan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Physics from IR image sequences: Quantitative analysis of transport models and parameters of air-sea gas transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">American Geophysical Union</style></publisher><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">103--108</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">invited</style></notes><custom3><style face="normal" font="default" size="100%">Geophysical Monograph</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Carlsson, Stefan</style></author><author><style face="normal" font="default" size="100%">Tell, Dennis</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Projective factorization of planes and cameras in multiple views</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings - International Conference on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">737–740</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper proposes a novel method for the projective reconstruction of planes and cameras from multiple images by factorizing a matrix containing all planar homographies between a reference view and all other views. If some planes are not visible in all views an alternative method is presented which solves the problem in two steps: a) all camera centers are recovered simultaneously b) all planes are reconstructed. The key idea of both methods is to specify one of the planes, which is visible in all views, as the plane at infinity. The methods were applied to synthetic and real data, where VRML models can be created with a small amount of user interaction.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">John L. Barron</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Range flow estimation.</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Image Understanding</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">85</style></volume><pages><style face="normal" font="default" size="100%">209--231</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We discuss the computation of the instantaneous 3D displacement vector fields of deformable surfaces from sequences of range data. We give a novel version of the basic motion constraint equation that can be evaluated directly on the sensor grid. The various forms of the aperture problem encountered are investigated and the derived constraint solutions are solved in a total least squares (TLS) framework. We propose a regularization scheme to compute dense full flow fields from the sparse TLS solutions. The performance of the algorithm is analyzed quantitatively for both synthetic and real data. Finally we apply the method to compute the 3D motion field of living plant leaves.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Brocke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical Image Sequence Processing for Temporal Change Detection</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 24th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><volume><style face="normal" font="default" size="100%">LNCS 2449</style></volume><pages><style face="normal" font="default" size="100%">215--223</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The aim is to detect sudden temporal changes in image sequences, focusing on bright objects that appear in a few consecutive frames. The proposed algorithm detects such outliers by computing a variance weighted deviation from mean values for every pixel. On this result, an object segmentation based on 2D-moments and its invariants is done frame by frame at a 3-sigma threshold. The algorithm was designed for a wide range of tasks in pre-processing as a tool for detection of fast temporal changes such as suddenly appearing or moving objects. Two different applications on noisy sequence data were realized. The entire system proved to fulfill the requirements of industrial environments for online process control and scientific demands for data rejection.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Brocke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistische Ereignisdetektion in Bildfolgen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/3065/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This thesis presents a technique to detect statistically unlikely changes in noisy image sequences. Methods for outlier detection are well known in statistical data analysis. This work applies these techniques to image processing. Appropriate statistical tests are performed to identify the relevant pixels by hypothesis testing. The image sequence is represented as a separate time series for each image pixel with the assumption that at steady state the scene is static. This assumption is commonly made for many applications in surveillance and spatio-temporal measurements. The significance level related to the hypothesis test remains the only free parameter. This allows an even comparison of the algorithm&#039;s performance across different data sets. A confidence measure is calculated for each binary decision (inlier vs. outlier). Effects such as occlusion or false positives that occur for multiple outliers are controlled by an iterative extension. The algorithm was put into practice twice 1) A complete computer vision system for an industrial laser welding process control was patented. It replaces human visual inspection for mass production and improves robustness over spatially integrating sensors. 2) The algorithm has been applied to infrared image sequences in order to distinguish events caused by two separate processes. Hence heat flux parameter estimation was improved by an outlier detector module at the beginning of the estimation scheme. The technique presented has proven to be an easy-to-configure, modular, and fast tool for event detection in image sequences.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Hara</style></author><author><style face="normal" font="default" size="100%">B. M. Uz</style></author><author><style face="normal" font="default" size="100%">H. Wei</style></author><author><style face="normal" font="default" size="100%">J. B. Edson</style></author><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author><author><style face="normal" font="default" size="100%">W. R. McGilles</style></author><author><style face="normal" font="default" size="100%">S. P. McKenna</style></author><author><style face="normal" font="default" size="100%">E. J. Bock</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">M. A. Donelan</style></author><author><style face="normal" font="default" size="100%">W. M. Drennan</style></author><author><style face="normal" font="default" size="100%">R. Wanninkhof</style></author><author><style face="normal" font="default" size="100%">E. S. Saltzman</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Surface wave observations during CoOP experiments and their relations to air-sea gas transfer</style></title><secondary-title><style face="normal" font="default" size="100%">Gas Transfer at Water Surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">American Geophysical Union</style></publisher><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">45--49</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Geophysical Monograph</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Thermographic measurements in environmental and bio sciences</style></title><secondary-title><style face="normal" font="default" size="100%">Quantitative Infrared Thermography</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://qirt.gel.ulaval.ca/archives/qirt2002/papers/033.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">253--259</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Maldague, X. P.</style></author><author><style face="normal" font="default" size="100%">Rozlosnik, A. E.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Thermographic measurements on plant leaves</style></title><secondary-title><style face="normal" font="default" size="100%">ThermoSense</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">SPIE</style></publisher><volume><style face="normal" font="default" size="100%">4710</style></volume><pages><style face="normal" font="default" size="100%">407--416</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">An important process of plant physiology is the transpiration of plant leaves. It is actively controlled by pores (stomata) in the leaf and the governing feature for vital factors such as gas exchange and water transport affixed to which is the nutrient transport from the root to the shoot. Because of its importance, the transpiration and water transport in leaves have been extensively studied. However, current measurement techniques provide poor spatial and temporal resolution. With the use of one single low-NETD infrared camera important parameter of plant physiology such as transpiration rates, heat capacity per unit area of the leaf and the water flow velocity can be measured to high temporal and special resolution by techniques presented in this paper. The latent heat flux of a plant, which is directly proportional to the transpiration rate, can be measured with passive thermography. Here use is made of the linear relationship between the temperature difference between a non transpiring reference body and the transpiring leaf and the latent heat flux. From active thermography the heat capacity per unit area of the leaf can be measured. This method is termed active, because the response of the leaf temperature to an imposed energy flux is measured. Through the use of digital image processing techniques simultaneous measurements of the velocity field and temporal change of heated water parcels traveling through the leaf can be estimated from thermal image sequences.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Keuchel, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">van Gool, L.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised Image Partitioning with Semidefinite Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, Proc. 24th DAGM Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">lncs</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Zürich, Switzerland</style></pub-location><volume><style face="normal" font="default" size="100%">2449</style></volume><pages><style face="normal" font="default" size="100%">141–149</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Florian Raisch</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Norbert Kirchgeßner</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Rainer H. A. Fink</style></author><author><style face="normal" font="default" size="100%">D. Uttenweiler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Velocity and feature estimation of actin filaments using active contours in noisy fluorescence image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 2nd IASTED Int. Conf. Visualization, Imaging and Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><pages><style face="normal" font="default" size="100%">645--650</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new approach for determining particle fea tures such as length, curvature and hence flexibility in addition to the velocity of moving single actin filaments in noisy fluorescence image sequences. The reliable deter mination of these features is essential for the analysis of the elementary force generation process of single motor molecules including heart and skeletal muscle myosins. First, the image sequence is preprocessed with the 3D structure tensor - where the third dimension is the time t in the image sequence - in order to eliminate noise and to obtain a measure for extracting coherently moving par ticles. Secondly, we determine the contour of the actin filaments with subpixel accuracy using active contours. Thereafter, we readjust a local coordinate system to elim inate inner movements of the active contour. In the fourth step, we estimate the initial position of the active contour in the next frame from the displacement vector field cal culated by the 3D structure tensor. The accuracy of the method is verified on synthetic test data, a prerequisite for the quantitative use of this method on experimentally obtained data. Finally this is demonstrated for fluorescence image sequences of actin filament movement in the in vitro motility assay.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author><author><style face="normal" font="default" size="100%">D. Fuß</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Thomas Luhmann</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Wellenbewegte Wasseroberfläche</style></title><secondary-title><style face="normal" font="default" size="100%">Nahbereichsphotogrammetrie in der Praxis, Beispiele und Problemlösungen</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/96618503X</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Wichmann</style></publisher><pages><style face="normal" font="default" size="100%">259--262</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wiehler, K.</style></author><author><style face="normal" font="default" size="100%">Heers, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.–S.</style></author><author><style face="normal" font="default" size="100%">Grigat, R.–R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A 1D analog VLSI implementation for non-linear real-time signal preprocessing</style></title><secondary-title><style face="normal" font="default" size="100%">Real–Time Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">127–142</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Klar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D Particle-Tracking Velocimetry applied to Turbulent Open-Channel Flow over a Gravel Layer</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Accurate optical flow in noisy image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">ICCV</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><pages><style face="normal" font="default" size="100%">587--592</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">John H. Steele</style></author><author><style face="normal" font="default" size="100%">Steve A. Thorpe</style></author><author><style face="normal" font="default" size="100%">Karl K. Turekian</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Air-sea interaction: gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Encyclopedia of Ocean Sciences</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">122 - 131</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysing Dynamic Processes in Range Data Sequences</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/1665</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this thesis a technique to estimate dynamic processes in range data sequences is developed. This includes the instantaneous velocity field (range flow) of a deformable surface and local expansion rates. For the velocity estimation novel differential constraint equations for the depth and intensity data are introduced. These constraint equations are then combined in a general total least squares parameter estimation framework. It turns out that this method can be used for a much broader class of problems where the parameters describing dynamic changes in multi-dimensional data are to be estimated. In addition to a confidence measure does the algorithm yield type measures indicating whether and to what degree there are linear dependencies in the data. Due to these dependencies the full parameter (range flow) set can usually not be computed at all observed data points. To overcome this a special regularisation scheme is developed that makes use of the determined local data structure. Surface expansion rates can then be computed locally from such regularised range flow fields. After an accuracy analysis of the presented algorithms they are applied to study living castor bean leaves. It is shown that this method can be used to investigate the movement and growth of such leaves with high spatial and temporal resolution.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Roth, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of convex optimization techniques to the relational matching of object views</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Dept. Math. and Comp. Science</style></publisher><pub-location><style face="normal" font="default" size="100%">University of Mannheim, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Comp. Science Series, Technical Report</style></work-type><notes><style face="normal" font="default" size="100%">in preparation</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Saracoglu, K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildanalyse von M-FISH</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/1805/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Multiplex-FISH is a combinatorial staining technique that allows the simultaneous detection and discrimination of all human chromosomes. Using at least five fluorochromes all chromosomes can be uniquely labeled in a combinatorial way and identified by their specific spectral signature. Within this thesis I developed a novel approach for the automated analysis of M-FISH images, yielding robust classification results and allowing the analysis of M-FISH images of different experiments. The method combines spectral information with spatial information to tesselate the image into regions of similar color. Subsequently a cluster analysis in color space and a final classification step are performed to identify the biological targets. This approach is applicable to images of different M-FISH experiments, allowing the analysis of interchromosomal as well as intrachromosomal abnormalities in the genome. It also allows the 3D analysis of M-FISH labeled chromosomes in interphase nuclei.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Fleet, D. J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computing optical flow with physical models of brightness variation</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans. Pattern Analysis Machine Intelligence</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">661--673</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Although most optical flow techniques presume brightness constancy, it is well-known that this constraint is often violated, producing poor estimates of image motion. This paper describes a generalized formulation of optical flow estimation based on models of brightness variations that are caused by time-dependent physical processes. These include changing surface orientation with respect to a directional illuminant, motion of the illuminant, and physical models of heat transport in infrared images. With these models, we simultaneously estimate the 2D image motion and the relevant physical parameters of the brightness change model. The estimation problem is formulated using total least squares (TLS), with confidence bounds on the parameters. Experiments in four domains, with both synthetic and natural inputs, show how this formulation produces superior estimates of the 2D image motion</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Keuchel, J.</style></author><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Radig, B.</style></author><author><style face="normal" font="default" size="100%">Florczyk, S.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Convex Relaxations for Binary Image Partitioning and Perceptual Grouping</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 2001</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lect. Notes Comp. Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 12–14</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Munich, Germany</style></pub-location><volume><style face="normal" font="default" size="100%">2191</style></volume><pages><style face="normal" font="default" size="100%">353–360</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author><author><style face="normal" font="default" size="100%">Weickert, Joachim</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE First Workshop on Variational and Level Set Methods in Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE Comp. Soc.</style></publisher><pub-location><style face="normal" font="default" size="100%">Vancouver, Canada</style></pub-location><pages><style face="normal" font="default" size="100%">237–244</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heiler, M.</style></author><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Efficient Feature Subset Selection for Support Vector Machines</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct.</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">21/2001</style></number><publisher><style face="normal" font="default" size="100%">Dept. Math. and Comp. Science</style></publisher><pub-location><style face="normal" font="default" size="100%">University of Mannheim, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Comp. Science Series, Technical Report</style></work-type><notes><style face="normal" font="default" size="100%">submitted</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Roth, S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Radig, B.</style></author><author><style face="normal" font="default" size="100%">Florczyk, S.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Evaluation of Convex Optimization Techniques for the Weighted Graph–Matching Problem in Computer Vision</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 2001</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lect. Notes Comp. Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 12–14</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Munich, Germany</style></pub-location><volume><style face="normal" font="default" size="100%">2191</style></volume><pages><style face="normal" font="default" size="100%">361–368</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weickert, J.</style></author><author><style face="normal" font="default" size="100%">Heers, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Zuiderveld, K.–J.</style></author><author><style face="normal" font="default" size="100%">Scherzer, O.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.–S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fast parallel algorithms for a broad class of nonlinear variational diffusion approaches</style></title><secondary-title><style face="normal" font="default" size="100%">Real–Time Imaging</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">31–45</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Achleitner, U.</style></author><author><style face="normal" font="default" size="100%">Krismer, A. C.</style></author><author><style face="normal" font="default" size="100%">Lindner, K. H.</style></author><author><style face="normal" font="default" size="100%">Wenzel, V.</style></author><author><style face="normal" font="default" size="100%">Strohmenger, H.-U.</style></author><author><style face="normal" font="default" size="100%">Thiel, W.</style></author><author><style face="normal" font="default" size="100%">van Gunsteren, W. F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fibrillation power: An alternative method of ECG spectral analysis for prediction of countershock success in a porcine model of ventricular fibrillation</style></title><secondary-title><style face="normal" font="default" size="100%">Resuscitation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">287-296</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">John L. Barron</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">R. Klette</style></author><author><style face="normal" font="default" size="100%">Huang, T.</style></author><author><style face="normal" font="default" size="100%">Gimel&#039;farb, G.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The fusion of image and range flow</style></title><secondary-title><style face="normal" font="default" size="100%">Multi-Image Search and Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">174--192</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Sciences</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A general framework for image sequence processing</style></title><secondary-title><style face="normal" font="default" size="100%">Fachtagung Informationstechnik</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><pages><style face="normal" font="default" size="100%">125--132</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heers, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Globally–Convergent Iterative Numerical Schemes for Non–Linear Variational Image Smoothing and Segmentation on a Multi–Processor Machine</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans. Image Proc.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">6</style></number><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">852–864</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark Wenig</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GOME-Spurenstoffauswertung und Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Restle</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Horizontal scannendes Wei\DFlicht-Interferometer</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Robert Bosch GmbH</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schellewald, C.</style></author><author><style face="normal" font="default" size="100%">Keuchel, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Figueiredo, M.