@article {7032,
	title = {A Digital 3D Reference Atlas Reveals Cellular Growth Patterns Shaping the Arabidopsis Ovule},
	journal = {eLife},
	year = {2021},
	doi = {10.7554/eLife.63262},
	author = {Vijayan, A. and Tofanelli, R and Strauss, S and Cerrone, L and Wolny, A and Strohmeier, J and Kreshuk, A and Fred A. Hamprecht and Smith, R S and Schneitz, K}
}
@unpublished {7061,
	title = {Evidential Turing Processes},
	number = {https://arxiv.org/abs/2106.01216},
	year = {2021},
	publisher = {arXiv preprint},
	url = {https://arxiv.org/abs/2106.01216},
	author = {Kandemir, M and Agk{\"u}l, A and Hau{\ss}mann, M and {\"U}nal, G}
}
@article {7086,
	title = {Integration of Cell Growth and Asymmetric Division during Lateral Root Initiation in Arabidopsis thaliana},
	journal = {Plant and Cell Physiology},
	volume = {62},
	year = {2021},
	pages = {1269-1279},
	doi = {10.1093/pcp/pcab038},
	author = {Sch{\"u}tz, LM and Louveaux, M and Vilches-Barro, A and Bouziri, S and Cerrone, L and Wolny, A and Kreshuk, A and Hamprecht, FA and Maizel, A}
}
@proceedings {7060,
	title = {Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes},
	volume = {PMLR 130},
	year = {2021},
	pages = {478-486},
	author = {Manuel Hau{\ss}mann and Gerwinn, S and Look, A and Rakitsch, B and Kandemir, M}
}
@article {7022,
	title = {Microscopy-based assay for semi-quantitative detection of SARS-CoV-2 specific antibodies in human sera},
	journal = {BioEssays},
	volume = {43},
	year = {2021},
	doi = {10.1002/bies.202000257},
	author = {Pape, C and Remme, R and Wolny, A and Olberg, S and Wolf, S and Cerrone, L and Cortese, M and Klaus, S and Lucic, B and Ullrich, S and Anders-{\"O}sswein, M and Wolf, S and Cerikan, B and Neufeldt, C J and Ganter, M and Schnitzler, P and Merle, U and Lusic, M and Boulant, S and Stanifer, M and Bartenschlager, R and Hamprecht, F A and Kreshuk, A and Tischer, C and Kr{\"a}usslich, H.-G. and M{\"u}ller, B and Laketa, V}
}
@proceedings {7041,
	title = {Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes},
	year = {2021},
	abstract = {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.},
	url = {https://compvis.github.io/brushstroke-parameterized-style-transfer/},
	author = {Dmytro Kotovenko and Matthias Wright and Arthur Heimbrecht and Bj{\"o}rn Ommer}
}
@proceedings {7031,
	title = {Shape or Texture: Understanding Discriminative Features in CNNs},
	year = {2021},
	author = {Md Amirul Islam and Matthew Kowal and Patrick Esser and Sen Jia and Bj{\"o}rn Ommer and Konstantinos G Derpanis and Neil Bruce}
}
@article {7090,
	title = {Accurate and Versatile 3D Segmentation of Plant Tissues at Cellular Resolution},
	journal = {eLife},
	volume = {9},
	year = {2020},
	doi = {10.7554/eLife.57613},
	author = {Wolny, A and Cerrone, L and Vijayan, A and Tofanelli, R and Vilches-Barro, A and Louveaux, M and Wenzel, C and Strauss, S and Wilson-Sanchez, D and Lymbouridou, R and Steigleder, SS and Pape, C and Bailoni, A and Duran-Nebreda, S and Bassel, GW and Lohmann, JU and Tsiantis, M and Hamprecht, FA and Schneitz, K and Maizel, A and Kreshuk, A}
}
@article {Krull2020,
	title = {Artificial-intelligence-driven scanning probe microscopy},
	journal = {Communications Physics},
	volume = {3},
	year = {2020},
	abstract = {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.},
	issn = {23993650},
	doi = {10.1038/s42005-020-0317-3},
	author = {Krull, A. and Hirsch, P. and Rother, C. and Schiffrin, A. and Krull, C.}
}
@conference {Radev2020,
	title = {BayesFlow: Learning complex stochastic models with invertible neural networks},
	year = {2020},
	abstract = {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.},
	url = {http://arxiv.org/abs/2003.06281},
	author = {Radev, Stefan T. and Mertens, Ulf K. and Voss, Andreass and Lynton Ardizzone and K{\"o}the, Ullrich}
}
@proceedings {7059,
	title = {Bayesian Evidential Deep Learning with PAC Regularization },
	year = {2020},
	author = {Manuel Hau{\ss}mann and Gerwinn, S and Kandemir, M}
}
@conference {Kamann2019,
	title = {Benchmarking the Robustness of Semantic Segmentation Models},
	booktitle = {CVPR 2020},
	year = {2020},
	abstract = {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.},
	url = {http://arxiv.org/abs/1908.05005},
	author = {Kamann, Christoph and Rother, Carsten}
}
@conference {Kluger2020,
	title = {CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus},
	booktitle = {CVPR 2020},
	year = {2020},
	month = {01/2020},
	abstract = {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.},
	url = {http://arxiv.org/abs/2001.02643},
	author = {Kluger, Florian and Brachmann, Eric and Ackermann, Hanno and Carsten Rother and Yang, Michael Ying and Rosenhahn, Bodo}
}
@article {7029,
	title = {Deep learning of cuneiform sign detection with weak supervision using transliteration alignment},
	journal = {PLoS ONE},
	volume = {15},
	year = {2020},
	month = {12/2020},
	type = {Journal},
	chapter = {1-21},
	abstract = {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.},
	keywords = {cuneiform script, deep learning, sign detection, Weakly supervised learning},
	doi = {https://doi.org/10.1371/journal.pone.0243039},
	url = {https://hci.iwr.uni-heidelberg.de/compvis/projects/cuneiform},
	author = {Tobias Dencker and Pablo Klinkisch and Stefan M Maul and Bj{\"o}rn Ommer}
}
@article {6319,
	title = {Deep-Learning Jets with Uncertainties and More},
	journal = {SciPost Phys},
	volume = {8},
	year = {2020},
	doi = {10.21468/SciPostPhys.8.1.006 },
	url = {https://scipost.org/10.21468/SciPostPhys.8.1.006},
	author = {Bollweg, S and Manuel Hau{\ss}mann and Kasieczka, G and Luchmann, M and Plehn, T and Thompson, J}
}
@proceedings {7089,
	title = {DISCo: Deep Learning, Instance Segmentation, and Correlations for Cell Segmentation in Calcium Imaging},
	year = {2020},
	pages = {151-162},
	author = {Kirschbaum, E and Bailoni, A and Hamprecht, FA}
}
@conference {Sorrenson2020,
	title = {Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)},
	booktitle = {Intl. Conf. Learning Representations (ICLR)},
	year = {2020},
	abstract = {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.},
	url = {http://arxiv.org/abs/2001.04872},
	author = {Sorrenson, Peter and Rother, Carsten and K{\"o}the, Ullrich}
}
@article {6333,
	title = {End-to-End Learning of Decision Trees and Forests},
	journal = {International Journal of Computer Vision},
	volume = {128},
	year = {2020},
	pages = {997-1011},
	doi = {10.1007/s11263-019-01237-6},
	author = {Hehn, TM and Kooij, J F P and Fred A. Hamprecht}
}
@article {Ardizzone2020,
	title = {Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling},
	year = {2020},
	month = {jan},
	abstract = {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.},
	url = {http://arxiv.org/abs/2001.06448},
	author = {Lynton Ardizzone and Mackowiak, Radek and Carsten Rother and Ullrich K{\"o}the}
}
@conference {6922,
	title = {Mind the Gap {\textendash} A Benchmark for Dense Depth Prediction beyond Lidar},
	booktitle = {2nd Workshop on Safe Artificial Intelligence for Automated Driving, in conjunction with CVPR 2020},
	year = {2020},
	author = {H. Schilling and M. Gutsche and A. Brock and D. Sp{\"a}th and C. Rother and K. Krispin}
}
@article {Wolf2020,
	title = {The Mutex Watershed and its Objective: Efficient, Parameter-Free Graph Partitioning},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {43},
	year = {2020},
	pages = {3724-3738},
	abstract = {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 "Mutex Watershed". 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.},
	doi = {10.1109/tpami.2020.2980827},
	author = {Wolf, Steffen and Bailoni, Alberto and Pape, Constantin and Rahaman, Nasim and Kreshuk, Anna and K{\"o}the, Ullrich and Hamprecht, Fred A.}
}
@proceedings {7005,
	title = {Proposal-Free Volumetric Instance Segmentation from Latent Single-Instance Masks},
	volume = {12544},
	year = {2020},
	pages = {331-344},
	publisher = {Springer},
	edition = {LNCS},
	doi = {10.1007/978-3-030-71278-5_24},
	author = {Bailoni, A and Pape, C and Wolf, S and Kreshuk, A. and Hamprecht, F A}
}
@conference {Cvpr2020,
	title = {Self-Supervised Viewpoint Learning From Image Collections},
	booktitle = {CONSAC},
	year = {2020},
	abstract = {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 .},
	url = {https://github.com/NVlabs/SSV},
	author = {S.K. Mustikovela and V. Jampani and S. De Mello and S. Liu and U. Iqbal and C. Rother and J. Kautz}
}
@proceedings {6990,
	title = {The Semantic Mutex Watershed for Efficient Bottom-Up Semantic Instance Segmentation},
	year = {2020},
	pages = {208-224},
	doi = {doi.org/10.1007/978-3-030-58539-6_13},
	author = {Wolf, S and Li, Y and Pape, C and Bailoni, A and Kreshuk, A and Hamprecht, F A}
}
@article {krall2019b,
	title = {Air{\textendash}sea gas exchange at wind speeds up to 85 m/s},
	journal = {Ocean Science},
	volume = {15},
	year = {2019},
	pages = {1783-{\textendash}1799},
	doi = {10.5194/os-15-1783-2019},
	author = {Kerstin E. Krall and Andrew W. Smith and Naohisa Takagaki and Bernd J{\"a}hne}
}
@article {6320,
	title = {Bayesian Prior Networks with PAC Training},
	journal = {arXiv preprint arXiv:1906.00816},
	year = {2019},
	author = {Manuel Hau{\ss}mann and Gerwinn, S and Kandemir, M.}
}
@techreport {Kruse2019,
	title = {Benchmarking Invertible Architectures on Inverse Problems},
	number = {i},
	year = {2019},
	abstract = {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.},
	author = {Kruse, Jakob and Lynton Ardizzone and Carsten Rother and Ullrich K{\"o}the}
}
@article {Kamann2019,
	title = {Benchmarking the Robustness of Semantic Segmentation Models},
	year = {2019},
	abstract = {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.},
	url = {http://arxiv.org/abs/1908.05005},
	author = {Kamann, Christoph and Carsten Rother}
}
@article {6290,
	title = {Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models, in press},
	journal = {Physics in Medicine and Biology},
	volume = {64},
	year = {2019},
	doi = {http://iopscience.iop.org/article/10.1088/1361-6560/aafce7},
	author = {Bendinger, AL and Debus, C and Glowa, C and Karger, CP and Peter, J and Martin Storath}
}
@article {Kleesiek2019,
	title = {Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?: A Feasibility Study},
	journal = {Investigative Radiology},
	volume = {54},
	year = {2019},
	pages = {653{\textendash}660},
	abstract = {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 {\textpm} 0.019, a peak signal-to-noise ratio of 22.967 {\textpm} 1.162 dB, and a structural similarity index of 0.872 {\textpm} 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 "features" 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.},
	keywords = {Bayesian uncertainty, deep learning, gadolinium-based contrast agents, glioma, multiparametric MRI},
	issn = {15360210},
	doi = {10.1097/RLI.0000000000000583},
	author = {Kleesiek, Jens and Morshuis, Jan Nikolas and Isensee, Fabian and Deike-Hofmann, Katerina and Paech, Daniel and Kickingereder, Philipp and K{\"o}the, Ullrich and Rother, Carsten and Forsting, Michael and Wick, Wolfgang and Bendszus, Martin and Schlemmer, Heinz Peter and Radbruch, Alexander}