</style></author><author><style face="normal" font="default" size="100%">Zerubia, J.</style></author><author><style face="normal" font="default" size="100%">Jain, A.K.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Image labeling and grouping by minimizing linear functionals over cones</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR&#039;01)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lect. Notes Comp. Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 3–5</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">INRIA, Sophia Antipolis, France</style></pub-location><volume><style face="normal" font="default" size="100%">2134</style></volume><pages><style face="normal" font="default" size="100%">267–282</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">Carsten Leue</style></author><author><style face="normal" font="default" size="100%">S. Kraus</style></author><author><style face="normal" font="default" size="100%">T. Wagner</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">B. Radig</style></author><author><style face="normal" font="default" size="100%">S. Florczyk</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Image sequence analysis of satellite NO$_2$ concnetration maps</style></title><secondary-title><style face="normal" font="default" size="100%">Pattern Recognition, 23rd DAGM Symposium Munich</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2191</style></volume><pages><style face="normal" font="default" size="100%">223--230</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Here we describe a new method for the quantification of a global NOx budget from image sequences of the GOME instrument on the ERS-2. The focus of this paper is on image processing techniques to separate tropospheric and stratospheric NO2-colums using normalized convolution with infinite impulse response filters (IIR) to interpolate gaps in the data and average the cloud coverage of the earth, the estimation the NO2 life time and the determination of regional NOx source strengths.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Carlsson, Stefan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linear multi view reconstruction and camera recovery</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE International Conference on Computer Vision</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Critical configurations</style></keyword><keyword><style  face="normal" font="default" size="100%">Duality</style></keyword><keyword><style  face="normal" font="default" size="100%">Missing data</style></keyword><keyword><style  face="normal" font="default" size="100%">Multiple views</style></keyword><keyword><style  face="normal" font="default" size="100%">Planar parallax</style></keyword><keyword><style  face="normal" font="default" size="100%">Projective reconstruction</style></keyword><keyword><style  face="normal" font="default" size="100%">Reference plane</style></keyword><keyword><style  face="normal" font="default" size="100%">Structure from motion</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">42–49</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a linear algorithm for the simultaneous computation of 3D points and camera positions from multiple perspective views, based on having four points on a reference plane visible in all views. The reconstruction and camera recovery is achieved, in a single step, by finding the null-space of a matrix using singular value decomposition. Unlike factorization algorithms, the presented algorithm does not require all points to be visible in all views. By simultaneously reconstructing points and views the numerically stabilizing effect of having wide spread cameras with large mutual baselines is exploited. Experimental results are presented for both finite and infinite reference planes. An especially interesting application of this method is the reconstruction of architectural scenes with the reference plane taken as the plane at infinity which is visible via three orthogonal vanishing points. This is demonstrated by reconstructing the outside and inside (courtyard) of a building on the basis of 35 views in one single SVD.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measuring Heat Exchange Processes at the Air--Water Interface from Thermographic Image Sequence Analysis</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/1875</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this thesis a novel technique for estimating heat transfer at the free air water interface is presented. For the first time spatially resolved heat flux and transfer velocity measurements are available with a high temporal resolution. The statistical properties of the transfer processes are deduced and the parameters characterizing them established. Based on this analysis a second way to estimate the heat flux is presented. These techniques are based on thermal image sequences on which a motion analysis is performed. The motion is modelled in a general parameterization and physically motivated intensity changes can be incorporated by means of linear partial differential equations. In the presented framework the parameters of physical processes described by such differential equations can be estimated in multidimensional data. These general equations of motion are solved simultaneously by a method of least squares. To do so algorithms are developed that allow for unbiased estimates taking the structure of the noise into account. Methods from robust statistics are employed to correctly solve the estimation problem regardless if the data is corrupted by outliers. The relevance of the developed techniques to other scientific applications is shown. In an accuracy analysis confidence bounds of the proposed algorithms are established and limitations revealed. Following an examination under controlled laboratory conditions in the Heidelberg Aeolotron, the techniques are successfully applied at an international field campaign.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Wagner</style></author><author><style face="normal" font="default" size="100%">Carsten Leue</style></author><author><style face="normal" font="default" size="100%">Klaus Pfeilsticker</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Monitoring of the stratospheric chlorine activation by Global Ozone Monitoring Experiment (GOME) OClO measurements in the austral and boreal winters 1995 through 1999</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">D6</style></number><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">4971-4986</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Measurements of OClO total column amounts by means of the Global Ozone Monitoring Experiment (GOME) instrument conducted in the austral and boreal winter stratospheres from 1995 through 1999 are presented, GOME is a four-channel UV/visible spectrometer (240-790 nm) deployed on the polar orbiting European ERS-2 satellite since April 1995. Previous studies have shown that the observations of OClO, the symmetric chlorine dioxide formed in a side channel of the reaction of BrO + ClO, can serve as an indicator for a stratospheric chlorine activation. GOME&#039;s 3-day coverage of the global atmosphere allows us to infer the first global data set of OClO, and to study continuous time series of its occurrence in both winter stratospheres. It is found that, while OClO regularly occurs over Antarctica in similar amounts and seasonal timing during the different winters, its occurrence is much more variable in the Arctic winter stratosphere, primarily because of the larger dynamic activity that result in warmer temperatures there. About 40% higher OClO column amounts are found in the Antarctic polar stratosphere than in its northern counterpart, a further indication for a significantly more efficient chlorine activation in the Antarctic than the Arctic late winter and spring stratosphere.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andrew, Alex M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multiple View Geometry in Computer Vision</style></title><secondary-title><style face="normal" font="default" size="100%">Kybernetes</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer</style></keyword><keyword><style  face="normal" font="default" size="100%">Cybernetics</style></keyword><keyword><style  face="normal" font="default" size="100%">Machine vision</style></keyword><keyword><style  face="normal" font="default" size="100%">Publication</style></keyword><keyword><style  face="normal" font="default" size="100%">Robotics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">9-10</style></number><volume><style face="normal" font="default" size="100%">30</style></volume><pages><style face="normal" font="default" size="100%">1333–1341</style></pages><isbn><style face="normal" font="default" size="100%">0521540518</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">2nd ed. A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering th. Introduction-a tour of multiple view geometry – Projective geometry and transformations of 2D – Projective geometry and transformations of 3D – Estimation-2D projective transformations – Algorithm evaluation and error analysis – Camera models – Computation of the camera matrix P – More single view geometry – Epipolar geometry and the fundamental matrix – 3D reconstruction of cameras and structure – Computation of the fundamental matrix F – Structure computation – Scene planes and homographies – Affine epipolar geometry – The trifocal tensor – Computation of the trifocal tensor T – N-Linearities and multiple view tensors – N-View computational methods – Auto-calibration – Duality – Cheirality – Degenerate configurations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">B. Radig</style></author><author><style face="normal" font="default" size="100%">S. Florczyk</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonlinear Shape Statistics via Kernel Spaces</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 2001</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2191</style></volume><pages><style face="normal" font="default" size="100%">269--276</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lect.~Notes Comp.~Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Daniel Cremers</style></author><author><style face="normal" font="default" size="100%">Kohlberger, Timo</style></author><author><style face="normal" font="default" size="100%">Christoph Schnörr</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Radig, B.</style></author><author><style face="normal" font="default" size="100%">Florczyk, S.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonlinear Shape Statistics via Kernel Spaces</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 2001</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lect. Notes Comp. Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 12–14</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Munich, Germany</style></pub-location><volume><style face="normal" font="default" size="100%">2191</style></volume><pages><style face="normal" font="default" size="100%">269–276</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schulzke, Erich</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Numerische Simulation von elementaren Kalziumfreisetzungsereignissen und photolytische Ca^2+-Freisetzung aus Käfigmolekülen mittels Picosekundenlaser</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Küsters, R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Objektverfolgung und Bildfusion zur Untersuchung wachsender Pflanzenwurzeln</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">N. E. Huang</style></author><author><style face="normal" font="default" size="100%">Y. Toba</style></author><author><style face="normal" font="default" size="100%">Z. Shen</style></author><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">M. L. Banner</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">I. S. F. Jones</style></author><author><style face="normal" font="default" size="100%">Y Toba</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Ocean wave spectra and integral properties</style></title><secondary-title><style face="normal" font="default" size="100%">Wind Stress over the Ocean</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Cambridge University Press</style></publisher><pages><style face="normal" font="default" size="100%">82--123</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Jost, D.</style></author><author><style face="normal" font="default" size="100%">Rüttimann, M.</style></author><author><style face="normal" font="default" size="100%">Calamai, F.</style></author><author><style face="normal" font="default" size="100%">Kowalski, J. J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Preliminary results on the prediction of countershock success with fibrillation power</style></title><secondary-title><style face="normal" font="default" size="100%">Resuscitation</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">297-299</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Leue</style></author><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">T. Wagner</style></author><author><style face="normal" font="default" size="100%">Klimm, O.</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative analysis of NO$_x$ emissions from Global Ozone Monitoring Experiment satellite image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">D6</style></number><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">5493--5505</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Nitric oxides (NOx) play a very important role among the anthropogenic trace gases. They affect human health and have an impact on ozone chemistry and climatic change. Here we describe a new method for the quantification of the global NOx budget from image sequences of the Global Ozone Monitoring Experiment (GOME) spectrometer on board the ERS 2 satellite. In contrast to measurements using ground-based or balloon- or aircraft-borne sensors, this instrument provides, for the first time, the possibility of observing global maps of NO2 column densities. As part of this work, algorithms were developed to analyze GOME spectra numerically and to extract physically relevant parameters from the resulting maps using image-processing techniques. Column densities of NO x were determined using differential optical absorption spectroscopy (DOAS) [Platt, 1994]. By the combined use of an efficient B-spline interpolation and an inversion algorithm based on Householder transformations, the numerical algorithms accelerate the retrievals by a factor of 26 with respect to previous methods. Moreover, techniques are presented for separating tropospheric and stratospheric NO2 colums and estimating the lifetime of NO2 in the troposphere. This allows determination of regional NOx source strengths. Independent of traditional methods, a global source strength of (43 ± 20) Tg N yr-1 is estimated. The accuracy of this method is comparable to that of established statistical approaches.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">B. Radig</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Reliable estimates of the sea surface heat flux from image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 23th DAGM Symposium on Pattern Recognition, München</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">2191</style></number><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">194--201</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We present a new technique for estimating the sea surface heat flux from infrared image sequences. Based on solving an extension to the standard brightness change constraint equation in a total least squares (TLS) sense, the total derivative of the sea surface temperature with respect to time is obtained. Due to inevitable reflexes in field data the TLS framework was further extended to a robust estimation based on a Least Median of Squares Orthogonal Distances (LMSOD) scheme. From this it is possible for the first time to compute accurate heat flux densities to a high temporal and spatial resolution. Results obtained at the Heidelberg Aeolotron showed excellent agreement to ground truth and field data was obtained on the GasExII experiment.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture notes on computer science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Norbert Kirchgeßner</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Root Growth Analysis in Physiological Coordinates</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Image Analysis and Processing (ICIAP&#039;01)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Norbert Kirchgeßner</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Root Growth Measurements in Object Coordinates</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 23th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Simulation der Austauschprozesse an der Ozeanoberfläche im Labor: das neue Heidelberger Aeolotron</style></title><secondary-title><style face="normal" font="default" size="100%">Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Hamburg, 26.-30.03.2001</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Deutsche Physikalische Gesellschaft</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Die Mechanismen der kleinskaligen Austauschprozesse an der Meeresoberfläche sind trotz ihrer Bedeutung für die globalen Energie- und Stoffkreisläufe bis heute weitgehend unverstanden. Erste Ergebnisse von Messungen am neuen Heidelberger Aeolotron, einem großen ringförmiger Wind/Wellen-Kanal, werden vorgestellt. Diese Versuchseinrichtung mit 10 m Durchmesser bietet neue experimentelle Möglichkeiten zur Untersuchung des Wärme- und Gasaustausches, der winderzeugten Wellen, der Mikroturbulenz an der Ozeanoberfläche und Oberflächenfilmen. Es können Windgeschwindigkeiten bis zu 12 m/s, Luft- und Wassertemperaturen zwischen 5 und 35 Grad Celsius und Wärmeflüsse bis 1 kW/m^2 simuliert werden. Vorgestellt werden neue Methoden, mit denen sich aus Wärmebildsequenzen direkt der Nettowärmefluss an der Wasseroberfläche, die Wärmeaustauschrate und die Gasaustauschrate bestimmen lassen. Die ersten Messungen zeigen auch die Bedeutung von Oberflächenerneuerungsprozessen in der wasserseitigen Grenzschicht für den Stoffaustausch.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Wagner</style></author><author><style face="normal" font="default" size="100%">Carsten Leue</style></author><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">Klaus Pfeilsticker</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial and temporal distribution of enhanced boundary layer BrO concentrations measured by the GOME instrument aboard ERS-2</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">D6</style></number><volume><style face="normal" font="default" size="100%">106</style></volume><pages><style face="normal" font="default" size="100%">24225--24235</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The temporal and spatial distribution of enhanced boundary layer BrO concentrations in both hemispheres during 1997 is presented using observations of the Global Ozone Monitoring Experiment (GOME) on board the European research satellite ERS-2. BrO concentrations (up to 50 ppt) are the major cause for catalytic boundary layer ozone destruction typically observed during polar spring in both hemispheres. While autocatalytic mechanisms are most probably responsible for the release of the observed high concentrations of reactive bromine compounds, uncertainties still remain with respect to the primary release mechanisms and whether the autocatalytic reactions are taking place on sea-salt aerosol or the surface of sea ice. We find that enhanced boundary layer BrO concentrations correlate very well with ozone depletion events. Enhanced BrO concentrations are always found over or near to areas of frozen salt water (above sea ice or also above the frozen surface of the Caspian Sea) consistent with the assumption that such conditions are a prerequisite for the autocatalytic release of high BrO concentrations to the troposphere.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">C. Schnörr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistische Mustererkennung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Unterlagen zur Vorlesung</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Peter, C.</style></author><author><style face="normal" font="default" size="100%">Daura, X.</style></author><author><style face="normal" font="default" size="100%">Thiel, W.</style></author><author><style face="normal" font="default" size="100%">van Gunsteren, W. F.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A strategy for analysis of (molecular) equilibrium simulations: configuration space density estimation, clustering and visualization</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Physics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">114</style></volume><pages><style face="normal" font="default" size="100%">2079-2089</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Stybalkowski</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Strömungsmessung in Sedimentporen mittel 3D Particle Tracking Velocimetry</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">John L. Barron</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Surface expansion from range data sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 23th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">163--169</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We compute the range flow field, i.e. the 3D velocity field, of a moving deformable surface from a sequence of range data. This is done in a differential framework for which we derive a new constraint equation that can be evaluated directly on the sensor data grid. It is shown how 3D structure and intensity information can be used together in the estimation process. We then introduce a method to compute surface expansion rates from the now available velocity field. The accuracy of the proposed scheme is assessed on a synthetic data set. Finally we apply the algorithm to study 3D leaf motion and growth on a real range sequence.</style></abstract><custom3><style face="normal" font="default" size="100%">LNCS 2191</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weickert, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Theoretical Framework for Convex Regularizers in PDE–Based Computation of Image Motion</style></title><secondary-title><style face="normal" font="default" size="100%">Int. J. Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">245–264</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Thermographie in den Umwelt- und Biowissenschaften</style></title><secondary-title><style face="normal" font="default" size="100%">DGZfP-Berichtsband 77</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><publisher><style face="normal" font="default" size="100%">Deutsche Gesellschaft für zerstörungsfreie Prüfung e.V.</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weickert, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Optic Flow Computation with a Spatio-Temporal Smoothness Constraint</style></title><secondary-title><style face="normal" font="default" size="100%">J. Math. Imaging and Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">245–255</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Kudryavtsev, V. N.</style></author><author><style face="normal" font="default" size="100%">Makin, V. K.