}
@conference {6322,
	title = {Content and Style Disentanglement for Artistic Style Transfer},
	booktitle = {Proceedings of the Intl. Conf. on Computer Vision (ICCV)},
	year = {2019},
	author = {Dmytro Kotovenko and Sanakoyeu, Artsiom and Sabine Lang and Bj{\"o}rn Ommer}
}
@proceedings {6318,
	title = {Deep Active Learning with Adaptive Acquisition},
	year = {2019},
	pages = {2470-2476},
	doi = { 10.24963/ijcai.2019/343},
	author = {Manuel Hau{\ss}mann and Fred A. Hamprecht and Kandemir, M.}
}
@article {6335,
	title = {An efficient algorithm for the piecewise affine-linear Mumford-Shah model based on a Taylor jet splitting},
	journal = {IEEE Transactions on Image Processing},
	volume = {29},
	year = {2019},
	chapter = {921-933},
	doi = {10.1109/TIP.2019.2937040},
	author = {Kiefer, L and Martin Storath and Weinmann, A}
}
@article {7083,
	title = {Experimental and computational analyses reveal that environmental restrictions shape HIV-1 spread in 3D cultures},
	journal = {Nature Communications},
	volume = {13;10(1)},
	year = {2019},
	doi = {10.1038/s41467-019-09879-3},
	author = {Imle, A and Kumberger, P and Schnellb{\"a}cher, ND and Fehr, J and Carillo-Bustamente, P and Ales, J and Schmidt, P and Ritter, C and Godinez, WJ and M{\"u}ller, B and Rohr, K and Hamprecht, FA and Schwarz, US and Graw, F and Fackler, OT}
}
@article {kirchhoefer2019,
	title = {Extended noise equalisation for image compression in microscopical applications},
	journal = {tm - Technisches Messen},
	volume = {86},
	year = {2019},
	pages = {422{\textendash}432},
	doi = {10.1515/teme-2019-0022},
	author = {Daniel M. Kirchh{\"o}fer and Gerhard A. Holst and Fred S. Wouters and Stephan Hock and Bernd J{\"a}hne}
}
@phdthesis {klein2019,
	title = {The Fetch Dependency of Small-Scale Air-Sea Interaction Processes at Low to Moderate Wind Speeds},
	volume = {Dissertation},
	year = {2019},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ. Heidelberg},
	type = {phdDissertation},
	address = {Heidelberg},
	doi = {10.11588/heidok.00026559},
	author = {Angelika Klein}
}
@article {Kostrykin:2019aa,
	title = {Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information},
	journal = {Medical Image Analysis},
	year = {2019},
	url = {https://doi.org/10.1016/j.media.2019.101536},
	author = {Kostrykin, L. and Schn{\"o}rr, C. and Rohr, K.}
}
@article {Ardizzone2019a,
	title = {Guided Image Generation with Conditional Invertible Neural Networks},
	year = {2019},
	abstract = {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.},
	url = {http://arxiv.org/abs/1907.02392},
	author = {Lynton Ardizzone and Carsten L{\"u}th and Kruse, Jakob and Carsten Rother and Ullrich K{\"o}the}
}
@article {Ardizzone2019,
	title = {Guided Image Generation with Conditional Invertible Neural Networks},
	year = {2019},
	abstract = {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.},
	url = {http://arxiv.org/abs/1907.02392},
	author = {Lynton Ardizzone and Carsten L{\"u}th and Kruse, Jakob and Carsten Rother and Ullrich K{\"o}the}
}
@article {6334,
	title = {ilastik: interactive machine learning for (bio)image analysis},
	journal = {Nature Methods},
	volume = {16},
	year = {2019},
	pages = {1226-1232},
	doi = {10.1038/s41592-019-0582-9},
	author = {Stuart Berg and Kutra, D and Kroeger, T and Christoph N. Straehle and Bernhard X. Kausler and Haubold, C. and Schiegg, M and Ales, J and Thorsten Beier and Rudy, M and Eren, K and Cervantes, JI and Xu, B and Beuttenm{\"u}ller, F and Wolny, A and Zhang, C and Ullrich K{\"o}the and Fred A. Hamprecht and Kreshuk, A}
}
@proceedings {6288,
	title = {LeMoNADe: Learned Motif and Neuronal Assembly Detection in calcium imaging videos},
	year = {2019},
	author = {Kirschbaum, E. and Manuel Hau{\ss}mann and Wolf, S and Sonntag, H and Schneider, J and Elzoheiry, S and Kann, O and Durstewitz, D and Fred A. Hamprecht}
}
@article {nagel2019,
	title = {Measurement of air-sea gas transfer velocities in the Baltic Sea},
	journal = {Ocean Science},
	volume = {15},
	year = {2019},
	pages = {235{\textendash}247},
	doi = {10.5194/os-15-235-2019},
	author = {Leila Nagel and Kerstin E. Krall and Bernd J{\"a}hne}
}
@article {6315,
	title = {A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms},
	journal = {arXiv preprint arXiv:1901.10912},
	year = {2019},
	author = {Bengio, Y and Deleu, T and Rahaman, N and Ke, R and Lachapelle, S and Bilaniuk, O and Goyal, A and Pal, C}
}
@phdthesis {6340,
	title = {Novel Machine Learning Approaches for Neurophysiological Data Analysis},
	year = {2019},
	publisher = {Heidelberg University},
	author = {Kirschbaum, E.}
}
@conference {Adler2019,
	title = {Out of Distribution Detection for Intra-operative Functional Imaging},
	booktitle = {MICCAI UNSURE Workshop 2019},
	volume = {11840 LNCS},
	year = {2019},
	pages = {75{\textendash}82},
	abstract = {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.},
	issn = {16113349},
	doi = {10.1007/978-3-030-32689-0_8},
	author = {Adler, Tim J and Ayala, Leonardo and Lynton Ardizzone and Kenngott, Hannes G and Vemuri, Anant and M{\"u}ller-Stich, Beat P and Carsten Rother and Ullrich K{\"o}the and Maier-Hein, Lena}
}
@proceedings {6317,
	title = {Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation},
	year = {2019},
	pages = {563-573},
	author = {Manuel Hau{\ss}mann and Fred A. Hamprecht and Kandemir, M.}
}
@article {6336,
	title = {Smoothing for signals with discontinuities using higher order Mumford-Shah models},
	journal = {Numerische Mathematik},
	volume = {143(2)},
	year = {2019},
	pages = {423-460},
	doi = {10.1007/s00211-019-01052-8},
	author = {Martin Storath and Kiefer, L and Weinmann, A}
}
@proceedings {6300,
	title = {Using a Transformation Content Block For Image Style Transfer},
	year = {2019},
	author = {Dmytro Kotovenko and Sanakoyeu, A. and Sabine Lang and Ma, P. and Bj{\"o}rn Ommer}
}
@conference {Hodan2018,
	title = {BOP: Benchmark for 6D object pose estimation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11214 LNCS},
	year = {2018},
	month = {aug},
	pages = {19{\textendash}35},
	abstract = {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.},
	isbn = {9783030012489},
	issn = {16113349},
	doi = {10.1007/978-3-030-01249-6_2},
	url = {http://arxiv.org/abs/1808.08319},
	author = {Hoda{\v n}, Tom{\'a}{\v s} and Michel, Frank and Brachmann, Eric and Kehl, Wadim and Buch, Anders Glent and Kraft, Dirk and Drost, Bertram and Vidal, Joel and Ihrke, Stephan and Zabulis, Xenophon and Sahin, Caner and Manhardt, Fabian and Tombari, Federico and Kim, Tae Kyun and Matas, Ji{\v r}{\'\i} and Carsten Rother}
}
@article {Arnab2018,
	title = {Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation},
	journal = {Cvpr},
	volume = {XX},
	number = {Xx},
	year = {2018},
	pages = {1{\textendash}15},
	abstract = {{\textemdash}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.},
	keywords = {conditional random fields, deep learning, seman-},
	url = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889\&rep=rep1\&type=pdf\%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050},
	author = {Arnab, Anurag and Zheng, Shuai and Jayasumana, Sadeep and Romera-paredes, Bernardino and Kirillov, Alexander and Savchynskyy, Bogdan and Carsten Rother and Kahl, Fredrik and Torr, Philip}
}
@article {6254,
	title = {Early reduced behavioral activity induced by large strokes affects the efficiency of enriched environment in rats},
	journal = {Sage Journals},
	volume = { Journal of Cerebral Blood Flow \& Metabolism},
	year = {2018},
	month = {05/18},
	abstract = {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{\textendash}2 weeks as a physiological response paralleled by signs of systemic inflammation. Rats were exposed early (1{\textendash}2 weeks) or late (3{\textendash}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.},
	url = {http://journals.sagepub.com/doi/abs/10.1177/0271678X18777661},
	author = {Anna-Sophia Wahl and Erlebach, E. and Biagio Brattoli and Uta B{\"u}chler and Kaiser, J. and Ineichen, V. B. and Alice C. Mosberger and Schneeberger, S. and Imobersteg, S. and Wieckhorst, M. and Stirn, M. and Schroeter, A. and Bj{\"o}rn Ommer and M. E. Schwab}
}
@article {kunz2018,
	title = {Investigating small scale air-sea exchange processes via thermography},
	journal = {Front. Mech. Eng.},
	volume = {26},
	year = {2018},
	doi = {10.3389/fmech.2018.00004},
	author = {Jakob Kunz and Bernd J{\"a}hne}
}
@article {erb2017mathematical,
	title = {Mathematical Analysis of the 1D Model and Reconstruction Schemes for Magnetic Particle Imaging},
	journal = {Inverse Problems},
	volume = {34},
	year = {2018},
	author = {Erb, Wolfgang and Andreas Weinmann and Ahlborg, Mandy and Christina Brandt and Bringout, Gael and Buzug, Thorsten M and Frikel, J{\"u}rgen and Kaethner, Christian and Tobias Knopp and M{\"a}rz, Thomas and M{\"o}ddel, Martin and Martin Storath and Alexander Weber}
}
@article {6221,
	title = {Model-based learning of local image features for unsupervised texture segmentation},
	journal = {IEEE Transactions on Image Processing},
	volume = {27},
	year = {2018},
	pages = {1994-2007},
	author = {Martin Kiechle and Martin Storath and Andreas Weinmann and Martin Kleinsteuber}
}
@proceedings {6275,
	title = {The Mutex Watershed: Efficient, Parameter-Free Image   Partitioning},
	year = {2018},
	pages = {571-587},
	publisher = {Springer},
	doi = {10.1007/978-3-030-01225-0_34},
	author = {Wolf, S and Pape, C and Bailoni, A and Rahaman, N and Kreshuk, A. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Wolf2018,
	title = {The Mutex Watershed: Efficient, Parameter-Free Image Partitioning},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11208 LNCS},
	year = {2018},
	month = {apr},
	pages = {571{\textendash}587},
	abstract = {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 {\textquotedblleft}Mutex Watershed{\textquotedblright}, 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.},
	isbn = {9783030012243},
	issn = {16113349},
	doi = {10.1007/978-3-030-01225-0_34},
	url = {http://arxiv.org/abs/1904.12654},
	author = {Wolf, Steffen and Pape, Constantin and Bailoni, Alberto and Rahaman, Nasim and Kreshuk, Anna and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Kostrykin:2018aa,
	title = {Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming},
	booktitle = {Proc. ISBI},
	year = {2018},
	author = {Kostrykin, L. and Schn{\"o}rr, C. and Rohr, K.}
}
@mastersthesis {6245,
	title = {Semantic Segmentation of Urban Scenes Using Deep Learning},
	year = {2018},
	school = {Heidelberg University},
	author = {Kawetzki, D}
}
@conference {style_aware_content_loss_eccv18,
	title = {A Style-Aware Content Loss for Real-time HD Style Transfer},
	booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) (Oral)},
	year = {2018},
	abstract = {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.
},
	keywords = {deep learning, generative network, Style transfer},
	author = {Sanakoyeu, A. and Dmytro Kotovenko and Sabine Lang and Bj{\"o}rn Ommer}
}
@article {6180,
	title = {Active machine learning for training an event classification},
	journal = {Patent, Patent Number WO2017032775 A1},
	year = {2017},
	author = {Kandemir, M. and Fred A. Hamprecht and Wojek, C. and Schmidt, U.}
}
@phdthesis {kunz2017,
	title = {Active Thermography as a Tool for the Estimation of Air-Water Transfer Velocities},
	volume = {Dissertation},
	year = {2017},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ. Heidelberg},
	type = {phd},
	doi = {10.11588/heidok.00022903},
	author = {Jakob Kunz}
}
@conference {Jafari2017,
	title = {Analyzing modular CNN architectures for joint depth prediction and semantic segmentation},
	booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
	year = {2017},
	month = {feb},
	pages = {4620{\textendash}4627},
	abstract = {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.},
	isbn = {9781509046331},
	issn = {10504729},
	doi = {10.1109/ICRA.2017.7989537},
	url = {http://arxiv.org/abs/1702.08009 http://dx.doi.org/10.1109/ICRA.2017.7989537},
	author = {Omid Hosseini Jafari and Groth, Oliver and Kirillov, Alexander and Yang, Michael Ying and Carsten Rother}
}
@mastersthesis {6326,
	title = {Correlation of Performance and Entropy in Active Learning with Convolutional Neural Networks},
	year = {2017},
	school = {Heidelberg University},
	author = {Krause, G}
}
@conference {Schlesinger2017,
	title = {Crowd sourcing image segmentation with iaSTAPLE},
	booktitle = {Proceedings - International Symposium on Biomedical Imaging},
	year = {2017},
	pages = {401{\textendash}405},
	abstract = {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.},
	keywords = {Crowdsourcing, Epithelial cell segmentation, IaSTAPLE, Markovian Random Fields},
	isbn = {9781509011711},
	issn = {19458452},
	doi = {10.1109/ISBI.2017.7950547},
	author = {Schlesinger, Dmitrij and Jug, Florian and Myers, Gene and Carsten Rother and Kainmueller, Dagmar}
}
@conference {Brachmann2017,
	title = {DSAC - Differentiable RANSAC for camera localization},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {nov},
	pages = {2492{\textendash}2500},
	abstract = {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.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.267},
	url = {http://arxiv.org/abs/1611.05705},
	author = {Brachmann, Eric and Krull, Alexander and Nowozin, Sebastian and Shotton, Jamie and Michel, Frank and Gumhold, Stefan and Carsten Rother}
}
@article {storath2016edge,
	title = {Edge preserving and noise reducing reconstruction for magnetic particle imaging},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {36},
	year = {2017},
	pages = {74 - 85},
	doi = {10.1109/TMI.2016.2593954},
	author = {Martin Storath and Christina Brandt and Martin Hofmann and Tobias Knopp and Johannes~Salamon and Alexander Weber and Andreas Weinmann}
}
@conference {Michel2017,
	title = {Global hypothesis generation for 6D object pose estimation},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {115{\textendash}124},
	abstract = {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 "Occluded Object Dataset".},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.20},
	url = {http://arxiv.org/abs/1612.02287},
	author = {Michel, Frank and Kirillov, Alexander and Brachmann, Eric and Krull, Alexander and Gumhold, Stefan and Savchynskyy, Bogdan and Carsten Rother}
}
@conference {Kirillov2017a,
	title = {InstanceCut: From edges to instances with MultiCut},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	pages = {7322{\textendash}7331},
	abstract = {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.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.774},
	author = {Kirillov, Alexander and Levinkov, Evgeny and Bj{\"o}rn Andres and Savchynskyy, Bogdan and Carsten Rother}
}
@conference {Levinkov2017,
	title = {Joint graph decomposition \& node labeling: Problem, algorithms, applications},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	pages = {1904{\textendash}1912},
	abstract = {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.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.206},
	author = {Levinkov, Evgeny and Uhrig, Jonas and Tang, Siyu and Omran, Mohamed and Insafutdinov, Eldar and Kirillov, Alexander and Carsten Rother and Brox, Thomas and Schiele, Bernt and Bj{\"o}rn Andres}
}
@conference {Kirillov2017,
	title = {Joint training of generic CNN-CRF models with stochastic optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {10112 LNCS},
	year = {2017},
	pages = {221{\textendash}236},
	abstract = {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.},
	isbn = {9783319541839},
	issn = {16113349},
	doi = {10.1007/978-3-319-54184-6_14},
	url = {http://host.robots.ox.ac.uk:8080/leaderboard},
	author = {Kirillov, A and Schlesinger, D and Zheng, S and Savchynskyy, B and Torr, P. H.S. and Carsten Rother}
}
@article {6212,
	title = {Learned Watershed: End-to-End Learning of Seeded Segmentation},
	year = {2017},
	pages = {2030-2038},
	author = {Wolf, S and Schott, L and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@techreport {Kruse,
	title = {Learning to Push the Limits of Efficient FFT-based Image Deconvolution - Supplemental Material},
	year = {2017},
	abstract = {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).},
	author = {Kruse, Jakob and Carsten Rother and Schmidt, Uwe and Dresden, T U}
}
@conference {Kruse2017,
	title = {Learning to Push the Limits of Efficient FFT-Based Image Deconvolution},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2017-Octob},
	year = {2017},
	pages = {4596{\textendash}4604},
	abstract = {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.},
	isbn = {9781538610329},
	issn = {15505499},
	doi = {10.1109/ICCV.2017.491},
	author = {Kruse, Jakob and Carsten Rother and Schmidt, Uwe}
}
@article {6181,
	title = {Maschinelles Lernen},
	journal = {Patent, Patent Number WO2017032775A1},
	year = {2017},
	author = {Kandemir, M. and Fred A. Hamprecht and Wojek, C and Schmidt, U.}
}
@article {6122,
	title = {Multicut brings automated neurite segmentation closer to human performance},
	journal = {Nature Methods},
	volume = {14},
	year = {2017},
	pages = {101-102},
	doi = {10.1038/nmeth.4151},
	url = {http://rdcu.be/oVDQ},
	author = {Thorsten Beier and Pape, C and Rahaman, N and Prange, T and Stuart Berg and Bock, D and A. Cardona and G. W. Knott and Plaza, S M and Scheffer, L K and Ullrich K{\"o}the and Kreshuk, A and Fred A. Hamprecht}
}
@article {6178,
	title = {Neuron Segmentation with High-Level Biological Priors},
	journal = {IEEE Transactions on Medical Imaging},
	volume = {37},
	year = {2017},
	chapter = {829-839},
	doi = {10.1109/TMI.2017.2712360},
	author = {Krasowki, N and Thorsten Beier and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht and Kreshuk, A.}
}
@article {6210,
	title = {An Objective Comparison of Cell Tracking Algorithms},
	journal = {Nature Methods},
	volume = {14},
	year = {2017},
	pages = {1141-1152},
	doi = {10.1038/NMETH.447},
	author = {Ulman, V. and Ma{\v s}ka, M. and Magnusson, K .E. G. and Ronneberger, O. and Haubold, C. and Harder, N. and Matula, P. and Matula, P. and Svoboda, D. and Radojevic, M. and Smal, I. and Karl Rohr and Jald{\'e}n, J. and Blau, H. M. and Dzyubachyk, O. and Lelieveldt, B. and Xiao, P. and Li, Y. and Cho, S-Y. and Dufour, A. and Olivo-Marin, J. C. and Reyes-Aldasoro, C. C. and Solis-Lemus, J. A. and Bensch, R. and Brox, T. and Stegmaier, J. and Mikut, R. and Wolf, S. and Fred A. Hamprecht and Esteves, T. and Quelhas, P. and Demirel, {\"O}. and Malstr{\"o}m, L. and Jug, F. and Toman{\v c}{\'a}k, P. and Meijering, E. and Mu{\~n}oz-Barrutia, A. and Kozubek, M. and Ortiz-de-Solorzano, C.}
}
@article {6188,
	title = {Optogenetically stimulating the intact corticospinal tract post-stroke restores motor control through regionalized functional circuit formation},
	journal = {Nature Communications},
	year = {2017},
	pages = {(ASW \& UB contributed equally; BO and MES contributed equally)},
	doi = {10.1038/s41467-017-01090-6},
	url = {https://www.nature.com/articles/s41467-017-01090-6},
	author = {Anna-Sophia Wahl and Uta B{\"u}chler and A. Br{\"a}ndli and Biagio Brattoli and S. Musall and H. Kasper and B.V. Ineichen and F. Helmchen and Bj{\"o}rn Ommer and M. E. Schwab}
}
@conference {Krull2017,
	title = {PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning},
	booktitle = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
	volume = {2017-Janua},
	year = {2017},
	month = {dec},
	pages = {2566{\textendash}2574},
	abstract = {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.},
	isbn = {9781538604571},
	doi = {10.1109/CVPR.2017.275},
	url = {http://arxiv.org/abs/1612.03779},
	author = {Krull, Alexander and Brachmann, Eric and Nowozin, Sebastian and Michel, Frank and Shotton, Jamie and Carsten Rother}
}
@booklet {Massiceti,
	title = {Random Forests versus Neural Networks - What{\textquoteright}s best for camera location},
	year = {2017},
	abstract = {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.},
	author = {Massiceti, Daniela and Krull, Alexander and Brachmann, Eric and Carsten Rother and Torr, Philip H S}
}
@conference {Markowsky2017,
	title = {Segmentation of cell structure using model-based set covering with iterative reweighting},
	booktitle = {Proc. ISBI},
	year = {2017},
	author = {Markowsky, P. and Reith, S. and Zuber, T.E. and K{\"o}nig, R. and Rohr, K. and Schn{\"o}rr, C.}
}
@article {Hullin2017,
	title = {Semantic-Aware Image Smoothing},
	journal = {Vision, Modeling, and Visualization},
	year = {2017},
	abstract = {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.},
	keywords = {Enhancement{\textemdash} Smoothing, I43 [Image Processing and Computer Vision]},
	url = {https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf},
	author = {Hullin, M and Klein, R and Schultz, T and Yao, A and Weihao Li and Omid Hosseini Jafari and Carsten Rother}
}
@article {6213,
	title = {Solving Large Multicut Problems for Connectomics via Domain Decomposition},
	year = {2017},
	pages = {1-10},
	doi = { 10.1109/ICCVW.2017.7},
	author = {Pape, C and Thorsten Beier and Li, P and Jain, V and Brock, D. D. and Kreshuk, A.}
}
@proceedings {6205,
	title = {Sparse convolutional coding for neuronal assembly detection},
	year = {2017},
	author = {Peter, S. and Kirschbaum, E. and M. Both and Campbell, L. A. and Harvey, B. K. and Heins, C. and Durstewitz, D. and Ferran Diego and Fred A. Hamprecht}
}
@proceedings {6140,
	title = {Variational Bayesian Multiple Instance Learning with Gaussian Processes},
	year = {2017},
	pages = {6570-6579},
	doi = {10.1109/CVPR.2017.93},
	author = {Manuel Hau{\ss}mann and Fred A. Hamprecht and Kandemir, M}
}
@conference {kunz2016,
	title = {Active thermography as a tool to investigate heat and gas transfer across the air-water interface},
	booktitle = {13th Quantitative Infrared Thermographie Conference (QIRT 2016), Gdansk 4{\textendash}8 July 2016},
	year = {2016},
	doi = {10.21611/qirt.2016.069},
	author = {Jakob Kunz and Bernd J{\"a}hne}
}
@phdthesis {6076,
	title = {Automated Segmentation for Connectomics Utilizing Higher-Order Biological Priors},
	year = {2016},
	publisher = {University of Heidelberg},
	author = {Krasowski, N}
}
@conference {Royer2016,
	title = {Convexity shape constraints for image segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	month = {sep},
	pages = {402{\textendash}410},
	abstract = {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.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.50},
	url = {http://arxiv.org/abs/1509.02122},
	author = {Royer, Loic A. and Richmond, David L. and Carsten Rother and Bj{\"o}rn Andres and Kainmueller, Dagmar}
}
@conference {DBLP:conf/isvc/GussefeldHK16,
	title = {Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets},
	booktitle = {Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}},
	year = {2016},
	abstract = {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.},
	doi = {10.1007/978-3-319-50835-1_8},
	author = {G{\"u}ssefeld, Burkhard and Katrin Honauer and Daniel Kondermann}
}
@conference {Honauer-etal-2017-ICCV,
	title = {A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields},
	booktitle = {Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	address = {Cham},
	abstract = {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.},
	isbn = {978-3-319-54186-0},
	doi = {10.1007/978-3-319-54187-7_2},
	author = {Katrin Honauer and Ole Johannsen and Daniel Kondermann and Bastian Goldl{\"u}cke},
	editor = {Lai, Shang-Hong}
}
@article {kleesiek_16_deep,
	title = {Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.},
	journal = {NeuroImage},
	volume = {129},
	year = {2016},
	pages = {460-469},
	doi = {10.1016/j.neuroimage.2016.01.024},
	author = {Kleesiek, J and Urban, G and Hubert, A and Schwarz, D and Maier-Hein, K and M. Bendszus and A. Biller}
}
@article {6152,
	title = {A Dual Ascent Framework for Lagrangean Decomposition of Combinatorial Problems},
	journal = {arXiv, preprint},
	year = {2016},
	url = {https://arxiv.org/pdf/1612.05460.pdf},
	author = {Swoboda, P. and Kuske, J. and Savchynskyy, B.}
}
@proceedings {6078,
	title = {An Efficient Fusion Move Algorithm for the Minimum Cost Lifted Multicut Problem},
	volume = {LNCS 9906},
	year = {2016},
	pages = {715-730},
	publisher = {Springer},
	doi = { 10.1007/978-3-319-46475-6_44},
	author = {Thorsten Beier and Bj{\"o}rn Andres and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@proceedings {6080,
	title = {Gaussian process density counting from weak supervision},
	volume = {LNCS 9905},
	year = {2016},
	pages = {365-380 },
	publisher = {Springer},
	doi = { 	10.1007/978-3-319-46448-0_22},
	author = {M. von Borstel and Kandemir, M and Schmidt, P and Rao, M and Rajamani, K and Fred A. Hamprecht}
}
@conference {Kondermann_2016_CVPR_Workshops,
	title = {The HCI Benchmark Suite: Stereo and Flow Ground Truth With Uncertainties for Urban Autonomous Driving},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
	year = {2016},
	month = {June},
	abstract = { 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.