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Wavenumber Spectra of Short Wind Waves: Laboratory Measurements and Interpretation</style></title><secondary-title><style face="normal" font="default" size="100%">IGARSS &#039;01, Geoscience and Remote Sensing Symposium, Sydney, NSW, Australia</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2001</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">965-967</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Short wind-generated capillary-gravity waves were measured with a refraction-based optical technique in different wind/wave tanks. Directional wave number spectra were determined from the wave slope images for a wide range of wind speeds and fetches for the different geometries of the laboratory facilities. The shape of the wavenumber spectra and their dependence on die friction velocity and fetch were analyzed. We found that the parasitic capillaries dominate the capillary range of the spectra. The interpretation of the results is given by a physical model of the short wind-wave spectrum that takes the generation of parasitic capillaries under laboratory conditions into account</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Engelmann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D-Flow Measurement by Stereo Imaging</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/1070</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new method to record three-dimensional liquid flow fields by using `Particle Tracking Velocimetry&#039; is presented. It is based on a two-dimensional Particle Tracking Velocimetry method. It was extended to the third space dimension in order to include the complete physical space. This procedure allows to determine the Lagrange-flow field and to calculate from it the Euler-velocity flow field obtained from many other flow measuring techniques. A calibration method was developed for the wind-wave-flume which allows a high resolution in space. The stereo camera setup and the experimental setup were optimized for the liquid flow measurements. For the first time a liquid prism and a Scheimpflug-camera geometry was used. Numerical calculations using the finite element method demonstrate the complexity of the problem of dealing with free surfaces with wind-induced shear forces as a boundary condition. They show clearly that experimental studies are indispensable for describing phenomena such as `bursts&#039; (descending of liquid elements from close to the surface into deeper layers). Flow measurements were performed in a newly constructed wind-wave-flume (AEOLOTRON) and in a smaller predecessor by using the newly developed imaging methods. In this way the flow fields of wind driven water waves could be characterized by the velocity field and the `turbulence&#039; conditions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Norbert Kirchgeßner</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">3D-Modellierung von Pflan­zen­blät­tern mittels eines Depth-from-Motion Verfahrens</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 22th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><pages><style face="normal" font="default" size="100%">381--388</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Hermann Gröning</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analyzing particle movements at soil interfaces</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications. A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">648--649</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">A20</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analyzing size spectra of oceanic air bubbles</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications. A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">634--635</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">A13</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An anisotropic diffusion algorithm with optimized rotation invariance</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 22th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><pages><style face="normal" font="default" size="100%">460--467</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Prokop, M.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bestimmung Physiologischer Parameter von Pflanzen mittels Digitaler Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">Carsten Leue</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cloud classification analyzing image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications. A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">652--653</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">A22</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Marxen</style></author><author><style face="normal" font="default" size="100%">Sullivan, P. E.</style></author><author><style face="normal" font="default" size="100%">Loewen, M. R.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison of Gaussian particle center estimators and the achievable measurement density for particle tracking velocimetry</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">145-153</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A series of numerical simulations were conducted to investigate the performance of two particle center estimation algorithms for Particle Tracking Velocimetry: a simple three-point Gaussian estimator and a least-square Gaussian. The smallest position error for images with reasonable noise levels was found to be approximately 0.03 pixels for both estimators using particles with diameters of 4 pixels. As both estimators performed equally well, use of the simple three-point Gaussian algorithm is recommended because it executes 100 times faster than the least-square algorithm. The maximum achievable measurement density and accuracy for the three-point Gaussian estimator were determined with a numerical simulation of an Oseen vortex. Uncertainty measures have been introduced to filter out unreliable displacement measurements. It was found that 4 to 5 velocity vectors could be obtained within a 32×32 pixel area with an average displacement error of 0.1 pixels. This doubles the spatial resolution of conventional cross-correlation based Particle Image Velocimetry at comparable accuracy.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wulf, M.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.S.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Bothe, H.</style></author><author><style face="normal" font="default" size="100%">Rojas, R.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">On the computational rôle of the primate retina</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 2nd ICSC Symposium on Neural Computation (NC 2000)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May, 23–26</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Berlin, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer Vision and Applications: A Guide for Students and Practitioners</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">679</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">John L. 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Barron</style></author><author><style face="normal" font="default" size="100%">Liptay, A.</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical and range flow to measure 3D plant growth and motion</style></title><secondary-title><style face="normal" font="default" size="100%">Image Vision Computing New Zealand</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><pages><style face="normal" font="default" size="100%">68--77</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dominik Schmund</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hau\DFecker, H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical leaf growth analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications - A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">640-641</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">A16</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimal separable interpolation of color images with bayer array format</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/12680/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">DFG research unit Image Sequence Analysis to Investigate Dynamic Processes, Interdisciplinary Center for Scientific Computing, University of Heidelberg, Germany</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimale Operatoren in der Digitalen Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/962</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A novel method for optimal choice of filter operators is presented. Single filters as well as filter families with linear or nonlinear optimal criteria can be addressed by different weighted norms in wave number domain. Coefficients of filters with arbitrary support can be optimized in floating point or fixed point accuracy. Numerous examples are presented to illustrate optimization of filters e.g by isotropy, rotation invariance or accuracy of absolute value, and each is followed by discussions of results. Errors are decreased by up to 3 orders of magnitude compared to standard parameter choices. In an investigation of displacements calculated by the well known structure tensor approach with optimal filters the results are improved in two respects. Firstly, estimation errors are decreased by approximately two orders of magnitude and secondly, they are more robust with respect to noise. The greatly improved performance is demonstrated by a tracking application. A novel explicit discretization for anisotropic diffusion filtering using optimal filters is introduced. Numerical errors of this scheme obtained by comparison with a novel analytical solution are about 1.5 to 2.5 orders of magnitude smaller than the errors introduced by the best comparable standard method. The new method clearly outperforms the latter in a reconstruction test. Due to the higher stability with respect to larger time steps, the new method is 3 to 4 times faster than other explicit schemes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Engelmann</style></author><author><style face="normal" font="default" size="100%">M. Stöhr</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hau\DFecker, H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle-tracking velocimetry</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications - A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">646-647</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">A19</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Weickert, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PDE–Based Preprocessing of Medical Images</style></title><secondary-title><style face="normal" font="default" size="100%">Künstliche Intelligenz</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">July</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">5–10</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">R. Klette</style></author><author><style face="normal" font="default" size="100%">H. S. Stiehl</style></author><author><style face="normal" font="default" size="100%">K. Vincken</style></author><author><style face="normal" font="default" size="100%">M. Viergever</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Performance characteristics of low-level motion estimation in spatiotemporal images</style></title><secondary-title><style face="normal" font="default" size="100%">Performance Characterization in Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Kluwer, Dordrecht</style></publisher><pages><style face="normal" font="default" size="100%">pp. 139-152</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">invited, Workshop Schloss Dagstuhl, March 16-20, 1998</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">John L. Barron</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative regularized range flow</style></title><secondary-title><style face="normal" font="default" size="100%">Vision Interface</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><pages><style face="normal" font="default" size="100%">203--210</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hau\DFecker, H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Radiation and illumination</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications - A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">11--52</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">2</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hau\DFecker, H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Radiometry of imaging</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications - A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">85--109</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">4</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">John L. Barron</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Vernon, D.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Regularised range flow</style></title><secondary-title><style face="normal" font="default" size="100%">European Conference on Computer Vision (ECCV)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">785--799</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Extending a differential total least squares method for range flow estimation we present an iterative regularisation approach to compute dense range flow fields. We demonstrate how this algorithm can be used to detect motion discontinuities. This can can be used to segment the data into independently moving regions. The different types of aperture problem encountered are discussed. Our regularisation scheme then takes the various types of flow vectors and combines them into a smooth flow field within the previously segmented regions. A quantitative performance analysis is presented on both synthetic and real data. The proposed algorithm is also applied to range data from castor oil plants obtained with the Biris laser range sensor to study the 3-D motion of plant leaves.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science 1843</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Representation of multidimensional signals</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications. A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">211--272</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">8</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Small-scale air-sea interaction with thermography</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications. A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">638--639</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">A15</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Kümmerlen</style></author><author><style face="normal" font="default" size="100%">Stefan Dauwe</style></author><author><style face="normal" font="default" size="100%">Dominik Schmund</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Thermography to measure water relations of plant leaves</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications. A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">636--637</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">A14</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Tobias Dierig</style></author><author><style face="normal" font="default" size="100%">Hanspeter A. Mallot</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Hau\DFecker, H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Three-dimensional imaging algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications - A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pages><style face="normal" font="default" size="100%">397--438</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">11</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Untersuchung des Gasaustausches und der Mikroturbulenz an der Meeresoberfläche mittels Thermographie</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/545</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Untersuchungen statistischer und geometrischer Eigenschaften von Windwellen und ihrer Wechselwirkung mit der wasserseitigen Grenzschicht</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/2473/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Using color image processing a refraction based technique for retrieving the slope of a water surface covered with waves has been substantially improved. This multichannel technique is similar to photometric stereo analysis for opaque surfaces. The three independend channels of a color camera allow simultaneous detection of both surface gradient components. The third information is used for normalization to the total illumination intensity for every individual pixel, leading to an increased linearity and enhanced robustness. Errors due to inhomogeneous illumination or disturbance by small particles in the water can be corrected almost completely. For combined measurements the technique can be reduced to two colors. Measuring only one slope component normalization is still possible. At two wind/wave facilities statistical and geometrical information for small scale waves were obtained and compared to results from authors using other techniques. Two combined experiments were conducted. At different scales the wave field and the underlying flow field were observed simultaneously. Hering [1996] computed kinetic energy density and vorticity of the flow field in a 10cm x 10cm frame. In the second combined experiment Münsterer [1996] measured concentration profiles of a tracer gas in a 4cm x 4cm section in the viscous boundery layer. Both experiments provided new insights in the interaction between capillary waves, turbulence in the flow field and in the boundary layer respectively.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Jähne, B.</style></author><author><style face="normal" font="default" size="100%">Haußecker, H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Adaptive Smoothing and Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Vision and Applications: A Guide for Students and Practitioners</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><pub-location><style face="normal" font="default" size="100%">San Diego</style></pub-location><pages><style face="normal" font="default" size="100%">459–482</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Weickert, J.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Sommer, G.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational Image Motion Computation: Theoretical Framework, Problems and Perspectives</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 2000</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Informatik aktuell</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept., 13–15</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Kiel, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">(invited paper)</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tvarusk\´o, Wolfgang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zeitaufgelöste Analyse und Visualisierung in lebenden Zellen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reinmuth, Jutta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zwei-Farbstoff-Technik zur Tiefenrekonstruktion von Gaskonzentrationen in der wasserseitigen Grenzschicht</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In the present work a laser-induced fluoerscence (LIF) technique for visualizing 3 dimensional gas profiles in water was further developed. This technique allows studies of small-scale processes in the aqueous boundary layer and, since the fluorescence intensity depends linearly on the pH value, it takes advantage of the acid/base reaction of the used fluorescence indicator. Thus gases, which change the pH value, can be traced by using such an indicator. A second, absorbing but non-fluorescent dye causes non-linear changes in the measured spectra, which depend on the travelled distance in the solution. The key idea for this approach was taken from differential optical absorptions spectroscopy (DOAS), in which atmospheric trace gas concentrations are determined by measured absoption spectra. The major goal was finding and analysing suitable dyes. In order to be able to use the LIF technique for gas profile measurements, the dyes needed to meet certain criteria. Various dyes were thoroughly examined and in the course of this work three suitable absorber could be determined. An illumination method for 3 dimensional depth reconstruction was designed, in which the laserbeam was expanded by an optical system and the resulting homogeneous patch was projeced onto the water surface. This illumination method allowed scale analysis of patterns caused by CO2 concentration fluctuations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Stöhr</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">D. Engelmann</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Gomes, S.</style></author><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">F. Keil</style></author><author><style face="normal" font="default" size="100%">W. Mackens</style></author><author><style face="normal" font="default" size="100%">H. Voß</style></author><author><style face="normal" font="default" size="100%">Wagner, H.-G..</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">J. Werther</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">4D particle tracking velocimetry applied to gas-liquid reactors</style></title><secondary-title><style face="normal" font="default" size="100%">In Scientific Computing in Chemical Engineering II - Simulation, Image Processing, Optimization and Control.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pages><style face="normal" font="default" size="100%">270--279</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Air--sea gas transfer and micro turbulence at the ocean surface using infrared image processing</style></title><secondary-title><style face="normal" font="default" size="100%">Geoscience and Remote Sensing Symposium, 1999. IGARSS apos;99 Proceedings. IEEE 1999 International</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">11--13</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">To obtain an insight into the transfer process at the air-water interface new techniques for the quantitative investigation of the gas exchange have been developed. The controlled flux technique, CFT (Jahne et al. 1989) uses heat as a proxy tracer for gases to measure the air-sea gas transfer velocity with a high spatial and temporal resolution. The results of a field cruise and a laboratory study are discussed and compared with a model (Schimpf et al.) predicting the sea surface temperature distribution based on surface renewal (Danckwerts 1970). The sea surface temperature fluctuations associated with the interplay of diffusive and turbulent transport give direct insight into the mechanisms of gas transfer. Using infrared image processing the spatial structure of the micro turbulence at the ocean surface is analyzed.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Oliver Beringer</style></author><author><style face="normal" font="default" size="100%">Hermann Gröning</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analyzing particle movements at soil interfaces</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Computer Vision and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><volume><style face="normal" font="default" size="100%">3: Systems and Applications</style></volume><pages><style face="normal" font="default" size="100%">699-718</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">32</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carlo Götz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildverarbeitungsalgorithmen in der Fluoreszenzmikroskopie</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">G. Dotzler</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Blätter, Wind und Wellen. Unsichtbares wird sichtbar.</style></title><secondary-title><style face="normal" font="default" size="100%">computer art faszination, dot&#039;99</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">dot-Verlag</style></publisher><pages><style face="normal" font="default" size="100%">38--43</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Continuous and digital signals</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Computer Vision and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">9--34</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">2</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Thomas Scholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Depth-from-focus for the measurement of size distributions of small particles</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Computer Vision and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><volume><style face="normal" font="default" size="100%">3: Systems and Applications</style></volume><pages><style face="normal" font="default" size="100%">623-646</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">29</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dominik Schmund</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development of an Optical Flow Based System for the Precise Measurement of Plant Growth</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">John L. Barron</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Differential range flow estimation</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 21th DAGM Symposium on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><pages><style face="normal" font="default" size="100%">309--316</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">DAGM award</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Noffz, K.-H.</style></author><author><style face="normal" font="default" size="100%">Lay, R.</style></author><author><style face="normal" font="default" size="100%">Männer, R.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Field Programmable Gate Array image processing</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Computer Vision and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><volume><style face="normal" font="default" size="100%">3: Systems and Applications</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">2</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H.