},
	author = {Daniel Kondermann and Nair, Rahul and Katrin Honauer and Karsten Krispin and Jonas Andrulis and Alexander Brock and G{\"u}ssefeld, Burkhard and Mohsen Rahimimoghaddam and Sabine Hofmann and Brenner, Claus and Bernd J{\"a}hne}
}
@article {Kappes2016a,
	title = {Higher-order Segmentation via Multicuts},
	journal = {Comp. Vision Image Understanding},
	volume = {143},
	year = {2016},
	pages = {104{\textendash}119},
	author = {Kappes, J. and Speth, M. and Reinelt, G. and Schn{\"o}rr, C.}
}
@article {biller_16_improved,
	title = {Improved Brain Tumor Classification by Sodium MR Imaging: Prediction of IDH Mutation Status and Tumor Progression},
	journal = {American Journal of Neuroradiology},
	volume = {37 },
	year = {2016},
	pages = {66-73},
	doi = {10.3174/ajnr.A4493},
	author = {A. Biller and Badde, S and Nagel, A and Neumann, JO and Wick, W and Hertenstein, A and M. Bendszus and Sahm, F and Benkhedah, N and Kleesiek, J}
}
@conference {Richmond2016,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	month = {jul},
	pages = {144.1{\textendash}144.12},
	abstract = {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.},
	doi = {10.5244/C.30.144},
	url = {http://arxiv.org/abs/1507.07583},
	author = {Richmond, David L. and Kainmueller, Dagmar and Yang, Michael Y. and Myers, Eugene W. and Carsten Rother}
}
@conference {Richmond2016a,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	pages = {144.1{\textendash}144.12},
	abstract = {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.},
	doi = {10.5244/C.30.144},
	url = {https://github.com/BVLC/caffe/wiki/Model-Zoo\#fcn},
	author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Carsten Rother}
}
@conference {Richmond2016b,
	title = {Mapping auto-context decision forests to deep convnets for semantic segmentation},
	booktitle = {British Machine Vision Conference 2016, BMVC 2016},
	volume = {2016-Septe},
	year = {2016},
	pages = {144.1{\textendash}144.12},
	abstract = {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.},
	doi = {10.5244/C.30.144},
	author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Carsten Rother}
}
@article {Kappes2016,
	title = {Multicuts and Perturb \& MAP for Probabilistic Graph Clustering},
	journal = {Journal of Mathematical Imaging and Vision},
	volume = {56},
	number = {2},
	year = {2016},
	month = {jan},
	pages = {221{\textendash}237},
	abstract = {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.},
	keywords = {Correlation clustering, graphical models, Multicut, Perturb and MAP},
	issn = {15737683},
	doi = {10.1007/s10851-016-0659-3},
	url = {http://arxiv.org/abs/1601.02088},
	author = {Kappes, Jorg Hendrik and Swoboda, Paul and Savchynskyy, Bogdan and Hazan, Tamir and Christoph Schn{\"o}rr}
}
@article {Kappes2016,
	title = {Multicuts and Perturb \& MAP for Probabilistic Graph Clustering},
	journal = {J. Math. Imag. Vision},
	volume = {56},
	number = {2},
	year = {2016},
	pages = {221{\textendash}237},
	author = {Kappes, J.H. and Swoboda, P. and Savchynskyy, B. and Hazan, T. and Schn{\"o}rr, C.}
}
@article {Zisler2016,
	title = {Non-Binary Discrete Tomography by Continuous Non-Convex Optimization},
	journal = {IEEE Comp. Imaging},
	volume = {2},
	number = {3},
	year = {2016},
	pages = {335-347},
	author = {M. Zisler and Kappes, J.H. and Schn{\"o}rr, Cl. and Petra, S. and Christoph Schn{\"o}rr}
}
@article {Swoboda2016,
	title = {Partial Optimality by Pruning for MAP-Inference with General Graphical Models},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {38},
	number = {7},
	year = {2016},
	month = {jul},
	pages = {1370{\textendash}1382},
	publisher = {IEEE Computer Society},
	abstract = {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{\textquoteright}s runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.},
	keywords = {energy minimization, Local polytope, MAP-inference, Markov random fields, partial optimality, persistency},
	issn = {01628828},
	doi = {10.1109/TPAMI.2015.2484327},
	author = {Swoboda, Paul and Shekhovtsov, Alexander and Kappes, Jorg Hendrik and Christoph Schn{\"o}rr and Savchynskyy, Bogdan}
}
@article {Swoboda2016,
	title = {Partial Optimality by Pruning for MAP-Inference with General Graphical Models},
	journal = {IEEE Trans. Patt. Anal. Mach. Intell.},
	volume = {38},
	number = {7},
	year = {2016},
	pages = {1370{\textendash}1382},
	author = {Swoboda, P. and Shekhovtsov, A. and Kappes, J.H. and Schn{\"o}rr, C. and Savchynskyy, B.}
}
@inbook {6055,
	title = {Segmenting and Tracking Multiple Dividing Targets Using ilastik},
	booktitle = {Focus on Bio-Image Informatics},
	series = {Advances in Anatomy, Embryology and Cell Biology},
	volume = {219},
	year = {2016},
	pages = {199-229},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-28549-8_8},
	author = {Haubold, C. and Schiegg, M. and Kreshuk, A. and Stuart Berg and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@mastersthesis {6074,
	title = {Structured Learning on Calcium Imaging Data},
	year = {2016},
	school = {University of Heidelberg},
	type = {Master Theses},
	author = {Kiem, A}
}
@conference {Brachmann2016,
	title = {Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	pages = {3364{\textendash}3372},
	abstract = {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.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.366},
	author = {Brachmann, Eric and Michel, Frank and Krull, Alexander and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@conference {Brachmann2016a,
	title = {Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {2016-Decem},
	year = {2016},
	pages = {3364{\textendash}3372},
	abstract = {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.},
	isbn = {9781467388504},
	issn = {10636919},
	doi = {10.1109/CVPR.2016.366},
	author = {Brachmann, Eric and Michel, Frank and Krull, Alexander and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@proceedings {6079,
	title = {Variational weakly-supervised Gaussian processes},
	year = {2016},
	author = {Kandemir, M and Manuel Hau{\ss}mann and Ferran Diego and Rajamani, K and van der Laak, J and Fred A. Hamprecht}
}
@article {6033,
	title = {Virtual Raters for Reproducible and Objective Assessments in Radiology},
	journal = {Nature Scientific Reports},
	volume = {6},
	year = {2016},
	doi = {10.1038/srep25007},
	author = {Kleesiek, J. and Petersen, J. and D{\"o}ring, M. and Maier-Hein, K. and Ullrich K{\"o}the and Wick, W. and Fred A. Hamprecht and M. Bendszus and A. Biller}
}
@article {Krah2015a,
	title = {An advanced image processing method to improve the spatial resolution of ion radiographies},
	journal = {Physics in Medicine and Biology},
	volume = {60},
	year = {2015},
	pages = {8525},
	abstract = {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.},
	url = {http://stacks.iop.org/0031-9155/60/i=21/a=8525},
	author = {Nils Krah and M Testa and S Brons and O J{\"a}kel and K Parodi and Bj{\"o}rn Voss and I Rinaldi}
}
@proceedings {kandemir_15_asymmetric,
	title = {Asymmetric transfer learning with deep Gaussian processes},
	year = {2015},
	note = {1},
	pages = {730-738},
	author = {Kandemir, M.}
}
@article {kreshuk_15_automated,
	title = {Automated Tracing of Myelinated Axons and Detection of the Nodes of Ranvier in Serial Images of Peripheral Nerves},
	journal = {Journal of Microscopy},
	volume = {259 (2)},
	year = {2015},
	pages = {143-154},
	doi = {10.1111/jmi.12266},
	author = {Anna Kreshuk and Walecki, R. and Ullrich K{\"o}the and Gierthm{\"u}hlen, M. and Plachta, D. and Genoud, C. and Haastert-Talini, K. and Fred A. Hamprecht}
}
@proceedings {kandemir_15_cell,
	title = {Cell event detection in phase-contrast microscopy sequences from few annotations},
	volume = {LNCS 9351},
	year = {2015},
	pages = {316-323},
	publisher = {Springer},
	doi = {10.1007/978-3-319-24574-4_38},
	author = {Kandemir, M. and Fred A. Hamprecht}
}
@article {krall2015,
	title = {Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank},
	journal = {Ocean Sci.},
	volume = {11},
	year = {2015},
	pages = {111--120},
	doi = {10.5194/os-11-111-2015},
	author = {Leila Nagel and Kerstin Ellen Krall and Bernd J{\"a}hne}
}
@article {kappes_15_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	year = {2015},
	note = {1},
	pages = {1-30},
	doi = {10.1007/s11263-015-0809-x},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@article {Kappes2015,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {Int.~J.~Comp.~Vision},
	year = {2015},
	note = {in press (preprint: arXiv:1404.0533)},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@article {Kappes2015,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {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.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015a,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {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.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	isbn = {25164671.25},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {Kappes2015b,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {International Journal of Computer Vision},
	volume = {115},
	number = {2},
	year = {2015},
	pages = {155{\textendash}184},
	abstract = {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.},
	keywords = {Benchmark, Combinatorial optimization, Discrete graphical models},
	issn = {15731405},
	doi = {10.1007/s11263-015-0809-x},
	author = {Kappes, J{\"o}rg H and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Kausler, Bernhard X and Kr{\"o}ger, Thorben and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@proceedings {6003,
	title = {The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors},
	volume = {44},
	year = {2015},
	pages = {145-159},
	author = {Kandemir, M and Fred A. Hamprecht}
}
@conference {beier_15_fusion,
	title = {Fusion Moves for Correlation Clustering},
	booktitle = {CVPR. Proceedings},
	year = {2015},
	note = {1},
	pages = {3507-3516},
	author = {Thorsten Beier and Fred A. Hamprecht and J{\"o}rg H. Kappes}
}
@conference {Alhaija2015,
	title = {Graphflow{\textemdash}6D large displacement scene flow via graph matching},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9358},
	year = {2015},
	pages = {285{\textendash}296},
	abstract = {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.},
	isbn = {9783319249469},
	issn = {16113349},
	doi = {10.1007/978-3-319-24947-6_23},
	author = {Hassan Abu Alhaija and Sellent, Anita and Kondermann, Daniel and Carsten Rother}
}
@article {schiegg_15_graphical,
	title = {Graphical Model for Joint Segmentation and Tracking of Multiple Dividing Cell},
	journal = {Bioinformatics},
	volume = {31},
	number = {6},
	year = {2015},
	note = {1},
	pages = {948-956},
	doi = {10.1093/bioinformatics/btu764},
	url = {http://bioinformatics.oxfordjournals.org/content/early/2014/11/17/bioinformatics.btu764.full.pdf?keytype=ref\&ijkey=mTXWsiFrci7R8tc},
	author = {Schiegg, M. and Hanslovsky, P. and Haubold, C. and Ullrich K{\"o}the and Hufnagel, L. and Fred A. Hamprecht}