-J. Köhler</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Oliver Beringer</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fluidisation and deformation of submerged soil due to fluctuating water level</style></title><secondary-title><style face="normal" font="default" size="100%">XII Europ. Conference on Soil Mechanics and Geotechnical Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><pages><style face="normal" font="default" size="100%">1109--1115</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sven Eichkorn</style></author><author><style face="normal" font="default" size="100%">Thomas Münsterer</style></author><author><style face="normal" font="default" size="100%">Ulrike Lode</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fluorescenceimaging of air-water gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Computer Vision and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><volume><style face="normal" font="default" size="100%">3: Systems and Applications</style></volume><pages><style face="normal" font="default" size="100%">647-662</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">30</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Hamid R. Tizhoosh</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fuzzy image processing</style></title><secondary-title><style face="normal" font="default" size="100%">Handbook of Computer Vision and Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><volume><style face="normal" font="default" size="100%">2: Signal Processing and Pattern Recognition</style></volume><pages><style face="normal" font="default" size="100%">683--727</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><section><style face="normal" font="default" size="100%">22</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gut beleuchtet ist halb gemessen</style></title><secondary-title><style face="normal" font="default" size="100%">QZ</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.qz-online.de/qz-zeitschrift/archiv/artikel/art-der-lichtquelle-und-der-einstrahlbedingungen-sind-entscheidend-gut-beleuchtet-ist-halb-gemessen-345974.html</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">10</style></number><volume><style face="normal" font="default" size="100%">44</style></volume><pages><style face="normal" font="default" size="100%">1283--1288</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Handbook of Computer Vision and Applications</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><publisher><style face="normal" font="default" size="100%">Academic Press</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">M. L. Banner</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The Heidelberg Aelotron --- a new facility for laboratory investigations of small-scale air-sea interaction</style></title><secondary-title><style face="normal" font="default" size="100%">The Wind-Driven Air-Sea Interface: Electromagnetic and Acoustic Sensing, Wave Dynamics and Turbulent Fluxes</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">Poster</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Heidelberg Aeolotron - a new facility for laboratory investigations of small scale air-sea interaction</style></title><secondary-title><style face="normal" font="default" size="100%">Poster presented at: The Wind-Driven Air-Sea Interface: Electromagnetic and Acoustic Sensing, Wave Dynamics and Turbulent Fluxes</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1999</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The study of small-scale air sea interaction still lacks suitable facilities. No facilities are available with large fetches and correspondingly high wave ages. Furthermore almost no large facility is suitable for measurements with sea water, is clean enough for controllable experiments with surface films or sufficiently gas tight for gas exchange measurements. Currently a new large annular wind/wave facility with quasi unlimited fetch is under construction at the Institute for Environmental Physics of Heidelberg University. An annular facility is not a new idea. A 40 m diameter facility (``storm basin&#039;&#039;) was already built shortly after World War II in Sevastopol for wind/wave interaction studies. Various small circular facilities for air-sea gas exchange studies have been used since the late seventies at Heidelberg University and later at Woods Hole Oceanographic Institution for air-sea gas exchange studies. The new facility builds on the experience with these facilities but now reaches a critical size. It has an outer diameter of 10 m, the channel is 0.65 m wide and 2.4 m high and can be filled with water up to a height of 1.2 m. It is thus a rather deep facility. Only the wind/wave flume of the Hydraulic facility at Scripps Institution of Oceanography is deeper with a depth of 1.5 m. Since the maximum phase speed of the waves is 3.4 m/s, high wave age conditions can be reached at least for low and medium wind speeds. The facility is designed for a maximum wind speed of 15 m/s. Water currents can be generated separately by a set of thrusters. The air space is gas tight and the materials that come into contact with the water and air make experiments possible with de-ionized water, artificial seawater, surfactants, and reactive gases. Water temperatures and heat fluxes across the interface range from 5o to 35oC and from -0.5 to 1 kW/m2, respectively. We plan to focus in the next decade on measurements revealing the mechanism of air-sea gas transfer including the effects of surfactants and waves. The new facility will be open for guest scientists. 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Laboratory and field measurements show that wind waves significantly increase the gas transfer rate and that it is significantly influenced in this way by surfactants. Because of limited experimental techniques, the mechanisms for this enhancement and the structure of the turbulence in the boundary layer at a wavy water surface are still not known. A number of new imaging techniques are described that give direct insight into the processes and promise to trigger substantial theoretical progress in the near future.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Scholze, Marko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analyse der visuellen Stratigraphie alpiner Eisbohrkerne mittels digitaler Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Fakultät für Physik und Astronomie Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jochen Dieter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of Small-Scale Ocean Wind Waves by Image Sequence Analysis of Specular Reflections</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/955455804</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hagen Spies</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bewegungsdetektion und Geschwindigkeitsanalyse in Bildfolgen zur Untersuchung von Sedimentverlagerungen und Porenströmungen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Brocke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildverarbeitung für Mikrolaserschweißen unter Verwendung der Houghtransformation</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heers, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A class of parallel algorithms for nonlinear variational image segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Noblesse Workshop on Non–Linear Model Based Image Analysis (NMBIA&#039;98)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">July, 1–3</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Glasgow, Scotland</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rennekamp, Frank</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Datenbank gestützte Verwaltung kalibrierter Bildsequenzen zur Qualitätsbewertung von Algorithmen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Fakultät für Physik und Astronomie Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter Geißler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Depth-from-Focus zur Messung der Größenverteilung durch Wellenbrechen erzeugter Blasenpopulationen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Cohen, A. J.</style></author><author><style face="normal" font="default" size="100%">Tozer, D. J.</style></author><author><style face="normal" font="default" size="100%">Handy, N. C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Development and assessment of new exchange-correlation functionals</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Physics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">109</style></volume><pages><style face="normal" font="default" size="100%">6264-6271</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wiehler, K.</style></author><author><style face="normal" font="default" size="100%">Grigat, R.–R.</style></author><author><style face="normal" font="default" size="100%">Heers, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dynamic Circular Cellular Networks for Adaptive Smoothing of Multi–Dimensional Signals</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 5th IEEE Int. Workshop on Cellular Neural Networks and their Applications</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">April, 14–17</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">London</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Oliver Beringer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ein Messsystem zur Untersuchung von Sedimentverlagerungen und Durchmischungsprozessen mittels digitaler Bildfolgenanalyse</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In dieser Arbeit wurde in Zusammenarbeit mit der Bundesanstalt für Wasserbau, BAW, ein Messsystem zur Untersuchung von Sediment-verlagerungen mittels Bildfolgenanalyse entwickelt. Damit soll eine theoretisch-fundierte Basis für den Entwurf von Uferbebauungen und Dämmen geschaffen werden. Untersucht wurden die, aufgrund von hydraulischen Laständerungen, auftretenden Durchmischungs- und Transportprozesse an Kies/Sand Grenzschichten sowie die Fluidisierung von Sediment. Die Messungen wurden in einem speziell angefertigten großen Drucktank durchgeführt. Durch Auswertung von synchronisierten Bild- und Druckdaten konnten die relevanten physikalischen Größen gewonnen werden. Die Bildaufnahme erfolgte mittels Endoskopen und CCD-Kameras. Es wurde ein Bildsequenzkomprimierungsverfahren zur Vorauswertung der Bilddaten entwickelt. Die Berechnung von Geschwindigkeitsvektorfeldern erfolgte durch Analyse der Bildsequenzen mit einem Tensorverfahren. Farbkodierte Sandkörner sowie die Aufnahme von Farbbildern ermöglichen die Quantifizierung von Durchmischungsprozessen. Durch Segmentierung können die verschiedenfarbigen Sedimentschichten getrennt werden. Ein Dif- fusionsansatz liefert die gesuchten Durchmischungsparameter. Ein wesentlicher Teil der Arbeit bestand in der Entwicklung und Erprobung eines Beleuchtungssystems für die Bildaufnahme. Als Lichtquellen werden spezielle Mikro-LEDs eingesetzt. Diese Beleuchtung zeichnet sich durch ihre geringen Abmessungen, die hohe Homogenität und hohe Intensität aus.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carstens, Heiko</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ein Skalenraumverfahren zur Orts/Wellenzahl-Raum-Analyse winderzeugter Wasserwellen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The following thesis describes a new technique for the calculation of local wave numbers and the creation of a combined space/wave number-representation of images. The design of a steerable and separable smoothing filter and a corresponding Hilbert filter makes it possible to create a logarithmic scale-space of the local amplitude. An oversampled multigrid structure (pyramid) is used for the Implementierung. An additional decomposition of the orientation is carried out. As the method&#039;s response to a wave with a delta-shaped spectra is known, a superposition of partial waves can be separated and their individual absolute values, orientations and amplitudes be calculated. A calibration using test images shows the accuracy of the described technique and its robustness to noise. The scale-space method is applied to wave slope images and the results on single images are visualised. Saturation spectra of image sequences are calculated and and shown to match those computed by a discrete Fourier transform. Further possibilities of the technique are demonstrated by calculating spectra showing the distribution of the amplitudes as a function of the wave number.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">M. Stöhr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung dreidimensionaler Particle Tracking Velocimetry zur Messung der Zweiphasenströmung in Gas-Flüssig-Reaktoren</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung eines Systems zur dreidimensionalen Particle Tracking Velocimetry mit Genauigkeitsuntersuchungen und Anwendung bei Messungen in einem Wind-Wellen Kanal</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Leue</style></author><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">GOME mißt atmosphärische Stickoxide. Globale Biomassenverbrennung und Industrieemissionen</style></title><secondary-title><style face="normal" font="default" size="100%">Physik in unserer Zeit</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">29</style></volume><pages><style face="normal" font="default" size="100%">179</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Kümmerlen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Infrarot-Thermographie zum Studium physiologischer Parameter von Pflanzenblättern</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heers, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.–S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE Int. Conf. Image Proc.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct. 4–7</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Chicago</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Münsterer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">LIF measurements of concentration profiles in the aqueous mass boundary layer</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">190--196</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A laser-induced fluorescence (LIF) technique is described to measure vertical concentration profiles of gases in the aqueous mass boundary layer at a free water surface. The technique uses an acid-base reaction of the fluorescence indicator fluorescein at the water surface to visualize the concentration profiles. The technique is capable of measuring two-dimensional vertical concentration profiles at a rate of 200 frames/s and a spatial resolution of 16 um. The mass boundary layer at a free surface is characterized by significant fluctuations. Direct surface renewal is observed. The mean profiles also support rather surface renewal models than turbulent diffusion models.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peckar, W.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Rohr, K.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.–S.</style></author><author><style face="normal" font="default" size="100%">Spetzger, U.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linear and Incremental Estimation of Elastic Deformations in Medical Registration Using Prescribed Displacements</style></title><secondary-title><style face="normal" font="default" size="100%">Machine Graphics &amp; Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><number><style face="normal" font="default" size="100%">4</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">807–829</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurements of air--sea gas transfer using active and passive thermography</style></title><secondary-title><style face="normal" font="default" size="100%">Eurochem 387 &quot;Remote Sensing of Slicks and Air--Sea Interactions&quot;</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Uwe Schimpf</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurements of the air-sea gas transfer and its mechanisms by active and passive thermography</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE International Geoscience and Remote Sensing Symposium IGARSS &#039;98</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">484--486 vol.1</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In order to reliably measure air-sea gas transfer velocities in the field with a high spatial and temporal resolution a new technique has been developed called the controlled flux technique, CFT. The current implementation splits up into two independent techniques using active and passive thermography, respectively. The CFT field instrument has been successfully used during two research cruises along the California Pacific coast (MBL/CoOP, 1995) and on the North Atlantic (CoOP, 1997). In addition to in-situ gas transfer rates, thermography of the ocean surface gives direct insight into the mechanisms of gas transfer. It has been shown that surface renewal dominates the transfer processes even at low wind speeds.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Helmut Herrmann</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modulare Software für die höherdimensionale Bildverarbeitung</style></title><secondary-title><style face="normal" font="default" size="100%">5. ABW-Workshop, TA Esslingen 20.--21.01.1998</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Burkhardt, H.</style></author><author><style face="normal" font="default" size="100%">Neumann, B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Multichannel shape from shading techniques for moving specular surfaces</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV 1998</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer, Berlin</style></publisher><volume><style face="normal" font="default" size="100%">1407</style></volume><pages><style face="normal" font="default" size="100%">170--184</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peckar, W.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Rohr, K.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.S.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Carter, J.N.</style></author><author><style face="normal" font="default" size="100%">Nixon, M.S.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Non-Rigid Image Registration Using a Parameter-Free Elastic Model</style></title><secondary-title><style face="normal" font="default" size="100%">9th British Machine Vision Conference (BMVC`98)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 14–17</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Southampton/UK</style></pub-location><pages><style face="normal" font="default" size="100%">134–143</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sven Mann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Objektbasierte Bildfolgenanalyse zur Bewegungsbestimmung im in vitro Motility Assay unter Verwendung eines Strukturtensorverfahrens</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heers, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.–S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 1998</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Informatik aktuell</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Marxen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle Image Velocimetry in Strömungen mit starken Geschwindigkeitsgradienten</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Carsten Leue</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle tracking velocimetry beneath water waves. part II : water waves</style></title><secondary-title><style face="normal" font="default" size="100%">Exp. Fluids</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">24</style></volume><pages><style face="normal" font="default" size="100%">10-16</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Particle Tracking techniques described earlier in the first part of this paper (Hering et al. 1997) were used to study the flow field beneath mechanically generated and wind induced flow fields. Experiments were conducted at three different wind/wave facilities (Heidelberg, Delft and San Diego). Particle Tracking allows an extensive study of drift velocities, wave orbital motion and turbulent Reynolds Stress beneath water waves. Monte Carlo simulations show, that the effects of the moving water surface on the calculation of mean properties of a flow can easily be avoided by Lagrangian measurements. Due to micro-scale wave breaking friction velocity profiles show a significant increase of turbulence towards the interface.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dominik Schmund</style></author><author><style face="normal" font="default" size="100%">Mark Stitt</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative analysis of the local rates of growth of dicot leaves at a high temporal and spatial resolution, using image sequence analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Plant Journal</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">505--514</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new technique is presented for quantitative mapping of dicot leaf growth at high spatial and temporal resolution, at a speed making online-mapping feasible. Time lapse video sequences of growing leaves are captured by a personal computer (PC) with a frame-grabber board and a standard CCD camera, and evaluated using algorithms that have been recently developed to analyse motion in dynamic image sequences. Growth can be detected at under 1% per hour, with a time resolution of minutes and a spatial resolution of a few millimeters. The new technique has been verified by comparing it with classical approaches to map integrated growth. Diurnal courses of leaf growth of Ricinus communis and tobacco are presented to demonstrate the localised character of growth in leaves. Expansion growth is restricted to the base of the leaf and is restricted to a few hours at the end of the night and the start of the day. The high resolution of the method is illustrated by mapping the responses to step-changes in leaf turgor. A 3 bar turgor jump led to a rapid transient expansion over the entire length of the leaf that was partially reversed when the turgor was relaxed.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Leue</style></author><author><style face="normal" font="default" size="100%">Mark Wenig</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Ulrich Platt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Quantitative observation of biomass-burning plumes from GOME</style></title><secondary-title><style face="normal" font="default" size="100%">ESA Publications EOQ</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://esapub.esrin.esa.it/eoq/eoq58</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">58</style></volume><pages><style face="normal" font="default" size="100%">33--35</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wiehler, K.</style></author><author><style face="normal" font="default" size="100%">Grigat, R.–R.</style></author><author><style face="normal" font="default" size="100%">Heers, J.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Stiehl, H.–S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Real–Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 1998</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Informatik aktuell</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sehen, was man sonst nicht sieht</style></title><secondary-title><style face="normal" font="default" size="100%">Ruperto Carola</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.uni-heidelberg.de/uni/presse/RuCa3_98/jaehne.htm</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><pages><style face="normal" font="default" size="100%">32--36</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">D. Engelmann</style></author><author><style face="normal" font="default" size="100%">Christoph S. Garbe</style></author><author><style face="normal" font="default" size="100%">M. Stöhr</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Stereo particle tracking</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of the 8th International Symposium on Flow Visualization</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><pages><style face="normal" font="default" size="100%">240--249</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction</style></title><secondary-title><style face="normal" font="default" size="100%">J. of Math. Imag. and Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">271–292</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Hanno Scharr</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Dominik Schmund</style></author><author><style face="normal" font="default" size="100%">Ulrich Schurr</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Study of dynamical processes with tensor-based spatiotemporal image processing techniques</style></title><secondary-title><style face="normal" font="default" size="100%">ECCV 1998</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">1407</style></volume><pages><style face="normal" font="default" size="100%">322--336</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Image sequence processing techniques are used to study exchange, growth, and transport processes and to tackle key questions in environmental physics and biology. These applications require high accuracy for the estimation of the motion field since the most interesting parameters of the dynamical processes studied are contained in first-order derivatives of the motion field or in dynamical changes of the moving objects. Therefore the performance and optimization of low-level motion estimators is discussed. A tensor method tuned with carefully optimized derivative filters yields reliable and dense displacement vector fields (DVF) with an accuracy of up to a few hundredth pixels/frame for real-world images. The accuracy of the tensor method is verified with computer-generated sequences and a calibrated image sequence. With the improvements in accuracy the motion estimation is now rather limited by imperfections in the CCD sensors, especially the spatial nonuniformity in the responsivity. With a simple two-point calibration, these effects can efficiently be suppressed. The application of the techniques to the analysis of plant growth, to ocean surface microturbulence in IR image sequences, and to sediment transport is demonstrated.</style></abstract><custom3><style face="normal" font="default" size="100%">LNCS</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Hagen Spies</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tensor-based image sequence processing techniques for the study of dynamical processes</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Intern. Symp. On Real-time Imaging and Dynamic Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">International Society of Photogrammetry and Remote Sensing, ISPRS, Commision V</style></publisher><pages><style face="normal" font="default" size="100%">704--711</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bühl, M.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Theoretical Investigation of NMR Chemical Shifts and Reactivities of Oxovanadium (V) Compounds</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Computational Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">19</style></volume><pages><style face="normal" font="default" size="100%">113-122</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kubinyi, H.</style></author><author><style face="normal" font="default" size="100%">Fred A. Hamprecht</style></author><author><style face="normal" font="default" size="100%">Mietzner, T.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Threedimensional Quantitative Similarity-Activity Relationships (3DQSiAR) from SEAL Similarity Matrices</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Medicinal Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">2553-2564</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ulrike Lode</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tiefenrekonstruktion vertikaler Konzentrationsprofile in der wasserseitigen Grenzschicht mittels spektroskopischer laserinduzierter Fluoreszenz (LIF)</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new laser-induced fluorescence (LIF) technique is presented to reconstruct depthresolved vertical two-dimensional concentration profiles of gases solved in water. It was developed to study small-scale processes in the viscous mass boundary layer. The key idea originated from differential optical absorption spectroscopy (DOAS). A fluorescence indicator with pH-dependent fluorescence intensity was used as a tracer. Gases changing the pH-value when solved in water, can be detected by a linear change in fluorescence intensity. A second absorbing dye causes a non-linear change of the measured spectra. Therefore the shape of the fluorescence spectrum depends on the pathlenght the light travels from the depth it originates from up to the water surface. A laser-lightsheet perpendicular to the water-surface is created and the fluorescence light integrated over depth is being spectrally analyzed by the designed prism-spectrometer. The spectra were measured by a CCD-camera as gray value images. A method to do a wavelength calibration is presented and used. The performed measurements combined with the modelling of the underlying processes have proven, that a depth-resolved reconstruction of concetration profiles is feasible with this technique. The reconstruction leads to problems, that are beeing solved by means of discrete inverse theory. Eventually the optimum set of parameters, that can be chosen is being calculated.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lauer, Hermann</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Untersuchung der Neigungsstatistik von Wasseroberflächenwellen mittels eines schnellen, bildaufnehmenden Verfahrens</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Variational approaches to Image Segmentation and Feature Extraction</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Hamburg, Comp. Sci. Dept.</style></publisher><pub-location><style face="normal" font="default" size="100%">Hamburg, Germany</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phdHabilitation thesis</style></work-type><notes><style face="normal" font="default" size="100%">in German</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark Wenig</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Wolkenklassifizierung mittels Bildsequenzanalyse auf GOME-Satellitendaten</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Martin Bentele</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zeitliche Rekonstruktion und Visualisierung dynamischer Prozesse</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Helmut Herrmann</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ahlers, R. -J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">3D-Exploration mit einer sich bewegenden Kamera</style></title><secondary-title><style face="normal" font="default" size="100%">Bildverarbeitung&#039;97, Forschen, Entwickeln, Anwenden</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><publisher><style face="normal" font="default" size="100%">Technische Akademie Esslingen</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Borchers, O. J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bestimmung von Grösse und Geschwindigkeit der dispersen Phase in Gas-Flüssig-Strömungen mittels digitaler Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Jochen Dieter</style></author><author><style face="normal" font="default" size="100%">T. Netzsch</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">A. Grün</style></author><author><style face="normal" font="default" size="100%">H. Kahmen</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A comprehensive study of algorithms for multidimensional flow field diagnostics</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 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Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The circular wind wave facilities at the University of Heidelberg</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer - Selected papers from the Third International Symposium on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">505--516</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">van Vliet, P.</style></author><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Delft Hydraulics Large Wind-Wave Flume</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer---Selected Papers from the Third International Symposium of Air--Water Gas Transfer in Heidelberg</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">499--502</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Thomas Scholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Schmidt, C.</style></author><author><style face="normal" font="default" size="100%">H. Suhr</style></author><author><style face="normal" font="default" size="100%">G. Wehnert</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ahlers, R. -J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Depth-from-Focus Verfahren zur absoluten Größen- und Konzentrationsbestimmung kleiner Teilchen</style></title><secondary-title><style face="normal" font="default" size="100%">Bildverarbeitung&#039;95 - Forschen, Entwickeln, Anwenden</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Technische Akademie Esslingen</style></publisher><pages><style face="normal" font="default" size="100%">365--380</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">E. J. Bock</style></author><author><style face="normal" font="default" size="100%">J. B. Edson</style></author><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author><author><style face="normal" font="default" size="100%">A. Karachintsev</style></author><author><style face="normal" font="default" size="100%">W. R. McGilles</style></author><author><style face="normal" font="default" size="100%">R. K. Nelson</style></author><author><style face="normal" font="default" size="100%">K. Hansen</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">T. Hara</style></author><author><style face="normal" font="default" size="100%">B. M. Uz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Jochen Dieter</style></author><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Description of the science plan for the April 1995 CoOP experiment, `gas transfer in coastal waters&#039;, performed from the research vessel New Horizon</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">801--810</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digital Image Processing --- Concepts, Algorithms, and Scientific Applications</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/945151500</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">3</style></edition><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Netzsch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dreidimensionale Particle Tracking Velocimetry</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/945379838</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Münsterer</style></author><author><style face="normal" font="default" size="100%">Hans Jürgen Mayer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Dual-tracer measurements of concentration profiles in the aqueous mass boundary layer</style></title><secondary-title><style face="normal" font="default" size="100%">Air-water Gas Transfer, Selected Papers from the Third International Symposium on Air--Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">637--648</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">H.-J. Köhler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ahlers, R. -J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Effiziente Filterstrukturen auf Mehrgitter-Datenstrukturen</style></title><secondary-title><style face="normal" font="default" size="100%">Bildverarbeitung&#039;95 - Forschen, Entwickeln, Anwenden</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Technische Akademie Esslingen</style></publisher><pages><style face="normal" font="default" size="100%">43--57</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Scholz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ein Depth from Focus-Verfahren zur On Line-Bestimmung der Zell-kon-zentration bei Fermentationsprozessen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Enhanced turbulence beneath short wind-induced water waves as derived from Lagrangian flow fields</style></title><secondary-title><style face="normal" font="default" size="100%">IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><pages><style face="normal" font="default" size="100%">386</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Breaking waves and wind stress are a primary source for near-surface turbulence. Particle Tracking Velocimetry (PTV) was used developed to study turbulence beneath short wind-induced waterwaves at the circular wind-wave facility of the Institute for Environmental Physics. A thick light sheet (1-4 cm) was applied to illuminate small polystyrol seeding particles. The depth of the light sheet was chosen such that the particles stay long enough in the illuminated area to enable tracking over several wave periods. The flow field is observed by a digital CCD camera. Recording image sequences at up to 200 Hz allow an extensive study of the flow field. An automatic tracking algorithm was developed for the evaluation of the trajectories. Lagrangian flow field measurements offer an ideal approach to the study of drift profiles in the turbulent wave region, in addition yielding the thickness of that layer. Also bulk velocity and surface velocities can be derived. A measure for the turbulence was gained by the calculation of the friction velocity profile by correlating horizontal and vertical velocity components (eddy correlation technique). These profiles show a very interesting behavior. While the friction velocity profile is constant in the bulk, an abrupt enhancement of Reynolds stress from the bulk towards the water surface up to a factor of 10 is observed. The enhanced dissipation of kinetic energy beneath strong-breaking surface waves under fetch conditions was measured at Lake Ontario. Here, an enhancement dissipation factor of 5-60 was found. Our measurements now indicate that micro-scale wave breaking is sufficient to produce significant turbulence enhancement. This result contributes significantly to the understanding of gas exchange through the aqueous boundary layer and indicates that wave/turbulence interaction deserves further more detailed attention.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hans Jürgen Mayer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung einer laserinduzierten Fluoreszenz-Technik zum Messen von Konzentrationsprofilen und Diffusionskonstanten</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author><author><style face="normal" font="default" size="100%">M. Menzel</style></author><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Flow- and wave-fields associated with micro-scale wave breaking as measured simultaneously by particle tracking and wave slope visualization</style></title><secondary-title><style face="normal" font="default" size="100%">IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><pages><style face="normal" font="default" size="100%">385</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Longuet-Higgins predicted the development of a capillary roller at the crest of a short gravity wave, being the consequence of the existence of parasitic capillaries, (Capillary rollers and bores, J. Fluid Mech, Jan. 1992). According to the theory any free surface, at which the tangential stress vanishes, a surface vorticity is produced. The mean vorticity at the edge of the Stokes layer is of the order of 2(ak) f, ak being the slope and f the radian frequency of the wave. Hence capillary waves, with a high value of a are accompanied by particular large vorticity feeding the vortex roler at the crest of a short gravity wave. This phenomenon has been investigated at the circular facility of the Institute for Environmental Physics. At the same location, simultaneous time series of wave slope images and the Lagrangian flow field were obtained using an integrated optical set-up, including particle-tracking and wave-slope instruments. The particle tracking measurement yields drift- and bulk-velocities, Reynold stresses and the vorticity distribution beneath the wave, while the imaging wave slope gauges gives the 2D-slope and the height of the waves. First results from the measurement campaign of early 1995 will be presented.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Reinelt, S.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Heat as a proxy tracer for gas exchange measurements in the field: principles and technical realization</style></title><secondary-title><style face="normal" font="default" size="100%">Air--Water Gas Transfer: Selected Papers from the Third International Symposium on Air--Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">405--413</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">T. Netzsch</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Baltsavias, E. P.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A high-performance system for 3-dimensional particle tracking velocimetry in turbulent flow research using image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. ISPRS Intercommission workshop From Pixels to Sequences, Zürich, March 22-24</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Risc Books</style></publisher><volume><style face="normal" font="default" size="100%">30 Part 5W1</style></volume><pages><style face="normal" font="default" size="100%">202--207</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">R. Shear</style></author><author><style face="normal" font="default" size="100%">W. K. Melville</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Horizontal and vertical spatial structures of turbulence beneath short wind waves</style></title><secondary-title><style face="normal" font="default" size="100%">IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><pages><style face="normal" font="default" size="100%">384</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A poorly understood aspect of surface-wave physics is wave dissipation. Wave breaking may lead to enhanced turbulence levels and consequently enhanced viscous dissipation at the smallest scales. Recent laboratory, field and modelling studies have provided preliminary evidence that wave breaking may lead to dissipation levels one to two orders of magnitude greater than those in the classical logarithmic layer. However, the experimental techniques to study these phenomena are not well developed, and consequently the turbulence structure near the water surface is poorly understood. A method is described to simultaneously investigate the horizontal and vertical structure of the turbulent flow and wave fields at the water surface and in an intersecting two-dimensional vertical plane. The technique combines an active infrared technique (CFT) in the horizontal plane with digital particle imaging velocimetry (DPIV) in the vertical plane. The DPIV uses a CCD camera in an underwater housing to track submerged particles moving through a pulsed vertical laser light sheet aligned in the wind direction. The CFT uses infrared radiators to heat up the upper 20 micrometres of the water column and observes the resulting heat patterns with an infrared camera. Both instruments use digital image processing techniques to compute the flow variables and images of the turbulent structures. These initial experiments were conducted in the large wind-wave channel at Delft Hydraulics, The Netherlands, in September/October, 1994. The images show a dramatic transition from laminar to turbulent flow with the onset of significant wave activity coincident with vorticity transport from the surface to deeper layers.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Buckingham, M. J.</style></author><author><style face="normal" font="default" size="100%">Potter, J. R.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">An imaging optical technique for bubble measurements</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Sea Surface Sound &#039;94</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">World Scientific</style></publisher><pages><style face="normal" font="default" size="100%">290--296</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Impact of quantitative visualization and image processing on the study of small-scale air-sea interaction</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">3--12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Scholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">H. Suhr</style></author><author><style face="normal" font="default" size="100%">G. Wehnert</style></author><author><style face="normal" font="default" size="100%">P. Geissler</style></author><author><style face="normal" font="default" size="100%">K. Schneider</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">In situ determination of cell concentration in bioreactors with a new depth from focus technique</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Analysis of Images and Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">970</style></volume><pages><style face="normal" font="default" size="100%">392--399</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new depth-from-focus technique is introduced that requires only a single image to determine the distance of simple shaped objects from the focal plane. The technique has been applied to evaluate the concentration of cells in a bioreactor during a fermentation process. Since the low-intensity fluorescent light gathered by a light-amplifying camera results in images of low signal-to-noise ratio, an adaptive smoothing filter is used. A sharpness criterion derived from bandpass decomposition of the image in a Laplacian pyramid is used to define a virtual measuring volume. In this volume, process parameters such as cell concentration, cell size and intensity of cell fluorescence are evaluated. The technique is also suitable for other types of simple objects.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">In situ measurements of the air-sea gas transfer rate during the MBL/CoOP west coast experiment</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer - Selected Papers from the Third International Symposium on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">775--784</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H. Suhr</style></author><author><style face="normal" font="default" size="100%">G. Wehnert</style></author><author><style face="normal" font="default" size="100%">K. Schneider</style></author><author><style face="normal" font="default" size="100%">C. Bittner</style></author><author><style face="normal" font="default" size="100%">Thomas Scholz</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">T. Scheper</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">In-situ microscopy for on-line characterization of cell-populations in bioreactors, including concentration measurements by depth from focus</style></title><secondary-title><style face="normal" font="default" size="100%">Biotechnology and Bioengineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">106--116</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new technique is presented which allows the use of a front-end sensor head for in situ and on-line characterization of cell concentration and cell size during fermentation. An epifluorescence microscope is mounted in a port of a bioreactor viewing directly into the agitated broth. Still images from cells are generated using pulsed illumination. They are directly visualized on a monitor and used for automatic image analysis. The cell concentration and morphological information are determined by counting and evaluating the cell images with respect to their depth from focus characteristic. An in situ microscope was successfully tested during yeast fermentations and yielded results which correlated well with results from a hemocytometer.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Markus Beyer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interaction of short wind waves and turbulent shear flow as revealed by simultaneous wave slope and surface turbulence visualization</style></title><secondary-title><style face="normal" font="default" size="100%">IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><pages><style face="normal" font="default" size="100%">387</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Much work has been undertaken to understand the energy balance of wind waves, but almost no studies about the interaction between waves and turbulence are available up to now. In order to observe waves and surface turbulence simultaneously, measurements were performed in the large wind wave flume of Delft Hydraulics, The Netherlands. A new instrumental setup was used, combining an imaging slope gauge (ISG) with an active infrared technique (CFT). The ISG records time series of the two-dimensional slope within an image sector of 0.4m by 0.4m and a temporal resolution of 16ms. With the CFT, infrared radiators are used to force a constant heat flux through the water surface. The resulting surface temperature within the first 20 fim is observed with a Radiance 1 focal plane array IR camera in the 3-5um wavelenght range. With digital image processing techniques the dynamical behaviour of the water surface can be extracted. The resulting image sequences of temperature patterns are used to compute the 2-D flow field at the water surface. The flow field and wave slope image sequences reveal the significant influence of the turbulence distribution on the shape of the short wind waves. By analyzing properties of the wave field as wave length and phase velocity provided by the ISG the spatial structures of the surface waves can be compared with the scales of the turbulence structures and their dynamics.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roland Bremeyer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Lokale Orientierung zur Auswertung von Streakbildern</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-426</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurements of bubble size distributions with an optical technique based on depth from focus</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">351--362</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurements of enhanced turbulence in short wind-induced water waves</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">775--784</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurements of short ocean waves during the MBL ARI West Coast Experiment</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">165--173</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jochen Dieter</style></author><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Roland Bremeyer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurements of velocity profiles in the aqueous boundary layer at a wind-driven water surface</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">145--152</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Braess, Henning</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messungen der Krümmungsverteilung von Wasseroberflächenwellen mittels digitaler Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Peckar, W.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">V. Hlaváč, R. Šára</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Motion-Based Identification of Deformable Templates</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP &#039;95)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Lect. Notes in Comp. Sci.