}
@conference {Honauer-etal-2015-ICCV,
	title = {The HCI Stereo Metrics: Geometry-Aware Performance Analysis of Stereo Algorithms},
	booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
	year = {2015},
	month = {December},
	abstract = {@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}
} },
	author = {Katrin Honauer and Lena Maier-Hein and Daniel Kondermann}
}
@conference {krasowski_15_improving,
	title = {Improving 3D EM Data Segmentation by Joint Optimization over Boundary Evidence and Biological Priors},
	booktitle = {12th {IEEE} International Symposium on Biomedical Imaging, {ISBI} 2015, Brooklyn, NY, USA, April 16-19, 2015},
	year = {2015},
	note = {1},
	pages = {536-539},
	doi = {10.1109/ISBI.2015.7163929},
	author = {Niko Krasowski and Thorsten Beier and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht and Anna Kreshuk}
}
@conference {Kirillov2015a,
	title = {Inferring M-best diverse labelings in a single one},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {1814{\textendash}1822},
	abstract = {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.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.211},
	author = {Kirillov, Alexander and Savchynskyy, Bogdan and Schlesinger, Dmitrij and Vetrov, Dmitry and Carsten Rother}
}
@article {kiefhaber2015a,
	title = {Influence of natural surfactants on short wind waves in the coastal Peruvian waters},
	volume = {12},
	year = {2015},
	pages = {1291{\textendash}1325},
	doi = {10.5194/osd-12-1291-2015},
	author = {Daniel Kiefhaber and Christopher J Zappa and Bernd J{\"a}hne}
}
@conference {Krull2015,
	title = {Learning analysis-by-synthesis for 6d pose estimation in RGB-D images},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {954{\textendash}962},
	abstract = {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 "learns to compare", 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.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.115},
	author = {Krull, Alexander and Brachmann, Eric and Michel, Frank and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@inbook {Krolla-etal-2015-LF4CV,
	title = {Light Field from Smartphone-Based Dual Video},
	booktitle = {Computer Vision - ECCV 2014 Workshops: Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part II},
	year = {2015},
	pages = {600{\textendash}610},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-16181-5},
	doi = {10.1007/978-3-319-16181-5_46},
	url = {http://dx.doi.org/10.1007/978-3-319-16181-5_46},
	author = {Bernd Krolla and Maximilian Diebold and Didier Stricker}
}
@conference {Kirillov2015,
	title = {M-best-diverse labelings for submodular energies and beyond},
	booktitle = {Advances in Neural Information Processing Systems},
	volume = {2015-Janua},
	year = {2015},
	pages = {613{\textendash}621},
	abstract = {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.},
	issn = {10495258},
	author = {Kirillov, Alexander and Schlesinger, Dmitrij and Vetrov, Dmitry and Carsten Rother and Savchynskyy, Bogdan}
}
@article {mesarchaki2015,
	title = {Measuring air{\textendash}sea gas-exchange velocities in a large-scale annular wind{\textendash}wave tank},
	journal = {Ocean Sci.},
	volume = {11},
	year = {2015},
	pages = {121--138},
	doi = {10.5194/os-11-121-2015},
	author = {Evridiki Mesarchaki and Christine Kr{\"a}uter and Kerstin Ellen Krall and Maximilian Bopp and F. Helleis and Jonathan Williams and Bernd J{\"a}hne}
}
@article {friedl2015,
	title = {Optical sensing of oxygen using a modified Stern-Volmer equation for high laser irradiance},
	journal = {Sensors and Actuators B: Chemical},
	volume = {206},
	year = {2015},
	note = {Available online 30 September 2014},
	pages = {336--342},
	doi = {10.1016/j.snb.2014.09.073},
	author = {Felix Friedl and Nils Krah and Bernd J{\"a}hne}
}
@article {friedl2015,
	title = {Optical sensing of oxygen using a modified Stern-Volmer equation for high laser irradiance},
	journal = {Sensors and Actuators B: Chemical},
	volume = {206},
	year = {2015},
	pages = {336{\textendash}342},
	doi = {10.1016/j.snb.2014.09.073},
	author = {Felix Friedl and Nils Krah and Bernd J{\"a}hne}
}
@conference {Michel2015,
	title = {Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression},
	year = {2015},
	pages = {181.1{\textendash}181.11},
	abstract = {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.},
	doi = {10.5244/c.29.181},
	author = {Michel, Frank and Krull, Alexander and Brachmann, Eric and Yang, Michael Ying and Gumhold, Stefan and Carsten Rother}
}
@conference {Kappes2015a,
	title = {Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts},
	booktitle = {Proc.~SSVM},
	year = {2015},
	publisher = {Springer},
	organization = {Springer},
	author = {J{\"o}rg H. Kappes and Swoboda, P. and Savchynskyy, B. and Hazan, T. and Christoph Schn{\"o}rr}
}
@conference {Kappes2015c,
	title = {Probabilistic correlation clustering and image partitioning using perturbed Multicuts},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9087},
	year = {2015},
	pages = {231{\textendash}242},
	abstract = {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.},
	keywords = {Correlation clustering, Multicut, Perturb and MAP},
	isbn = {9783319184609},
	issn = {16113349},
	doi = {10.1007/978-3-319-18461-6_19},
	author = {Kappes, Jorg Hendrik and Swoboda, Paul and Savchynskyy, Bogdan and Hazan, Tamir and Christoph Schn{\"o}rr}
}
@conference {Kappes2015a,
	title = {Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts},
	booktitle = {Proc. SSVM},
	series = {LNCS},
	year = {2015},
	publisher = {Springer},
	organization = {Springer},
	author = {Kappes, J. and Swoboda, P. and Savchynskyy, B. and Hazan , T. and Schn{\"o}rr, C.}
}
@conference {schiegg_15_proof-reading,
	title = {Proof-reading Guidance in Cell Tracking by Sampling from Tracking-by-assignment Models},
	booktitle = {ISBI. Proceedings},
	year = {2015},
	note = {1},
	pages = {394-398},
	doi = {10.1109/ISBI.2015.7163895},
	author = {Schiegg, M. and Heuer, B. and Haubold, C. and Wolf, S. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Nair2015,
	title = {Reflection modeling for passive stereo},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	volume = {2015 Inter},
	year = {2015},
	pages = {2291{\textendash}2299},
	abstract = {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.},
	isbn = {9781467383912},
	issn = {15505499},
	doi = {10.1109/ICCV.2015.264},
	author = {Nair, Rahul and Fitzgibbon, Andrew and Kondermann, Daniel and Carsten Rother}
}
@inbook {Kondermann-etal-2015-ACCV,
	title = {Stereo Ground Truth with Error Bars},
	booktitle = {Computer Vision {\textendash} ACCV 2014: 12th Asian Conference on Computer Vision, Singapore, Singapore, November 1-5, 2014, Revised Selected Papers, Part V},
	year = {2015},
	pages = {595{\textendash}610},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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.},
	isbn = {978-3-319-16814-2},
	doi = {10.1007/978-3-319-16814-2_39},
	url = {http://dx.doi.org/10.1007/978-3-319-16814-2_39},
	author = {Daniel Kondermann and Nair, Rahul and Stephan Meister and Wolfgang Mischler and G{\"u}ssefeld, Burkhard and Katrin Honauer and Sabine Hofmann and Brenner, Claus and Bernd J{\"a}hne}
}
@article {cali_15_three-dimensional,
	title = {Three-dimensional immersive virtual reality for studying cellular compartments in 3D models from EM preparations of neural tissues},
	journal = {Journal of Comparative Neurology},
	volume = {524},
	year = {2015},
	pages = {23-38},
	doi = {10.1002/cne.23852},
	author = {Cali, C. and Baghabra, J. and Boges, D. J. and Holst, G. R. and Anna Kreshuk and Fred A. Hamprecht and Srinivasan, M. and Lehv{\"a}slaiho, H. and Magistretti, P. J.}
}
@conference {Kappes2015b,
	title = {TomoGC: Binary Tomography by Constrained Graph Cuts},
	booktitle = {Proc.~GCPR},
	year = {2015},
	author = {J{\"o}rg H. Kappes and Stefania Petra and Christoph Schn{\"o}rr and M. Zisler}
}
@conference {Kappes2015b,
	title = {TomoGC: Binary Tomography by Constrained Graph Cuts},
	booktitle = {Proc. GCPR},
	year = {2015},
	author = {Kappes, J.H. and Petra, S. and Schn{\"o}rr, C. and M. Zisler}
}
@article {kauppi_15_towards,
	title = {Towards brain-activity-controlled information retrieval: Decoding image relevance from MEG signals},
	journal = {NeuroImage},
	volume = {112},
	year = {2015},
	note = {1},
	pages = {288-298},
	doi = {10.1016/j.neuroimage.2014.12.079},
	author = {Kauppi, J .P. and Kandemir, M. and Saarinen, V .M. and Hirvenkari, L. and Parkkonen, L. and Klami, A. and Hari, R. and Kaski, S.}
}
@incollection {Richmond2015,
	title = {Uncertainty-driven forest predictors for vertebra localization and segmentation},
	volume = {9349},
	year = {2015},
	pages = {653{\textendash}660},
	abstract = {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.},
	issn = {16113349},
	doi = {10.1007/978-3-319-24553-9_80},
	author = {Richmond, David and Kainmueller, Dagmar and Glocker, Ben and Carsten Rother and Myers, Gene}
}
@phdthesis {kraeuter2015,
	title = {Visualization of air-water gas exchange},
	volume = {Dissertation},
	year = {2015},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	doi = {10.11588/heidok.00018209},
	author = {Christine Kr{\"a}uter}
}
@article {kiefhaber2015b,
	title = {Water wave measurement from stereo images of specular reflections},
	volume = {26},
	year = {2015},
	pages = {115401},
	abstract = {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{\textquoteright}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.},
	doi = {10.1088/0957-0233/26/11/115401},
	author = {Daniel Kiefhaber and Guillemette Caulliez and Christopher J Zappa and Julia Schaper and Bernd J{\"a}hne}
}
@proceedings {kreshuk_15_who,
	title = {Who is talking to whom: synaptic partner detection in anisotropic volumes of insect brain},
	volume = {LNCS 9349},
	year = {2015},
	pages = {661-668},
	publisher = {Springer},
	doi = {10.1007/978-3-319-24553-9_81},
	author = {Anna Kreshuk and Funke, J. and A. Cardona and Fred A. Hamprecht}
}
@conference {Kainmueller2014,
	title = {Active graph matching for automatic joint segmentation and annotation of C. elegans},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8673 LNCS},
	number = {PART 1},
	year = {2014},
	pages = {81{\textendash}88},
	abstract = {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. {\textcopyright} 2014 Springer International Publishing.},
	isbn = {9783319104034},
	issn = {16113349},
	doi = {10.1007/978-3-319-10404-1_11},
	author = {Kainmueller, Dagmar and Jug, Florian and Carsten Rother and Myers, Gene}
}
@conference {krall2014b,
	title = {Air-sea gas exchange and momentum transfer: turbulence and Schmidt number dependency},
	booktitle = {7th SOPRAN Annual Meeting, Bremen, Germany, 25-26 March 2014},
	year = {2014},
	doi = {10.5281/zenodo.10903},
	author = {Kerstin Ellen Krall and Bernd J{\"a}hne}
}
@conference {krall2014a,
	title = {Air-sea gas exchange under nature-like surfactant influence in the lab},
	booktitle = {7th SOPRAN Annual Meeting, Bremen, Germany, 25-26 March 2014},
	year = {2014},
	doi = {10.5281/zenodo.10900},
	author = {Kerstin Ellen Krall and Klaus Schneider-Zapf and Svenja Reith and Daniel Kiefhaber and Bernd J{\"a}hne}
}
@conference {guessefeld2014,
	title = {Are reflectance field renderings appropriate for optical flow evaluation?},
	booktitle = {International Conference on Image Processing, ICIP 2014},
	year = {2014},
	author = {G{\"u}ssefeld, Burkhard and Daniel Kondermann and Schwartz, Christopher and Klein, Reinhard}
}
@conference {kroeger_14_asymmetric,
	title = {Asymmetric Cuts: Joint Image Labeling and Partitioning},
	booktitle = {Pattern Recognition - 36th German Conference, {GCPR} 2014, M{\"u}nster, Germany, September 2-5, 2014, Proceedings},
	year = {2014},
	doi = {10.1007/978-3-319-11752-2_16},
	url = {http://dx.doi.org/10.1007/978-3-319-11752-2_16},
	author = {Thorben Kr{\"o}ger and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Kroeger-2014,
	title = {Asymmetric Cuts: Joint Image Labeling and Partitioning},
	booktitle = {36th German Conference on Pattern Recognition},
	year = {2014},
	author = {Kr{\"o}ger, Thorben and Kappes, J{\"o}rg H. and Thorsten Beier and K{\"o}the, Ullrich and Fred A. Hamprecht}
}
@article {Wahl:Science:2014,
	title = {Asynchronous Therapy Restores Motor Control by Rewiring of the Rat Corticospinal Tract after Stroke},
	journal = {Science},
	volume = {344},
	number = {6189},
	year = {2014},
	pages = {1250--1255},
	publisher = {American Association for The Advancement of Science},
	url = {http://www.sciencemag.org/content/344/6189/1250},
	author = {Anna-Sophia Wahl and Omlor, W. and Rubio, J. C. and Chen, J. L. and Zheng, H. and Schr{\"o}ter, A. and Gullo, M. and Weinmann, O. and Kobayashi, K. and F. Helmchen and Bj{\"o}rn Ommer and M. E. Schwab}
}
@conference {tek_14_automated,
	title = {Automated Cell Nucleus Detection for Large-Volume Electron Microscopy of Neural Tissue},
	booktitle = {ISBI. Proceedings},
	year = {2014},
	note = {1},
	pages = {69-72},
	doi = {10.1109/ISBI.2014.6867811},
	author = {Tek, B. F. and Thorben Kr{\"o}ger and Mikula, S. and Fred A. Hamprecht}
}
@article {kreshuk_14_automated,
	title = {Automated Detection of Synapses in Serial Section Transmission Electron Microscopy Image Stacks},
	journal = {PLoS ONE},
	volume = {9},
	year = {2014},
	note = {1},
	pages = {2},
	doi = {10.1371/journal.pone.0087351},
	author = {Anna Kreshuk and Ullrich K{\"o}the and Pax, E. and Bock, D. D. and Fred A. Hamprecht}
}
@conference {maierhein2014,
	title = {Can masses of non-experts train highly accurate image classifiers? A crowdsourcing approach to instrument segmentation in laparoscopic images},
	booktitle = {MICCAI},
	year = {2014},
	author = {Lena Maier-Hein and Sven Mersmann and Daniel Kondermann and Bodenstedt, S. and Sanchez, A. and C. Stock and Kenngott, H. and Eisenmann, M. and Speidel, S.}
}
@article {nagel2014a,
	title = {Comparative heat and gas exchange measurements in the Heidelberg Aeolotron, a large annular wind-wave tank},
	journal = {Ocean Sci. Discuss.},
	volume = {11},
	year = {2014},
	pages = {1691--1718},
	doi = {10.5194/osd-11-1691-2014},
	author = {Leila Nagel and Kerstin Ellen Krall and Bernd J{\"a}hne}
}
@article {kappes_14_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Structured
Discrete Energy Minimization Problems},
	journal = {CoRR},
	year = {2014},
	note = {1},
	url = {http://arxiv.org/abs/1404.0533},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Kim, S. and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, J. and Komodakis, N. and Savchynskyy, B. and Carsten Rother}
}
@article {kappes-1014-bench-arxiv,
	title = {A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems},
	journal = {CoRR},
	volume = {abs/1404.0533},
	year = {2014},
	url = {http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Thorben Kr{\"o}ger and Lellmann, Jan and Komodakis, Nikos and Savchynskyy, Bogdan and Carsten Rother}
}
@article {kandemir_14_computer-aided,
	title = {Computer-aided diagnosis from weak supervision: A benchmarking study},
	journal = {Computerized Medical Imaging and Graphics},
	volume = {42},
	year = {2014},
	note = {1},
	pages = {44-50},
	doi = {10.1016/j.compmedimag.2014.11.010},
	author = {Kandemir, M. and Fred A. Hamprecht}
}
@conference {maierhein2014a,
	title = {Crowdsourcing for reference correspondence generation in endoscopic images},
	booktitle = {MICCAI},
	year = {2014},
	author = {Lena Maier-Hein and Sven Mersmann and Daniel Kondermann and C. Stock and Kenngott, H. and Sanchez, A. and Wagner, M. and Preukschas, A. and Wekerle, A. -L. and Helfert, S. and Bodenstedt, S. and Speidel, S.}
}
@conference {beier_14_cut,
	title = {Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning},
	booktitle = {2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014},
	year = {2014},
	doi = {10.1109/CVPR.2014.17},
	url = {http://dx.doi.org/10.1109/CVPR.2014.17},
	author = {Thorsten Beier and Thorben Kr{\"o}ger and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {kandemir_14_digital,
	title = {Digital Pathology: Multiple instance learning can detect Barrett{\textquoteright}scancer},
	year = {2014},
	pages = {1348-1351},
	doi = {10.1109/ISBI.2014.6868127},
	author = {Kandemir, M. and Feuchtinger, A. and Walch, A. and Fred A. Hamprecht}
}
@conference {kandemir_14_empowering,
	title = {Empowering multiple instance histopathology cancer diagnosis by cell graphs},
	booktitle = {MICCAI. Proceedings},
	volume = {8674},
	year = {2014},
	note = {1},
	pages = {228-235},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-10470-6_29},
	author = {Kandemir, M. and Zhang, C. and Fred A. Hamprecht}
}
@conference {kandemir:MICCAI:2014,
	title = {Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures},
	booktitle = {Medical Image Computing and Computer-Assisted Intervention},
	year = {2014},
	pages = {154--161},
	publisher = {Springer},
	organization = {Springer},
	author = {Kandemir, M and Rubio, J. C. and Schmidt, U. and Wojek, C. and Welbl, J. and Bj{\"o}rn Ommer and Fred A. Hamprecht}
}
@conference {kandemir_14_event,
	title = {Event Detection by Feature Unpredictability in Phase-Contrast Videos of Cell Cultures},
	booktitle = {MICCAI. Proceedings},
	number = {8674},
	year = {2014},
	note = {1},
	pages = {154-161},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-10470-6_20},
	author = {Kandemir, M. and Rubio, J. C. and Schmidt, U. and Welbl, J. and Bj{\"o}rn Ommer and Fred A. Hamprecht}
}
@conference {esparza2014b,
	title = {Extrinsic calibration of a fisheye multi-camera setup using overlapping fields of view},
	booktitle = {Intelligent Vehicles Symposium Proceedings},
	year = {2014},
	pages = {1276--1281},
	doi = {10.1109/IVS.2014.6856403},
	author = {Knorr, M. and Esparza, J. and Niehsen, W. and Stiller, C.}
}
@article {krall2014,
	title = {First laboratory study of air-sea gas exchange at hurricane wind speeds},
	journal = {Ocean Sci.},
	volume = {10},
	year = {2014},
	pages = {257--265},
	doi = {10.5194/os-10-257-2014},
	author = {Kerstin Ellen Krall and Bernd J{\"a}hne}
}
@article {kraeuter2014,
	title = {High resolution 2-D fluorescence imaging of the mass boundary layer thickness at free water surfaces},
	journal = {J. Europ. Opt. Soc. Rap. Public.},
	volume = {9},
	year = {2014},
	pages = {14016},
	doi = {10.2971/jeos.2014.14016},
	author = {Christine Kr{\"a}uter and Darya Trofimova and Daniel Kiefhaber and Nils Krah and Bernd J{\"a}hne}
}
@conference {Hornacek2014,
	title = {Highly overparameterized optical flow using PatchMatch belief propagation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8691 LNCS},
	number = {PART 3},
	year = {2014},
	pages = {220{\textendash}234},
	abstract = {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 {\textquoteright}fronto-parallel{\textquoteright} 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. {\textcopyright} 2014 Springer International Publishing.},
	keywords = {9 DoF, large displacement, optical flow, PatchMatch, PMBP},
	isbn = {9783319105772},
	issn = {16113349},
	doi = {10.1007/978-3-319-10578-9_15},
	author = {Horn{\'a}{\v c}ek, Michael and Besse, Frederic and Kautz, Jan and Fitzgibbon, Andrew and Carsten Rother}
}
@conference {kraeuter2014a,
	title = {High-resolution 2-D fluorescence imaging of gas transfer at a free water surface},
	booktitle = {Ocean Science Meeting, 23--28. 02. 2014, Honolulu Hawaii},
	year = {2014},
	abstract = {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 > 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.},
	doi = {10.5281/zenodo.12330},
	author = {Christine Kr{\"a}uter and Darya Trofimova and Leila Nagel and Bernd J{\"a}hne}
}
@article {kiefhaber2014a,
	title = {High-speed imaging of short wind waves by shape from refraction},
	journal = {J. Europ. Opt. Soc. Rap. Public.},
	volume = {9},
	year = {2014},
	pages = {14015},
	doi = {10.2971/jeos.2014.14015},
	author = {Daniel Kiefhaber and Svenja Reith and Roland Rocholz and Bernd J{\"a}hne}
}
@conference {kleesiek_14_ilastik,
	title = {ilastik for Multi-modal Brain Tumor Segmentation},
	booktitle = {MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, 3rdplace},
	year = {2014},
	pages = {12-17},
	author = {Kleesiek, J. and A. Biller and Urban, G. and Ullrich K{\"o}the and M. Bendszus and Fred A. Hamprecht}
}
@conference {kandemir_14_instance,
	title = {Instance Label Prediction by Dirichlet Process Multiple Instance Learning},
	booktitle = {UAI. Proceedings},
	year = {2014},
	note = {1},
	author = {Kandemir, M. and Fred A. Hamprecht}
}
@phdthesis {kroeger-14_learning-based,
	title = {Learning-based Segmentation for Connectomics},
	year = {2014},
	publisher = {University of Heidelberg},
	author = {Thorben Kr{\"o}ger}
}
@conference {kappes_14_map,
	title = {MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves},
	booktitle = {Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}},
	year = {2014},
	doi = {10.1007/978-3-319-16181-5_37},
	url = {http://dx.doi.org/10.1007/978-3-319-16181-5_37},
	author = {J{\"o}rg H. Kappes and Thorsten Beier and Christoph Schn{\"o}rr}
}
@conference {Kappes-2014,
	title = {MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves},
	booktitle = {International Workshop on Graphical Models in Computer Vision},
	year = {2014},
	author = {Kappes, Jorg Hendrik and Thorsten Beier and Christoph Schn{\"o}rr}
}
@conference {kiefhaber2014b,
	title = {Measurement of wind waves statistics from specular reflections},
	booktitle = {Ocean Science Meeting, 23--28. 02. 2014, Honolulu Hawaii},
	year = {2014},
	abstract = {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 \& Munk{\textquoteright}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 J{\"a}hne, 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{\textquoteright}s glitter, J. Opt. Soc. Amer., 44 (11), 838-850.},