</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Sept. 6-8</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Springer Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Prague, Czech Republic</style></pub-location><volume><style face="normal" font="default" size="100%">970</style></volume><pages><style face="normal" font="default" size="100%">122-129</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new instrument for the optical measurement of the fine structure of the water surface in the field</style></title><secondary-title><style face="normal" font="default" size="100%">IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><pages><style face="normal" font="default" size="100%">388</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The measurement of the fine structure of the ocean surface constitutes a difficult task. Here, we describe the technical details of a new instrument that allows the full reconstruction of the water surface with sub-millimeter spatial resolution in areas of up to of 15cm x 20cm The single camera system is based on the imaging slope gauge used in previous laboratory experiments to obtain a single slope component of the water surface. The light source was modified such that two arrays of light emitting diodes (LEDs) are used to produce intensity gradients at right anglesJn order to obtain both slope components of the water surface with a monochrome CCD camera, the LED arrays are illuminated successively within a few milliseconds. The whole system is mounted on a the frame of a buoy which follows the orbital motion of the long waves and is critically damped to prevent instability in rougher conditions. Even at high wind speeds, the ambient wave field is not disturbed by the structure. The onboard computer for data acquisition and image processing is remotely controlled from a ship. Battery power allows free-floating operation for a duration of approximately eight hours. In addition to the optical wave slope gauge, the instrument contains a digital gyro sensor which collects stabilized pitch, roll, and magnetic azimuth data, a Young wind speed anemometer, as well as global positioning and temperature sensors.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Günther Balschbach</style></author><author><style face="normal" font="default" size="100%">M. Menzel</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A new instrument to measure steep wind-waves</style></title><secondary-title><style face="normal" font="default" size="100%">IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><pages><style face="normal" font="default" size="100%">387</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Current instruments to measure the spatial structure of short waves are limited to quite low slopes. Capillary waves, however, may show slopes well above one (45 degrees inclination). A new instrument has been designed to measure the spatial structure of steep capillary waves. It was designed to specifically address a number of basic questions: Is the limiting form for Crapper capillary waves ever reached? Can a bubble be trapped by such a wave? Do solitary capillary/gravity waves - as recently postulated by Longuet-Higgins - exist in wind/wave fields? The instrument uses the basic design as used in previous instruments of our research group but includes two important enhancements. By the use of a telecentric illumination and camera lens, the influence of wave height on the slope measurement is eliminated. Colored light is used to encode both slope components simultaneously and to correct for uneven intensity distribution in the light source and intensity losses by reflection at the water surface. This contribution describes the setup of the instrument and its calibration in detail. First results from the circular wind/wave facility at Heidelberg University are reported in a separate paper.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Scheuermann, T.</style></author><author><style face="normal" font="default" size="100%">Pfundt, G.</style></author><author><style face="normal" font="default" size="100%">Eyerer, P.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Oberflächenkonturvermessung mikroskopischer Objekte durch Projektion statistischer Rauschmuster</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 17. DAGM-Symposium Mustererkennung, Bielefeld, 13.-15. September 1995</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><pages><style face="normal" font="default" size="100%">319--326</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Peter Geißler</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Baltsavias, E. P.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">One-image depth-from-focus for concentration measurements</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. ISPRS Intercommission Workshop `From Pixels to Sequences&#039;, Zurich, March 22 - 24, 1995, In Int&#039;l Arch. of Photog. and Rem. Sens.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">RISC Books</style></publisher><volume><style face="normal" font="default" size="100%">XXX-5W1</style></volume><pages><style face="normal" font="default" size="100%">122--127</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Merle, M.</style></author><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Particle tracking in space time sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Analysis of Images and Patterns</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">970</style></volume><pages><style face="normal" font="default" size="100%">294--301</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A particle tracking technique at high particle concentration for the evaluation of flow fields beneath water waves is described. A 1-4 cm thick light sheet parallel to the main wave propagation direction was used to illuminate small polystyrol seeding particles. The depth of the light sheet was chosen such that the particles stay long enough in the illuminated area to enable tracking. An area of up to 14.0x10.0 cm2 is observed by a CCD camera. An automatic tracking algorithm is described to 800 particles/image, yielding both Lagrangian and Eulerian vector field. Test measurements show that the standard deviation for the velocity estimation is below 3%.</style></abstract><custom3><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Frank Hering</style></author><author><style face="normal" font="default" size="100%">Merle, M.</style></author><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Baltsavias, E. P.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A robust technique for tracking particles over long image sequences</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. ISPRS Intercommission Workshop `From Pixels to Sequences&#039;, Zurich, March 22 - 24, 1995, In Int&#039;l Arch. of Photog. and Rem. Sens.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">RISC Books</style></publisher><volume><style face="normal" font="default" size="100%">XXX-5W1</style></volume><pages><style face="normal" font="default" size="100%">74--79</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spatial measurement of short ocean waves during the MBL-ARI West Coast Experiment</style></title><secondary-title><style face="normal" font="default" size="100%">IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><pages><style face="normal" font="default" size="100%">390</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">During the MBL-ARI West Coast Experiment in April 1995 a new instrument for the optical measurement of ocean waves in the capillary-gravity and capillary range will be deployed. The optical system is mounted on the frame of a wave follower, and provides digital images of the water surface gradient in an area of 15cm X 20cm with submillimeter resolution at sampling rates of up to lOOHz. From single gradient images, the height of the water surface is reconstructed, and from the time sequences, intermittent phenomena such as microscale wave breaking can be studied. The instrument will be used in conjunction with the scanning laser slope gauge on the LADAS research catamaran from Woods Hole Oceanographic Institute. The intercomparison of the data allows an accurate performance assessment of both systems.In addition to the surface gradient image sequences, long wave data, wind speeds, as well as air and water temperatures will be collected. We present first results from the MBL-ARI West Coast Experiment, including directional wave number spectra, directional wave number-frequency spectra, surface reconstructions, and 2-D slope distributions of short ocean waves.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">R. Massen</style></author><author><style face="normal" font="default" size="100%">B. Nickolay</style></author><author><style face="normal" font="default" size="100%">H. Scharfenberg</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Technische Bildverarbeitung - Maschinelles Sehen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/94569895X</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andre Fachat</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Untersuchung eines 3D-Aufnahmeverfahrens für Strömungsvorgänge an der Wasseroberfläche</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-436</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nelson M. Frew</style></author><author><style face="normal" font="default" size="100%">E. J. Bock</style></author><author><style face="normal" font="default" size="100%">W. R. McGilles</style></author><author><style face="normal" font="default" size="100%">A. Karachintsev</style></author><author><style face="normal" font="default" size="100%">T. Hara</style></author><author><style face="normal" font="default" size="100%">Thomas Münsterer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. C. Monahan</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Variation of air--water gas transfer with wind stress and surface viscoelasticity</style></title><secondary-title><style face="normal" font="default" size="100%">Air-water Gas Transfer, Selected Papers from the Third International Symposium on Air-Water Gas Transfer</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">AEON</style></publisher><pages><style face="normal" font="default" size="100%">529--541</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Vision for waves</style></title><secondary-title><style face="normal" font="default" size="100%">IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><number><style face="normal" font="default" size="100%">19</style></number><pages><style face="normal" font="default" size="100%">391</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">For almost a century, researchers have tried to use imaging techniques for spatial measurement of short wind waves. A comparative survey of these techniques is given. The early techniques were all based on stereo imaging, while more recently shape from reflection (Stilwell photography) and shape from refraction techniques such as laser scanning slope gauges and imaging surface gradient methods have been used by a number of researchers. Theoretical analysis and experimental evidence clearly show that the shape from refraction techniques are the most suitable method for spatial measurements of short gravity and capillary waves. Although these techniques give an unprecedented insight into the dynamics of short wind waves, only digital image processing techniques open the way for a quantitative analysis. A number of techniques beyond Fourier analysis including scale-space analysis, Hilbert transformation, and various advanced filter techniques are envisioned to have a major impact in the near future.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dominik Schmund</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Voruntersuchung der Einsatzmöglichkeiten digitaler Bildverarbeitung zur Analyse von Transportvorgängen und Wachstumsprozessen in Pflanzen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">University of Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-422</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Ahlers, R. -J.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Zuverlässig, Schnell und Genau? - Bedeutung von Algorithmen in der Bildverarbeitung für die Praxis</style></title><secondary-title><style face="normal" font="default" size="100%">Bildverarbeitung&#039;95 - Forschen, Entwickeln, Anwenden</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1995</style></year></dates><publisher><style face="normal" font="default" size="100%">Technische Akademie Esslingen</style></publisher><pages><style face="normal" font="default" size="100%">3--14</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analytical studies of low-level motion estimators in space-time images using a unified filter concept</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Conference on Computer Vision and Pattern Recognition (CVPR &#039;94), Seattle, 20.-23. June 1994</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><pages><style face="normal" font="default" size="100%">229--236</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Reinelt, S.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bestimmung der Transfergeschwindigkeit mittels CFT mit Wärme als Tracer</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Kropatsch, W.G.</style></author><author><style face="normal" font="default" size="100%">Bischof, H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 1994</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Informatik Xpress</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Technische Universität Wien</style></publisher><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">178–185</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildverarbeitung für die Meeresforschung</style></title><secondary-title><style face="normal" font="default" size="100%">Ruperto Carola</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.uni-heidelberg.de/uni/presse/rc7/2.html</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><pages><style face="normal" font="default" size="100%">10--15</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Heikkonen, J.</style></author><author><style face="normal" font="default" size="100%">Koikkalainen, P.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Building Trajectories via Selforganization from Spatiotemporal Features</style></title><secondary-title><style face="normal" font="default" size="100%">12th Int. Conf. on Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct 9-13</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Jerusalem, Israel</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefan Waas</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Combined height/slope/curvature measurements of short ocean wind waves</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A comparative analytical study of low-level motion estimators in space-time images</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 16. DAGM-Symposium Mustererkennung</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Peter Geißler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Depth from focus with one image</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Conference on Computer Vision and Pattern Recognition (CVPR &#039;94), Seattle, 20.-23. June 1994</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><pages><style face="normal" font="default" size="100%">713--717</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sprengel, R.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author><author><style face="normal" font="default" size="100%">Neumann, B.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Kropatsch, W.G.</style></author><author><style face="normal" font="default" size="100%">Bischof, H.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Detection of Visual Data Transitions in Nonlinear Parameter Space</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 1994</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Informatik Xpress</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year><pub-dates><date><style  face="normal" font="default" size="100%">September</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Technische Universität Wien</style></publisher><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">315–323</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jochen Dieter</style></author><author><style face="normal" font="default" size="100%">Roland Bremeyer</style></author><author><style face="normal" font="default" size="100%">Frank Hering</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Flow measurements close to the free air/sea interface</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 7th International Symposium on Applications of Laser Techniques to Fluid Mechanics, Lisbon, Portugal, July 11.--14. 1994</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><volume><style face="normal" font="default" size="100%">II</style></volume><pages><style face="normal" font="default" size="100%">22.2.1--6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Kandlbinder</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gasaustauschmessungen mit Sauerstoff</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-385</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Stefan Waas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Imaging of short ocean wind waves: a critical theoretical review</style></title><secondary-title><style face="normal" font="default" size="100%">J. Opt. Soc. Am. A</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">2197--2209</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Optical techniques to measure the small-scale shape of the ocean surface, i.e., the short wind waves, are theoretically reviewed. The well-known shape-from-shading and shape-from-stereo paradigms from computer vision are applied to a specular reflecting surface such as the ocean surface and are used to study a variety of techniques. The analysis shows that most techniques for the imaging of short wind waves, such as Stilwell photography and various stereo techniques, have significant deficiencies. Stereophotography is plagued by insufficient height resolution for small waves and by the problem that, because of the specular nature of reflection at the water surface, features seen in one image are not necessarily found in the other (correspondence problem). Techniques based on light reflection (shape from reflection) are useful only for deriving wave-slope statistics, and techniques based on light refraction (shape from refraction) are found to be most suitable for wave slope imaging.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Horst Haußecker</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">In-situ measurements of the air-sea gas transfer using heat as a proxy tracer</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 2nd Inter. Conf. on Air-Sea Interaction and on Meteorology and Oceanography of the Coastal Zone, Lisbon, 22.--27. September 1994</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Markus Beyer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">W. K. Melville</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Laboratory studies of long-wave/short-wave interaction using wavelet analysis of space-time images</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 2nd Inter. Conf. on Air-Sea Interaction and on Meteorology and Oceanography of the Coastal Zone, Lisbon, 22.--27. September 1994</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Münsterer</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A LIF technique for the measurement of concentration profiles in the aqueous mass boundary layer</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.\ 7th Intern.\ Symp.\ on Appl.\ of Laser Techn.\ to Fluid Mechanics, Lisbon, Portugal, July 11.--14. 1994</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><volume><style face="normal" font="default" size="100%">II</style></volume><pages><style face="normal" font="default" size="100%">29.4.1--5</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Klinke</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurements of the small-scale structure of the water surface with a new optical instrument</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 2nd Inter. Conf. on Air-Sea Interaction and on Meteorology and Oceanography of the Coastal Zone, Lisbon, 22.--27. September 1994</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lauer, H.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung der Neigungsverteilung von Wasseroberflächenwellen mittels digitaler Bilverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-382</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jochen Dieter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung von Strömungen in der viskosen Grenzschicht an der Wasseroberfläche</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1994</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-413</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Scholz</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">H. 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R.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung eines videometrischen Verfahrens zur Messung der Wellenhöhe von Wasseroberflächenwellen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-369</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">C. J. Calkoen</style></author><author><style face="normal" font="default" size="100%">P. Snoeij</style></author><author><style face="normal" font="default" size="100%">van Halsema, D.</style></author><author><style face="normal" font="default" size="100%">J. Vogelzang</style></author><author><style face="normal" font="default" size="100%">W. A. Oost</style></author><author><style face="normal" font="default" size="100%">C. J. Calkoen</style></author><author><style face="normal" font="default" size="100%">P. Snoeij</style></author><author><style face="normal" font="default" size="100%">J. Vogelzang</style></author><author><style face="normal" font="default" size="100%">W. A. 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C.</style></author><author><style face="normal" font="default" size="100%">John S. Gulliver</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">From mean fluxes to a detailed experimental investigation of the gas transfer process</style></title><secondary-title><style face="normal" font="default" size="100%">2nd International Symposium on Gas Transfer at Water Surfaces - Air--Water Mass Transfer, Minneapolis 1990</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><publisher><style face="normal" font="default" size="100%">ASCE</style></publisher><pages><style face="normal" font="default" size="100%">244--256</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Funktionalanalytische Methoden zur Bestimmung von Bewegungsinformation aus TV-Bildfolgen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><publisher><style face="normal" font="default" size="100%">Fakultät für Informatik, Universität Karlsruhe (TH)</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">phd</style></work-type></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Wilhelms, S. C.</style></author><author><style face="normal" font="default" size="100%">John S. Gulliver</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurement of wave-induced turbulent flow structure using digital image sequence analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Mass Transfer, selected papers from the 2nd International Symposium on Gas Transfer at Water Surfaces, September 11--14, 1990, Minneapolis, Minnesota</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><publisher><style face="normal" font="default" size="100%">ASCE</style></publisher><pages><style face="normal" font="default" size="100%">200--209</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">van Halsema, D.</style></author><author><style face="normal" font="default" size="100%">P. Snoeij</style></author><author><style face="normal" font="default" size="100%">C. J. Calkoen</style></author><author><style face="normal" font="default" size="100%">W. A. Oost</style></author><author><style face="normal" font="default" size="100%">J. Vogelzang</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modulation of the microwave backscatter by long gravity waves as measured in a very large wind/wave flume</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IGARSS &#039;91</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">1293--1296</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Wilhelms, S. C.</style></author><author><style face="normal" font="default" size="100%">John S. Gulliver</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">New experimental results on the parameters influencing air-sea gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Air-Water Mass Transfer, selected papers from the 2nd International Symposium on Gas Transfer at Water Surfaces, September 11--14, 1990, Minneapolis, Minnesota</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><publisher><style face="normal" font="default" size="100%">ASCE</style></publisher><pages><style face="normal" font="default" size="100%">582--592</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Klinke</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zweidimensionale Wellenzahlspektren von kleinskaligen winderzeugten Wasseroberflächenwellen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1991</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-358</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic particle tracking beneath a wind-stressed wavy water surface with image processing</style></title><secondary-title><style face="normal" font="default" size="100%">Proc.