	doi = {10.5281/zenodo.12335},
	author = {Daniel Kiefhaber and Christopher J. Zappa and Bernd J{\"a}hne}
}
@article {mesarchaki2014a,
	title = {Measuring air-sea gas exchange velocities in a large scale annular wind-wave tank},
	journal = {Ocean Sci. Discuss.},
	volume = {11},
	year = {2014},
	pages = {1643--1689},
	doi = {10.5194/osd-11-1643-2014},
	author = {Evridiki Mesarchaki and Christine Kr{\"a}uter and Kerstin Ellen Krall and Maximilian Bopp and F. Helleis and Jonathan Williams and Bernd J{\"a}hne}
}
@conference {urban_14_multi-modal,
	title = {Multi-modal Brain Tumor Segmentation using Deep Convolutional NeuralNetworks},
	booktitle = {MICCAI BraTS (Brain Tumor Segmentation) Challenge. Proceedings, winningcontribution},
	year = {2014},
	pages = {31-35},
	author = {Urban, G. and M. Bendszus and Fred A. Hamprecht and Kleesiek, J.}
}
@conference {straehle_14_multiple,
	title = {Multiple instance learning with response-optimized random forests},
	booktitle = {ICPR. Proceedings},
	year = {2014},
	note = {1},
	pages = {3768 - 3773},
	doi = {10.1109/ICPR.2014.647},
	author = {Christoph N. Straehle and Kandemir, M. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {kandemir_14_multi-task,
	title = {Multi-task and multi-view learning of user state},
	journal = {Neurocomputing},
	volume = {139},
	year = {2014},
	pages = {97-106},
	doi = {10.1016/j.neucom.2014.02.057},
	author = {Kandemir, M. and Klami, A. and Gonen, M. and Vetek, A. and Kaski, S}
}
@conference {Marquez-Neila2014,
	title = {Non-parametric higher-order random fields for image segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8694 LNCS},
	number = {PART 6},
	year = {2014},
	pages = {269{\textendash}284},
	abstract = {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. {\textcopyright} 2014 Springer International Publishing.},
	keywords = {biomedical image analysis, higher-order models, image denoising, Image segmentation, random fields, structured prediction},
	isbn = {9783319105987},
	issn = {16113349},
	doi = {10.1007/978-3-319-10599-4_18},
	author = {M{\'a}rquez-Neila, Pablo and Kohli, Pushmeet and Carsten Rother and Baumela, Luis}
}
@phdthesis {kiefhaber2014,
	title = {Optical Measurement of Short Wind Waves --- from the Laboratory to the Field},
	volume = {Dissertation},
	year = {2014},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/16304},
	author = {Daniel Kiefhaber}
}
@article {Jug2014,
	title = {Optimal joint segmentation and tracking of escherichia coli in the mother machine},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {8677},
	year = {2014},
	pages = {25{\textendash}36},
	abstract = {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 {\textquotedblleft}Mother Machine{\textquotedblright} [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 > 95\% (1.22 times the inter-observer error) and is on average 2 - 11 times faster than the microscope produces the raw data.},
	issn = {16113349},
	doi = {10.1007/978-3-319-12289-2_3},
	author = {Jug, Florian and Pietzsch, Tobias and Kainm{\"u}ller, Dagmar and Funke, Jan and Kaiser, Matthias and van Nimwegen, Erik and Carsten Rother and Myers, Gene}
}
@article {swoboda_14_partial,
	title = {Partial Optimality by Pruning for MAP-inference with General GraphicalModels},
	year = {2014},
	pages = {1170-1177},
	doi = {10.1109/CVPR.2014.153},
	author = {Swoboda, P. and Savchynskyy, B. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Swoboda-2014,
	title = {Partial Optimality by Pruning for MAP-inference with General Graphical Models},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition 2014},
	year = {2014},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Swoboda-2014,
	title = {Partial Optimality by Pruning for MAP-inference with General Graphical Models},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition 2014},
	year = {2014},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and Kappes, J{\"o}rg H. and Christoph Schn{\"o}rr}
}
@article {Besse2014,
	title = {PMBP: PatchMatch Belief Propagation for correspondence field estimation},
	journal = {International Journal of Computer Vision},
	volume = {110},
	number = {1},
	year = {2014},
	month = {oct},
	pages = {2{\textendash}13},
	publisher = {Kluwer Academic Publishers},
	abstract = {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.},
	keywords = {Belief propagation, Correspondence fields, PatchMatch},
	issn = {15731405},
	doi = {10.1007/s11263-013-0653-9},
	author = {Besse, Frederic and Carsten Rother and Fitzgibbon, Andrew and Kautz, Jan}
}
@article {Besse2014a,
	title = {PMBP: PatchMatch Belief Propagation for correspondence field estimation},
	journal = {International Journal of Computer Vision},
	volume = {110},
	number = {1},
	year = {2014},
	pages = {2{\textendash}13},
	abstract = {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.},
	keywords = {Belief propagation, Correspondence fields, PatchMatch},
	issn = {15731405},
	doi = {10.1007/s11263-013-0653-9},
	author = {Besse, Frederic and Carsten Rother and Fitzgibbon, Andrew and Kautz, Jan}
}
@article {Besse2014b,
	title = {PMBP: PatchMatch Belief Propagation for correspondence field estimation},
	journal = {International Journal of Computer Vision},
	volume = {110},
	number = {1},
	year = {2014},
	pages = {2{\textendash}13},
	abstract = {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.},
	keywords = {Belief propagation, Correspondence fields, PatchMatch},
	issn = {15731405},
	doi = {10.1007/s11263-013-0653-9},
	author = {Besse, Frederic and Carsten Rother and Fitzgibbon, Andrew and Kautz, Jan}
}
@article {maco_14_semiautomated,
	title = {Semiautomated Correlative 3D Electron Microscopy of In Vivo Imaged Axons and Dendrites},
	journal = {Nature Protocols},
	volume = {9},
	year = {2014},
	pages = {1354-1366},
	doi = {10.1038/nprot.2014.101},
	author = {Maco, B. and Cantoni, M. and Holtmaat, A. and Anna Kreshuk and Fred A. Hamprecht and G. W. Knott}
}
@article {koethe_14_simplestorm,
	title = {SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy},
	journal = {Histochemistry and Cell Biology},
	volume = {141},
	year = {2014},
	note = {1},
	pages = {613-627},
	doi = {10.1007/s00418-014-1211-4},
	author = {Ullrich K{\"o}the and Herrmannsd{\"o}rfer, F. and Kats, I. and Fred A. Hamprecht}
}
@conference {krolla-etall-2014-BMVC,
	title = {Spherical Light Fields},
	booktitle = {Proceedings of the British Machine Vision Conference},
	year = {2014},
	publisher = {BMVA Press},
	organization = {BMVA Press},
	abstract = {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. },
	author = {Bernd Krolla and Maximilian Diebold and Bastian Goldl{\"u}cke and Didier Stricker}
}
@conference {kondermann2014,
	title = {Stereo ground truth with error bars},
	booktitle = {Asian Conference on Computer Vision, ACCV 2014},
	year = {2014},
	author = {Daniel Kondermann and Nair, Rahul and Stephan Meister and Wolfgang Mischler and G{\"u}ssefeld, Burkhard and Sabine Hofmann and Brenner, Claus and Bernd J{\"a}hne}
}
@incollection {lou_14_structured,
	title = {Structured Learning from Cheap Data},
	year = {2014},
	note = {1},
	publisher = {The MIT Press},
	author = {Lou, X. and Kloft, M. and R{\"a}tsch, G. and Fred A. Hamprecht},
	editor = {Nowozin, S. et al}
}
@article {goldluecke2014,
	title = {A super-resolution framework for high-accuracy multiview reconstruction},
	journal = {Int. J. Comp. Vision},
	volume = {106},
	year = {2014},
	pages = {172--191},
	doi = {10.1007/s11263-013-0654-8},
	author = {Bastian Goldl{\"u}cke and M. Aubry and K. Kolev and Daniel Cremers}
}
@conference {gottfried2014,
	title = {Time of flight motion compensation revisited},
	booktitle = {International Conference on Image Processing, ICIP 2014},
	year = {2014},
	author = {Gottfried, Jens-Malte and Nair, Rahul and Stephan Meister and Christoph S. Garbe and Daniel Kondermann}
}
@conference {fiaschi_14_tracking,
	title = {Tracking indistinguishable translucent objects over time using weakly supervised structured learning},
	booktitle = {CVPR. Proceedings},
	year = {2014},
	note = {1},
	pages = {2736 - 2743},
	doi = {10.1109/CVPR.2014.356},
	author = {Fiaschi, L. and Ferran Diego and Karl-Heinz Grosser and Schiegg, M. and Ullrich K{\"o}the and Zlatic, M. and Fred A. Hamprecht}
}
@conference {lenor2014,
	title = {Tracking-based visibility estimation},
	booktitle = {Pattern Recognition, 36th German Conference, GCPR 2014, M{\"u}nster, Germany, September 2-5, 2014},
	volume = {8753},
	year = {2014},
	pages = {365--376},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-11752-2_29},
	author = {Lenor, Stephan and Martini, Johannes and Bernd J{\"a}hne and Stopper, Ulrich and Weber, Stefan and Ohr, Florian},
	editor = {Jiang, Xiaoyi and Joachim Hornegger and Reinhard Koch}
}
@phdthesis {krah2014,
	title = {Visualization of air and water-sided concentration profiles in laboratory gas exchange experiments},
	volume = {Dissertation},
	year = {2014},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/16895},
	author = {Nils Krah}
}
@conference {esparaza2014a,
	title = {Wide base stereo with fisheye optics: a robust approach for 3D reconstruction in driving assistance},
	booktitle = {Pattern Recognition, 36th German Conference, GCPR 2014, M{\"u}nster, Germany, September 2-5, 2014},
	volume = {8753},
	year = {2014},
	pages = {342--353},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-11752-2_27},
	author = {Esparza, Jose and Helmle, Michael and Bernd J{\"a}hne},
	editor = {Jiang, Xiaoyi and Joachim Hornegger and Reinhard Koch}
}
@conference {zhang_14_yeast,
	title = {Yeast Cell Detection and Segmentation in Bright Field Microscopy},
	booktitle = {ISBI. Proceedings},
	year = {2014},
	note = {1},
	pages = {1267-1270},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	doi = {10.1109/ISBI.2014.6868107},
	author = {Zhang, C. and Huber, F. and Knop, M. and Fred A. Hamprecht}
}
@conference {Sindeev2013,
	title = {Alpha-flow for video matting},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7726 LNCS},
	number = {PART 3},
	year = {2013},
	pages = {438{\textendash}452},
	abstract = {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. We show high quality results on various challenging sequences, and give a detailed comparison to competing techniques. {\textcopyright} 2013 Springer-Verlag.},
	isbn = {9783642374302},
	issn = {03029743},
	doi = {10.1007/978-3-642-37431-9_34},
	author = {Sindeev, Mikhail and Konushin, Anton and Carsten Rother}
}
@article {mikut_13_automated,
	title = {Automated Processing of Zebrafish Imaging Data: A Survey},
	journal = {Zebrafish},
	volume = {10 (3)},
	year = {2013},
	doi = {10.1089/zeb.2013.0886},
	author = {Mikut, R. and Dickmeis, T. and Driever, W. and Geurts, P. and Fred A. Hamprecht and Bernhard X. Kausler and Ledesma-Carbayo, M. and Marée, R. and Mikula, K. and Pantazis, P. and Ronneberger, O. and Santos, A. and Stotzka, R.}
}
@conference {kappes_13_comparative,