\ 5th Int. Symposium Flow Visualization, Praque 1989</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1990</style></year></dates><pages><style face="normal" font="default" size="100%">943--956</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bister, D.</style></author><author><style face="normal" font="default" size="100%">Rohr, K.</style></author><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Großkopf, R.E.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 1990, 12. DAGM-Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Informatik-Fachberichte</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1990</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Oberkochen-Aalen</style></pub-location><volume><style face="normal" font="default" size="100%">254</style></volume><pages><style face="normal" font="default" size="100%">44–51</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. British Machine Vision Conference</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1990</style></year><pub-dates><date><style  face="normal" font="default" size="100%">sep</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Oxford/UK</style></pub-location><pages><style face="normal" font="default" size="100%">109–114</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Snoeij</style></author><author><style face="normal" font="default" size="100%">C. J. Calkoen</style></author><author><style face="normal" font="default" size="100%">W. A. Oost</style></author><author><style face="normal" font="default" size="100%">van Halsema, D.</style></author><author><style face="normal" font="default" size="100%">J. Vogelzang</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dual-polarized scatterometer measurements of generated Wind and Gravity Wave in a Very Large Wind/Wave Tank</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IGARSS&#039;90</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1990</style></year></dates><pages><style face="normal" font="default" size="100%">2157--2160</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Snoeij</style></author><author><style face="normal" font="default" size="100%">J. Vogelzang</style></author><author><style face="normal" font="default" size="100%">C. J. Calkoen</style></author><author><style face="normal" font="default" size="100%">W. A. Oost</style></author><author><style face="normal" font="default" size="100%">van Halsema, D.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A high resolution scatterometer for radar backscatter measurements of wind generated waves in wind/wave tanks</style></title><secondary-title><style face="normal" font="default" size="100%">European Micro Wave Conference 1990</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1990</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In February 1988, combined measurements of microwave backscatter, wind, waves and gas exchange have been carried out in the large Delft Hydraulics wind/wave tank, with wind generated waves. In March 1989 a second exaperiment took place in the huge outdoor wave tank, the Delta tank, with wind generated waves and mechanically generated waves. These experiments were perfonned in the framework of the VIERS-1 project. In this project a number of Dutch and German institutes cooperate. Main objective is to increase the knowledge about the physics involved in the interaction of microwaves and the ocean surface and, from that point, to an improvement of the algorithms used for determination of wind speed and direction from satelliteborne microwave scatterometers. A second objective is to study the relation between the gas exchange at the water surface and the microwave backscatter. To achieve these objectives two wind/wave tank experiments and one ocean based platform experiment are scheduled. In this paper, the VIERS-l program will be outlined. The features of a specially designed high resolution scatterometer will be described and some results of both tank expermnents will be shown.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung turbulenter Strömungen unterhalb der windwellenbewegten Wasseroberfläche mittels digitaler Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1990</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/910573255</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Faugeras, O.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Motion determination in space-time images</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. Computer Vision -- ECCV 90, Lecture Notes in Computer Science 427</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1990</style></year></dates><pages><style face="normal" font="default" size="100%">161--173</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A new approach to determine motion from multiple images of a sequence is presented. Motion is regarded as orientation in a three-dimensional space with one time and two space coordinates. The algorithm is analogous to an eigenvalue analysis of the inertia tensor. Besides the determination of the displacement vector field it allows the classification of four regions with regard to motion: a) constant regions, where no velocity determination is possible; b) edges, where the velocity component perpendicular to the edge is determined; c) corners, where both components of the velocity vector are calculated; d) motion discontinuities, which are used to mark the boundaries between objects moving with different velocities. The accuracy of the new algorithm has been tested with artificially generated image sequences with known velocity vector fields. An iterative refinement technique yields more accurate results than the usage of higher order approximations to the first spatial and temporal derivatives. Temporal smoothing significantly improves the velocity estimates in noisy images. Displacements between consecutive images can be computed with an accuracy well below 0.1 pixel distances.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Riemer, K.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Two-dimensional wave number spectra of small-scale water surface waves</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1990</style></year></dates><number><style face="normal" font="default" size="100%">C7</style></number><volume><style face="normal" font="default" size="100%">95</style></volume><pages><style face="normal" font="default" size="100%">11531--11646</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Two-dimensional wave slope spectra have been measured in the large Delft wind-wave facility using an imaging optical technique and digital image processing. The data cover wavelengths from 0.4 to 24 cm and wind speeds (U 10) from 2.7 to 17.2 ms-1. The spectral densities of small gravity waves at higher wind speeds are proportional to k ^-3.5 and u*. Capillary-gravity and capillary waves show features which clearly manifest that the energy balance for these waves is much different from that for gravity waves. The degree of saturation is approximately constant at a given wind speed, but strongly increases with friction velocity (proportional to u*^2.5). A sharp cutoff, which is almost independent of the wind speed, occurs at a wavelength of about 7 mm.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Automatic particle tracking velocimetry beneath a wind-stressed wavy water surface with image processing</style></title><secondary-title><style face="normal" font="default" size="100%">5th International Symposium on Flow Visualization</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">van Halsema, D.</style></author><author><style face="normal" font="default" size="100%">C. J. Calkoen</style></author><author><style face="normal" font="default" size="100%">W. A. Oost</style></author><author><style face="normal" font="default" size="100%">P. Snoeij</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparisons of X-band Radar Backscatter Measurements with Area extended wave slop measurements made in a large Wind/Wave Tank</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IGARSS&#039;89</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">2997--3001</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>6</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Digitale Bildverarbeitung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/890489467</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Trukenmüller, Alfred</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Einflüsse von Viskosität u. Oberflächenspannung auf winderzeugte Wasseroberflächenwellen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-272</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Komen, G. J.</style></author><author><style face="normal" font="default" size="100%">W. A. Oost</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Energy balance in small-scale waves: an experimental approach using optical slope measuring technique and image processing</style></title><secondary-title><style face="normal" font="default" size="100%">Radar Scattering from Modulated Wind Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><pages><style face="normal" font="default" size="100%">105--120</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karl-Heinz Grosser</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung eines Verfahrens zur optischen Messung der Wellenhöhe von Wasseroberflächenwellen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-271</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Halsema, D.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">C. J. Calkoen</style></author><author><style face="normal" font="default" size="100%">P. Snoeij</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. A. Oost</style></author><author><style face="normal" font="default" size="100%">Komen, G. J.</style></author><author><style face="normal" font="default" size="100%">W. A. Oost</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">First results of the VIERS-1 experiment</style></title><secondary-title><style face="normal" font="default" size="100%">Radar Scattering from Modulated Wind Waves</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><publisher><style face="normal" font="default" size="100%">Kluwer Academic Publishers</style></publisher><pages><style face="normal" font="default" size="100%">49--57</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">P. Libner</style></author><author><style face="normal" font="default" size="100%">Fischer, R.</style></author><author><style face="normal" font="default" size="100%">Thomas Billen</style></author><author><style face="normal" font="default" size="100%">Erich J. Plate</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Investigating the transfer process across the free aqueous boundary layer by the controlled flux method</style></title><secondary-title><style face="normal" font="default" size="100%">Tellus</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">41B</style></volume><pages><style face="normal" font="default" size="100%">177--195</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Theory and experimental results of a new method are described directly investigating the transfer processes across the aqueous viscous boundary layer. The method is based on a known and controllable flux density being applied at the interface. Then the local transfer velocity can be determined by monitoring the tracer concentration at the water surface within minutes. Moreover, the time constant for the transport across the boundary layer (&quot;surface renewal time&quot;) can be measured directly. Comparison of the theoretical and measured frequency response of the boundary layer yields significant deviations. The technique is put into operation for heat transfer measurements. Direct comparisons with gas exchange measurements in several wind/wave facilities verify that the gas transfer velocity can be accurately extrapolated from the heat transfer measurements. A new way is opened both for detailed studies of the transfer processes in wind/wave facilities and the urgently needed direct parameterization of the transfer velocity as a function of windshear, wave parameters, and water turbulence in natural systems as rivers, lakes and the ocean. This paper includes (as a first example) measurements on the fetch dependency of the transfer process.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Motion determination in space-time images</style></title><secondary-title><style face="normal" font="default" size="100%">Image Processing III, SPIE Proceeding 1135, international congress on optical science and engineering, Paris, 24-28 April 1989</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><pages><style face="normal" font="default" size="100%">147--152</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Stefan Waas</style></author><author><style face="normal" font="default" size="100%">Stefan Waas</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical measuring technique for small scale water surface waves</style></title><secondary-title><style face="normal" font="default" size="100%">Advanced Optical Instrumentation for Remote Sensing of the Earth&#039;s Surface from Space, SPIE Proceeding 1129, International Congress on Optical Science and Engineering, Paris, 24-28 April 1989</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><pages><style face="normal" font="default" size="100%">147--152</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Physics and chemistry of gas exchange on the ocean surface</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tremmel, H. G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Untersuchungen zur Wiederbelüftung von Neckar und Rhein mit der Konstantflußmethode</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-252</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schnörr, C.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Burkhardt, H.</style></author><author><style face="normal" font="default" size="100%">Höhne, K.H.</style></author><author><style face="normal" font="default" size="100%">Neumann, B.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Zur Schätzung von Geschwindigkeitsvektorfeldern in Bildfolgen mit einer richtungsabhängigen Glattheitsforderung</style></title><secondary-title><style face="normal" font="default" size="100%">Mustererkennung 1989, 11. DAGM-Symposium</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Informatik-Fachberichte</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">1989</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer-Verlag</style></publisher><pub-location><style face="normal" font="default" size="100%">Hamburg</style></pub-location><volume><style face="normal" font="default" size="100%">219</style></volume><pages><style face="normal" font="default" size="100%">294–301</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Billen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung einer LDA-Miniatursonde zur Messung der Strömungsgeschwindigkeit und ihrer Fluktuationen in Wasser</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1988</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-240</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ralf Klein</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung einer Videoaufnahmetechnik und eines Computerbildauswertungsverfahren zur Aufnahme und Auswertung von Blasenverteilungen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1988</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-237</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Stefan Waas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung eines Verfahrens zur Messung kombinierter Höhen- und Neigungsverteilungen von Wasseroberflächenwellen mit Stereoaufnahmen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1988</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-248</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">van Halsema, D.</style></author><author><style face="normal" font="default" size="100%">de Loor, P.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">VIERS-1: A Programme of Wind Scatterometry</style></title><secondary-title><style face="normal" font="default" size="100%">Geoscience and Remote Sensing Symposium, Proc.\ IGARSS&#039;88</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1988</style></year></dates><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">572</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dietmar Wierzimok</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">J. Dengler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. Paulus</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildfolgenanalyse dreidimensionaler turbulenter Strömungen</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 9. DAGM-Symposium zur Mustererkennung 1987</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">149</style></volume><pages><style face="normal" font="default" size="100%">288</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Informatik-Fachberichte 149</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Libner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Die Konstantflußmethode: Ein neuartiges, schnelles und lokales Meßverfahren zur Untersuchung von Austauschvorgängen an der Luft-Wasser Phasengrenze</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/881465941</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Libner</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Erich J. Plate</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ein neue Methode zur lokalen und momentanen Bestimmung der Wiederbelüftungsraten von Gewässern</style></title><secondary-title><style face="normal" font="default" size="100%">Wasserwirtschaft</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">77</style></volume><pages><style face="normal" font="default" size="100%">230--235</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Image sequence analysis of complex physical objects: nonlinear small scale water surface waves</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. of 1st International Conference on Computer Vision</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">191--200</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Gerhard Heinz</style></author><author><style face="normal" font="default" size="100%">Wolfgang Dietrich</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurement of the diffusion coefficients of sparingly soluble gases in water</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><number><style face="normal" font="default" size="100%">C10</style></number><volume><style face="normal" font="default" size="100%">92</style></volume><pages><style face="normal" font="default" size="100%">10,767--10,776</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The diffusion coefficients D of important gas tracers dissolved in water and seawater were measured with a modified Barrer method. The measurements include the gases He, Ne, Kr, Xe, H2, CH4, and CO2 dissolved in distilled water in the temperature range from 5 to 35°C, and He and H2 dissolved in seawater in the same temperature range. The maximum systematic error is estimated to be well below 5%. The isotopic fractionation in the diffusion coefficient was determined to be (0.87 ± 0.05) for 13CO2/12CO2 and (15 ± 3)% for 3He/4He.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">E. Paulus</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Neue Ansätze zur Bildfolgenanalyse</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 9. DAGM-Symposium zur Mustererkennung 1987</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><volume><style face="normal" font="default" size="100%">149</style></volume><pages><style face="normal" font="default" size="100%">287</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Informatik-Fachberichte 149</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">K. O. Münnich</style></author><author><style face="normal" font="default" size="100%">R. Bösinger</style></author><author><style face="normal" font="default" size="100%">A. Dutzi</style></author><author><style face="normal" font="default" size="100%">Werner A. Huber</style></author><author><style face="normal" font="default" size="100%">P. Libner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On the parameters influencing air-water gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><volume><style face="normal" font="default" size="100%">92</style></volume><pages><style face="normal" font="default" size="100%">1937--1950</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Detailed gas exchange measurements from two circular and one linear wind/wave tunnels are presented. Heat, He, CH4, CO2, Kr, and Xe have been used as tracers. The experiments show the central importance of waves for the water-side transfer process. With the onset of waves the Schmidt number dependence of the transfer velocity k changes from k proportional to Sc^-2/3 to k proportional to Sc^-1/2 indicating a change in the boundary conditions at the surface. Moreover, energy put into the wave field by wind is transferred to near-surface turbulence enhancing gas transfer. The data show that the mean square slope of the waves is the best parameter to characterize the free wavy surface with respect to water-side transfer processes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Lifermann</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">A. Ramamonjiarisoa</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Une ètude en soufflerie de la rèflexion des hyperfrèquences par des champs de houles et de vagues</style></title><secondary-title><style face="normal" font="default" size="100%">Oceanologia Acta</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><volume><style face="normal" font="default" size="100%">SP</style></volume><pages><style face="normal" font="default" size="100%">15--22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Werner A. Huber</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zweidimensionale Wellenzahlspektren von Wasseroberflächenwellen: Aufbau eines neuartigen Verfahrens</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1987</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://d-nb.info/881465860</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-229</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Bildfolgenanalyse in der Umweltphysik: Wasseroberflächenwellen und Gasaustausch zwischen Atmosphäre und Gewässern</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. 8. DAGM-Symposium Mustererkennung 1986</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1986</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/18103</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">201--205</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">DAGM award</style></notes><custom3><style face="normal" font="default" size="100%">Informatik-Fachberichte 125</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Broecker, W. S.</style></author><author><style face="normal" font="default" size="100%">Ledwell, J. R.</style></author><author><style face="normal" font="default" size="100%">Takahashi, T.</style></author><author><style face="normal" font="default" size="100%">R. Weiss</style></author><author><style face="normal" font="default" size="100%">L. Merlivat</style></author><author><style face="normal" font="default" size="100%">L. Memery</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">K. O. Münnich</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Isotopic versus micrometeorologic ocean CO$_2$ fluxes: A serious conflict</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1986</style></year></dates><number><style face="normal" font="default" size="100%">C9</style></number><volume><style face="normal" font="default" size="100%">91</style></volume><pages><style face="normal" font="default" size="100%">10517--10528</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Eddy correlation measurements over the ocean give CO2 fluxes an order of magnitude or more larger than expected from mass balance measurements using radiocarbon and radon 222. In particular, Smith and Jones (1985) reported large upward and downward fluxes in a surf zone at supersaturations of 15% and attributed them to the equilibration of bubbles at elevated pressures. They argue that even on the open ocean such bubble injection may create steady state CO2 supersaturations and that inferences of fluxes based on air-sea pCO2 differences and radon exchange velocities must be made with caution. We defend the global average CO2 exchange rate determined by three independent radioisotopic means: prebomb radiocarbon inventories; global surveys of mixed layer radon deficits; and oceanic uptake of bomb-produced radiocarbon. We argue that laboratory and lake data do not lead one to expect fluxes as large as reported from the eddy correlation technique; that the radon method of determining exchange velocities is indeed useful for estimating CO2 fluxes; that supersaturations of CO2 due to bubble injection on the open ocean are negligible; that the hypothesis that Smith and Jones advance cannot account for the fluxes that they report; and that the pC02 values reported by Smith and Jones are likely to be systematically much too high. The CO2 fluxes for the ocean measured to date by the micrometeorological method can be reconciled with neither the observed concentrations of radioisotopes of radon and carbon in the oceans nor the tracer experiments carried out in lakes and in wind/wave tunnels.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gerhard Heinz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung der Diffusionskonstanten von in Wasser gelösten Gasen mit einem modifizierten Barrerverfahren</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1986</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-217</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">R. Bösinger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messungen zur Schmidtzahlabhängigkeit des Gasaustausches</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1986</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-221</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Manfred Maiß</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Modelluntersuchung zum Einfluss von Blasen auf den Gasaustausch zwischen Atmosphäre und Meer</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1986</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-215</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Michael Barabas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufbau und Weiterentwicklung von optischen Verfahren zur Messung von Gasblasen in Wasser; Messungen von Blasendichtespektren in Wind/Wasser-Kanälen in Marseille und Heidelberg</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1985</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-191</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Thomas Wais</style></author><author><style face="normal" font="default" size="100%">L. Memery</style></author><author><style face="normal" font="default" size="100%">G. Caulliez</style></author><author><style face="normal" font="default" size="100%">L. Merlivat</style></author><author><style face="normal" font="default" size="100%">K. O. Münnich</style></author><author><style face="normal" font="default" size="100%">M. Coantic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">He and Rn gas exchange experiments in the large wind-wave facility of IMST</style></title><secondary-title><style face="normal" font="default" size="100%">J. Geophys. Res.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1985</style></year></dates><volume><style face="normal" font="default" size="100%">90</style></volume><pages><style face="normal" font="default" size="100%">11,989--11,998</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In a collaboration between the Laboratoire de Géochimie Isotopique (Centre d&#039;Etudes Nucléaires, Saclay), the Institut de Mécanique Statistique de la Turbulence (IMST, Marseille), and the Institut für Umweltphysik (Heidelberg), for the first time gas exchange experiments have been carried out in the large IMST wind-wave facility. The experiments included simultaneous measurements of Rn and He gas exchange rates, wave slope measurements at four fetches, and bubble measurements. Compared with transfer velocities measured previously in smaller tunnels, our results are considerably lower. This effect can be explained qualitatively by differences in the wave field, which must be taken into account as an important parameter for gas exchange. Wave breaking, starting at 12 m/s wind, was not intense. Consequently, only low bubble densities are obtained, not significantly enhancing gas exchange.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transfer processes across the free water interface</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1985</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ. Heidelberg</style></publisher><volume><style face="normal" font="default" size="100%">Habilitation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">It is the aim of this thesis to discuss the transfer processes across a free gas-liquid interface. After some basic considerations, the state of the art presented in the literature is discussed. The only quantities studied so far are mean parameters like the transfer velocity for mass transfer and the friction velocity as a measure for momentum exchange. The discussion of the data obtained shows that this experimental approach is completely insufficient to explain the effects observed, mainly the large enhancement of mass transfer across the aqueous boundary layer for a free surface if compared to a solid one. Therefore methods providing a deeper insight into the complex transfer mechanisms have been developed. Special emphasize is put on their capabilities and their significance in proving and disproving theoretical concepts: - the study of the Schmidt number dependence of the mass transfer process, in order to obtain the shape of the turbulence increase at the surface - a comparison of the Schmidt number dependence and the velocity profile in the boundary layer, leading to a distinction of multi- and single-stage transport models - the measurement of the local and instantaneous transfer velocity across the boundary layer - a detailed study of water surface waves including the measurement of the phase speed and the coherency - the visualization of the surface waves and - the visualization of the mass transfer across the aqueous boundary layer both providing a direct insight into the two-dimensional structure of the processes. From the results obtained so far a clearer picture of the exchange processes and the turbulent structure at the free, wavy surface can be drawn already: With the onset of the waves at the free surface a new flow regime is established which has no analogue in flow at a solid surface. Eddies with length scales comparable to the dominant waves and closely linked to the wave field are an important feature of this structure. Kitaigorodskii&#039;s concept (1984) that turbulent patches generated by wave instability cause the enhanced gas exchange rates is in agreement with these findings. The development of the new methods promises further progress in understanding near-surface transport processes in the liquid.</style></abstract><work-type><style face="normal" font="default" size="100%">Habilitation thesis</style></work-type><notes><style face="normal" font="default" size="100%">IUP D-200</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Liefermann</style></author><author><style face="normal" font="default" size="100%">A. Ramamonjiarisoa</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Wind tunnel investigation of the characterization of radar backscattering by different wave fields</style></title><secondary-title><style face="normal" font="default" size="100%">Third International Colloquium Spectral Signatures of Objects in Remote Sensing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1985</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://adsabs.harvard.edu/abs/1985ssor.proc..137L</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">137--140</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The motion phases of waves and sea swell that contribute to C and K-band reflection of radar waves were investigated in a wind tunnel designed to simulate air-sea interactions. Results at vertical incidence for various swells, water waves, winds, and combinations of wind and swell are presented. A very important contribution of wave breaking to radar backscattering strength is noted.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Werner A. Huber</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufbau eines gaschromatographischen Messsystems für Gasaustauschmessungen; Windkanalmessungen zur Schmidtzahl- und Wellenbildabhängigkeit des Gasaustausches</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1984</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-178</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">P. Libner</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Entwicklung eines optische Systems zur Erfassung von Wellenparametern bei Feldmessungen im Hinblick auf den Gasaustausch</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1984</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-189</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Schoder, Martin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung der Transfergeschwindigkeit des Gasaustausches durch Blasenoberflächen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1984</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-188</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Thomas Wais</style></author><author><style face="normal" font="default" size="100%">Michael Barabas</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Brutsaert</style></author><author><style face="normal" font="default" size="100%">G. H. Jirka</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">A new optical bubble measuring device; a simple model for bubble contribution to gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Gas transfer at water surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1984</style></year></dates><publisher><style face="normal" font="default" size="100%">Reidel</style></publisher><pages><style face="normal" font="default" size="100%">237--246</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">K. H. Fischer</style></author><author><style face="normal" font="default" size="100%">Johann Ilmberger</style></author><author><style face="normal" font="default" size="100%">P. Libner</style></author><author><style face="normal" font="default" size="100%">W. Weiss</style></author><author><style face="normal" font="default" size="100%">D. Imboden</style></author><author><style face="normal" font="default" size="100%">U. Lemnin</style></author><author><style face="normal" font="default" size="100%">J. M. Jaquet</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Brutsaert</style></author><author><style face="normal" font="default" size="100%">G. H. Jirka</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Parameterization of air/lake gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Gas transfer at water surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1984</style></year></dates><publisher><style face="normal" font="default" size="100%">Reidel</style></publisher><pages><style face="normal" font="default" size="100%">469--476</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">A. Dutzi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Untersuchungen zum Einfluss der Temperatur auf den Gasaustausch</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1984</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-190</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Werner A. Huber</style></author><author><style face="normal" font="default" size="100%">A. Dutzi</style></author><author><style face="normal" font="default" size="100%">Thomas Wais</style></author><author><style face="normal" font="default" size="100%">Johann Ilmberger</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">W. Brutsaert</style></author><author><style face="normal" font="default" size="100%">G. H. Jirka</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Wind/wave-tunnel experiments on the Schmidt number and wave field dependence of air-water gas exchange</style></title><secondary-title><style face="normal" font="default" size="100%">Gas transfer at water surfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1984</style></year></dates><publisher><style face="normal" font="default" size="100%">Reidel</style></publisher><pages><style face="normal" font="default" size="100%">303--309</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Thomas Wais</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufbau eines optischen Verfahrens zur Messung von Gasblasen in Wasser; Einfluss von Gasblasen auf den Gasaustausch</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1983</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-177</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolfgang Dietrich</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufbau und Erprobung eines neuartigen Diaphragmaverfahrens zur Messung der Diffusionskonstanten von in Wasser gelösten Gasen</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1983</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-176</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Messung des Gasaustausches und der Turbulenz an der Oberfläche durch Sichtbarmachung der Grenzschicht</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1983</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Universität Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optical water waves measuring techniques</style></title><secondary-title><style face="normal" font="default" size="100%">Talk, 1st International Symposium on Gas Transfer at Water Surfaces, Cornell University, Ithaca, New York, June 13--15, 1983</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1983</style></year></dates><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lange, P. A.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">Tschiersch, J.</style></author><author><style face="normal" font="default" size="100%">Johann Ilmberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison between an amplitude-measuring wire and a slope-measuring laser water wave gauge</style></title><secondary-title><style face="normal" font="default" size="100%">Rev. Sci. Instrum.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1982</style></year></dates><number><style face="normal" font="default" size="100%">5</style></number><volume><style face="normal" font="default" size="100%">53</style></volume><pages><style face="normal" font="default" size="100%">651--655</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Capillary waves produced in a laboratory wind wave tunnel have been measured using a wire resistance-type gauge (measuring wave amplitude) and a laser gauge (measuring wave slope). Comparison of power spectra of the gauges shows good agreement to 80 Hz, which is the upper frequency limit of the wire gauge. The upper frequency limit of the laser gauge depends upon laser beam diameter and is about 300 Hz.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Flothmann, D.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Trockene Deposition von Gasen über Wasser (Gasaustausch)</style></title><secondary-title><style face="normal" font="default" size="100%">Austausch von Luftverunreinigungen an der Grenzfläche Atmospäre/Erdoberfläche, Zwischenbericht für das Umweltbundesamt zum Teilprojekt 1: Deposition von Gasen, BleV-R-64.284-2</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1982</style></year></dates><publisher><style face="normal" font="default" size="100%">Battelle Institut</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Wolf, Günther</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Aufbau einer Pilotanlage zur gaschromatographischen Tritiumanreicherung</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1981</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Klaus Bönisch</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gasaustausch und Wärmetransfer bei freier Konvektion und unter Einfluss von Wellen: Aufbau einer Apparatur und erste Ergebnisse</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1981</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-204</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Johann Ilmberger</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Impulsübertrag und Strömungsverhältnisse in einem runden Wind-Wasser Kanal</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1981</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-167</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tschiersch, J.</style></author><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">H. C. Broecker</style></author><author><style face="normal" font="default" size="100%">L. Hasse</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Gas exchange trough a rough water surface in a circular windtunnel; wave characteristics under limited and unlimited fetch</style></title><secondary-title><style face="normal" font="default" size="100%">Berichte aus dem Sonderforschungsbereich 94 Meeresforschung - Symposium on Capillary Waves and Gas Exchange, Trier July 2--6, 1979</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1980</style></year></dates><number><style face="normal" font="default" size="100%">17</style></number><publisher><style face="normal" font="default" size="100%">Univ. Hamburg</style></publisher><pages><style face="normal" font="default" size="100%">63--70</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">U. Siegenthaler</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">H. C. Broecker</style></author><author><style face="normal" font="default" size="100%">L. Hasse</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">The influence of surface tension on gas exchange: measurements of gas exchange with alcohol/water mixtures in a circular wind-water tunnel</style></title><secondary-title><style face="normal" font="default" size="100%">Berichte aus dem Sonderforschungsbereich 94 Meeresforschung - Symposium on Capillary Waves and Gas Exchange, Trier July 2--6, 1979</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1980</style></year></dates><number><style face="normal" font="default" size="100%">17</style></number><publisher><style face="normal" font="default" size="100%">Univ. Hamburg</style></publisher><pages><style face="normal" font="default" size="100%">103--108</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">K. O. Münnich</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">H. C. Broecker</style></author><author><style face="normal" font="default" size="100%">L. Hasse</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Momentum induced gas exchange through a smooth water surface, models and experimental results from linear and circular wind-water tunnels</style></title><secondary-title><style face="normal" font="default" size="100%">Berichte aus dem Sonderforschungsbereich 94 Meeresforschung - Symposium on Capillary Waves and Gas Exchange, Trier July 2--6, 1979</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1980</style></year></dates><number><style face="normal" font="default" size="100%">17</style></number><publisher><style face="normal" font="default" size="100%">Univ. Hamburg</style></publisher><pages><style face="normal" font="default" size="100%">55--62</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Tschiersch, Jochen</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optische Messung von Kapillarwellen im Hinblick auf den Gasaustausch</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1980</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-151</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Fritz Weißer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Verdunstungsmessungen in einem ringförmigen Wind-Wasser-Kanal mit Hilfe von Psychochrometern und einem WLD-System</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1980</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">IUP D-159</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zur Parametrisierung des Gasaustauschs mit Hilfe von Laborexperimenten</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1980</style></year></dates><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik</style></publisher><volume><style face="normal" font="default" size="100%">Dissertation</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In der folgenden Arbeit werden in Laborexperimenten die Mechanismen des Gasaustausches zwischen Atmosphäre und Meer untersucht. Dazu wurde erstmals ein ringförmiger Wind-Wasser-Kanal eingesetzt, der gegenüber linearen eine Reihe von Vorteilen aufweist. Der Einfluß der Zentrifugalkräfte auf die Austauschprozesse erwies sich als gering. Gemessen wurden neben der Gasaustauschrate für CO2 der Transfer von Wärme in Wasser, die Verdunstungsrate, die Schubspannungsgeschwindigkeit und mit Hilfe einer optischen Methode Neigungsspektren und mittlere Neigungen der Wasserwellen. Im glatten Fall entspricht die viskose Grenzschicht beiderseits der Wasseroberfläche völlig der an einer festen Wand. Das bestätigen die Experimente sowohl durch die absoluten Raten als auch durch die Schmidtzahlabhängigkeit des Gasaustausches von Sc^(-2/3), die unmittelbar aus Kontinuitätsüberlegungen resultiert. Mit dem Auftreten von Kapillarwellen steigen die Transfergeschwindigkeiten der wasserseitig kontrollierten Austauschprozesse stark an. Die Erhöhung des Gasaustausches ist besonders groß, da sich gleichzeitig die Schrnidtzahlabhängigkeit auf Sc^(-1/2) ändert. Zur Erklärung des hohen Anstiegs reichen die bisherigen theoretischen Vorstellungen eine Grenzschichtdickenvariation durch Kapillarwellen nicht aus. Die Änderung der Schmidtzahlabhängigkeit des Gasaustausches deutet vielmehr an, daß durch das Wellenfeld sich ein neuer Mechanismus des turbulenten Transports einstellt. Zur Parametrisierung des Einflusses der Wellen erscheint die mittlere quadratische Neigung der Wellen als geeignete Größe. In Übereinstimmung mit Laborexperimenten an linearen Wind-Wasser-Kanälen entfaltet sich der Einfluß der Kapillarwellen in einem Schubspannungsgeschwindigkeitsbereich von u = 10-30 cm/sec (U10 = 3-8 m/sec). In diesem Bereich steigt der Gasaustausch mit u_*^2 bis u_*^3 an. Bei höheren Geschwindigkeiten ist der Gasaustausch proportional zu u_*. Das Zusammenwirken von turbulentem Transport und chemischer Reaktion läßt sich mit genügender Genauigkeit mit dem tau - Modell berechnen.</style></abstract><notes><style face="normal" font="default" size="100%">IUP D-145, Link Nationalbibliothek http://d-nb.info/810123614</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author><author><style face="normal" font="default" size="100%">K. O. Münnich</style></author><author><style face="normal" font="default" size="100%">U. Siegenthaler</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Measurements of gas exchange and momentum transfer in a circular wind-water tunnel</style></title><secondary-title><style face="normal" font="default" size="100%">Tellus</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1979</style></year></dates><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">321--329</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bernd Jähne</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Gaschromatische Tritiumanreicherung, Trennung der Wasserstoffisotope bei Adsorption</style></title></titles><dates><year><style  face="normal" font="default" size="100%">1977</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ub.uni-heidelberg.de/archiv/16797</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Die vorliegende Arbeit untersucht die Möglichkeiten gaschromatographischer Anreicherung von Tritium zur Messung von Low-Level-Proben. Es konnte ein trägergasfreies Verfahren entwickelt werden, das aus einer Kombination von Frontalanalyse und Verdrängungsentwicklung besteht. Es ist einfach zu handhaben, aber genauso effektiv wie kompliziertere bisherige Verfahren mit Trägergas (Kapitel 2). Verbesserungen im Kolonnenbau machen größere Anreicherungsfaktoren möglich. Durch eine optimalere Wahl der Adsorber und den Übergang zu tieferen Temperaturen (63,2°K, Sieden von flüssigem Stickstoff unter vermindertem Druck) läßt sich die vierfache Menge an Wasserstoff im gleichen Volumen anreichern wie mit den bisherigen gaschromatographischenVerfahren. Das gaschromatographische Verfahren ist gegenüber dem Trennrohr schneller und platzsparender: eine 20 Nl Wasserstoffprobe läßt sich in weniger als 2 Stunden in einer 0,3 1 Kolonne bei 63,2°K auf 1,3 Nl mit mehr als 99,0% Tritiumausbeute einengen. Auch größere Mengen H2 lassen sich verarbeiten, sodaß der bisherige Anreicherungsweg für Low-Level-Tritiumproben weiter vereinfacht werden kann (Kapitel 7). Breiten Raum nehmen grundlegende Untersuchungen ein, die erst eine optimale Parameterwahl ermöglicht haben. Im Kapitel 4 werden Grundlagen der Adsorption beschrieben und die Adsorber auf ihre Adsorptionskapazitäten verglichen, die Theorie der Adsorption selbst findet sich in Anhang A2, die Meßverfahren in Anhang A1. Die Untersuchung der Trennfaktoren der Adsorption ist Gegenstand von Kapitel 5. Da für HT geeignete Trennfaktoren in der Literatur fehlen, wurden sowohl eigene Messungen unternommen, als auch versucht mit 3 Adsorptionsmodellen die Beziehung der Trennfaktoren untereinander (logarithmische Verhältnisse, Bigeleisenfaktoren) theoretisch zu berechnen (Kapitel 5, Anhang A3). Dabei ergab sich, daß Isotopentrennfaktoren einschließlich der Ortho-Para-Trennung (Kapitel 3) ein geeignetes Mittel sind, zwischen verschiedenen Vorstellungen über die Adsorption zu unterscheiden, was mit Isothermenmessungen nur schwer möglich ist (Anhang A2). Das Modell einer mobilen Adsorption der H -Moleküle mit einer weitgehenden Störung der Rotation in einer Ebene senkrecht zur Oberfläche entspricht den Messergebnissen am besten.</style></abstract><notes><style face="normal" font="default" size="100%">IUP D-100</style></notes></record></records></xml>