	title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems},
	booktitle = {CVPR 2013. Proceedings},
	year = {2013},
	note = {1},
	doi = {10.1109/CVPR.2013.175},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, S. and Dhruv Batra and Sungwoong, K. and Bernhard X. Kausler and Lellmann, J. and Komodakis, N. and Carsten Rother}
}
@conference {Kappes-2013-benchmark,
	title = {A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem},
	booktitle = {CVPR},
	year = {2013},
	author = {J{\"o}rg H. Kappes and Bj{\"o}rn Andres and Fred A. Hamprecht and Christoph Schn{\"o}rr and Nowozin, Sebastian and Dhruv Batra and Kim, Sungwoong and Bernhard X. Kausler and Lellmann, Jan and Komodakis, Nikos and Carsten Rother}
}
@conference {schiegg_13_conservation,
	title = {Conservation Tracking},
	booktitle = {ICCV 2013. Proceedings},
	year = {2013},
	note = {1},
	pages = {2928--2935},
	doi = {10.1109/ICCV.2013.364},
	author = {Schiegg, M. and Hanslovsky, P. and Bernhard X. Kausler and Hufnagel, L. and Fred A. Hamprecht}
}
@article {maco_13_correlative,
	title = {Correlative in vivo 2 photon and focused ion beam scanning electron microscopy of cortical neurons},
	journal = {PloS one},
	volume = {8 (2)},
	year = {2013},
	doi = {10.1371/journal.pone.0057405},
	author = {Maco, B. and Holtmaat, A. and Cantoni, M. and Anna Kreshuk and Christoph N. Straehle and Fred A. Hamprecht and G. W. Knott}
}
@conference {lenzen_13_denoising,
	title = {Denoising Strategies for Time-of-Flight Data},
	booktitle = {Time-of-Flight Imaging: Algorithms, Sensors and Applications},
	volume = {8200},
	year = {2013},
	note = {1},
	pages = {24-25},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-44964-2_2},
	author = {Frank Lenzen and Kim, K. I. and Sch{\"a}fer, H. and Nair, R. and Stephan Meister and Florian Becker and Christoph S. Garbe},
	editor = {Grzegorzek, M. and Theobalt, C. and Andreas Kolb and Theobalt, C. and Reinhard Koch}
}
@incollection {Lenzen2013stategies,
	title = {Denoising Strategies for Time-of-Flight Data},
	volume = {8200},
	year = {2013},
	pages = {25-45},
	publisher = {Springer},
	author = {Frank Lenzen and Kim, Kwang In and Sch{\"a}fer, Henrik and Nair, Rahul and Stephan Meister and Florian Becker and Christoph S. Garbe},
	editor = {Grzegorzek, Marcin and Theobalt, Christian and Andreas Kolb and Theobalt, Christian and Reinhard Koch}
}
@conference {haeusler_13_ensemble,
	title = {Ensemble Learning for Confidence Measures in Stereo Vision},
	booktitle = {CVPR 2013, in press},
	year = {2013},
	note = {1},
	pages = {305-312},
	doi = {10.1109/CVPR.2013.46},
	author = {Haeusler, R. and Nair, R. and Daniel Kondermann}
}
@article {krall2013a,
	title = {First air-sea gas exchange laboratory study at hurricane wind speeds},
	journal = {Ocean Sci. Discuss.},
	volume = {10},
	year = {2013},
	pages = {1971--1996},
	doi = {10.5194/osd-10-1971-2013},
	url = {www.ocean-sci-discuss.net/10/1971/2013/},
	author = {Kerstin Ellen Krall and Bernd J{\"a}hne}
}
@conference {savchynskyy_13_global,
	title = {Global MAP-Optimality by Shrinking the Combinatorial Search Area
with Convex Relaxation},
	booktitle = {NIPS. Proceedings},
	year = {2013},
	note = {1},
	pages = {1950-1958},
	author = {Savchynskyy, B. and J{\"o}rg H. Kappes and Swoboda, P. and Christoph Schn{\"o}rr}
}
@conference {SavchynskyyNIPS2013,
	title = {Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation},
	booktitle = {NIPS},
	year = {2013},
	note = {Accepted},
	author = {Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Swoboda, Paul and Christoph Schn{\"o}rr}
}
@conference {SavchynskyyNIPS2013,
	title = {Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation},
	booktitle = {NIPS},
	year = {2013},
	note = {Accepted},
	author = {Savchynskyy, Bogdan and Kappes, Jorg Hendrik and Swoboda, Paul and Christoph Schn{\"o}rr}
}
@booklet {Kappes-2013-multicut,
	title = {Higher-order Segmentation via Multicuts},
	howpublished = {ArXiv e-prints},
	year = {2013},
	author = {J{\"o}rg H. Kappes and Speth, Markus and Reinelt, Gerhard and Christoph Schn{\"o}rr}
}
@booklet {Kappes-2013-multicut,
	title = {Higher-order Segmentation via Multicuts},
	howpublished = {ArXiv e-prints},
	year = {2013},
	month = {May},
	author = {Kappes, Jorg Hendrik and Speth, Markus and Reinelt, Gerhard and Christoph Schn{\"o}rr}
}
@article {donath_13_how,
	title = {How Good is Crowdsourcing for Optical Flow Ground Truth Generation?},
	journal = {submitted to CVPR},
	year = {2013},
	note = {1},
	author = {Donath, A. and Daniel Kondermann}
}
@conference {straehle_13_ksmallest,
	title = {K-smallest Spanning Tree Segmentations},
	booktitle = {German Conference on Pattern Recognition (DAGM/GCPR). Proceedings},
	number = {8142},
	year = {2013},
	note = {1},
	pages = {375-384},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-40602-7_40},
	author = {Christoph N. Straehle and Peter, S. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@phdthesis {krall2013,
	title = {Laboratory Investigations of Air-Sea Gas Transfer under a Wide Range of Water Surface Conditions},
	volume = {Dissertation},
	year = {2013},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {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 {\textquoteright}controlled leakage{\textquoteright} 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.},
	url = {http://www.ub.uni-heidelberg.de/archiv/14392},
	author = {Kerstin E. Krall}
}
@conference {kroeger_13_learning,
	title = {Learning to Segment Neurons with Non-local Quality Measures},
	booktitle = {MICCAI 2013. Proceedings, part II},
	volume = {8150},
	year = {2013},
	note = {1},
	pages = {419-427},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-40763-5_52},
	author = {Thorben Kr{\"o}ger and Mikula, S. and Denk, W. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {becker_13_movie,
	title = {Movie Dimensionalization Via Sparse User Annotations},
	booktitle = {submitted to 3DTV-Con},
	year = {2013},
	note = {1},
	author = {Becker, M. and Baron, M. and Daniel Kondermann and Bussler, M. and Helzle, V.}
}
@conference {kondermann_13_movie,
	title = {Movie Dimensionalization Via Sparse User Annotations},
	booktitle = {submitted to ICCV},
	year = {2013},
	note = {1},
	author = {Daniel Kondermann and Becker, M.}
}
@conference {kiefhaber2013,
	title = {Optical measurement of surface ocean waves},
	booktitle = {3rd EOS Topical Meeting on Blue Photonics --- Optics in the Sea},
	year = {2013},
	note = {best student poster award},
	doi = {10.5281/zenodo.12334},
	author = {Daniel Kiefhaber and Roland Rocholz and Paulus Salomon Bauer and Bernd J{\"a}hne}
}
@conference {swoboda_13_partial,
	title = {Partial Optimality via Iterative Pruning for the Potts Model},
	booktitle = {Proceedings of the 4th International Conference on Scale Space and
Variational Methods in Computer Vision SSVM},
	number = {7893},
	year = {2013},
	note = {1},
	pages = {477-488},
	doi = {10.1007/978-3-642-38267-3_40},
	author = {Swoboda, P. and Savchynskyy, B. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {SwobodaSSVM13,
	title = {Partial Optimality via Iterative Pruning for the Potts Model},
	booktitle = {Scale Space and Variational Methods (SSVM 2013)},
	year = {2013},
	author = {Swoboda, Paul and Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {SwobodaSSVM13,
	title = {Partial Optimality via Iterative Pruning for the Potts Model},
	booktitle = {Scale Space and Variational Methods (SSVM 2013)},
	year = {2013},
	author = {Paul Swoboda and Bogdan Savchynskyy and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {swoboda_13_persistency,
	title = {Persistency by Pruning for General Graphical Models},
	booktitle = {submitted to NIPS 2013.},
	year = {2013},
	author = {Swoboda, P. and Savchynskyy, B. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {meister_13_simulation,
	title = {Simulation of Time-of-Flight Sensors using Global Illumination},
	booktitle = {Vision, Modeling and Visualization (VMV), 2013 International Workshop
on. Proceedings},
	year = {2013},
	pages = {33-40},
	doi = {10.2312/PE.VMV.VMV13.033-040},
	author = {Stephan Meister and Nair, R. and Daniel Kondermann}
}
@conference {berger_13_state,
	title = {A State of the Art Report on Kinect Sensor Setups in Computer Vision},
	booktitle = {Time-of-Flight and Depth Imaging. Sensors, Algorithms, and Applications},
	volume = {8200},
	year = {2013},
	pages = {257-272},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-44964-2_12},
	author = {Berger, K. and Stephan Meister and Nair, R. and Daniel Kondermann},
	editor = {Grzegorzek, M. and Theobalt, C. and Andreas Kolb and Reinhard Koch}
}
@conference {nair_13_survey,
	title = {A Survey on Time-of-Flight Stereo Fusion},
	booktitle = {Time-of-Flight Imaging: Algorithms, Sensors and Applications},
	volume = {8022},
	year = {2013},
	pages = {105-127},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-44964-2_6},
	author = {Nair, R. and Ruhl, K. and Frank Lenzen and Stephan Meister and Sch{\"a}fer, H. and Christoph S. Garbe and Eisemann, M. and Daniel Kondermann},
	editor = {Grzegorzek, M. and Theobalt, C. and Andreas Kolb and Reinhard Koch}
}
@incollection {Nair2013survey,
	title = {A Survey on Time-of-Flight Stereo Fusion},
	volume = {8200},
	year = {2013},
	pages = {105-127},
	publisher = {Springer},
	author = {Nair, Rahul and Ruhl, Kai and Frank Lenzen and Stephan Meister and Sch{\"a}fer, Henrik and Christoph S. Garbe and Eisemann, Martin and Magnor, Marcus and Daniel Kondermann},
	editor = {Grzegorzek, Marcin and Theobalt, Christian and Andreas Kolb and Reinhard Koch}
}
@incollection {Nair2013survey,
	title = {A Survey on Time-of-Flight Stereo Fusion},
	volume = {8200},
	year = {2013},
	pages = {105-127},
	publisher = {Springer},
	author = {Nair, Rahul and Ruhl, Kai and Lenzen, Frank and Meister, Stephan and Sch{\"a}fer, Henrik and Garbe, Christoph S. and Eisemann, Martin and Magnor, Marcus and Kondermann, Daniel},
	editor = {Grzegorzek, Marcin and Theobalt, Christian and Koch, Reinhard and Kolb, Andreas}
}
@conference {lefloch_13_technical,
	title = {Technical Foundation and Calibration Methods for Time-of-Flight Cameras},
	booktitle = {Time-of-Flight Imaging: Algorithms, Sensors and Applications},
	volume = {8200},
	number = {3-24},
	year = {2013},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-44964-2_1},
	author = {Lefloch, D. and Nair, R. and Frank Lenzen and Sch{\"a}fer, H. and Streeter, L. and Michael J. Cree},
	editor = {Reinhard Koch and Grzegorzek, M. and Theobalt, C. and Andreas Kolb and Andreas Kolb and Reinhard Koch}
}
@incollection {Lefloch2013foundations,
	title = {Technical Foundation and Calibration Methods for Time-of-Flight Cameras},
	volume = {8200},
	year = {2013},
	pages = {3-24},
	publisher = {Springer},
	author = {Damien Lefloch and Rahul Nair and Frank Lenzen and Henrik Sch{\"a}fer and Lee Streeter and Michael J. Cree and Reinhard Koch and Andreas Kolb},
	editor = {Grzegorzek, Marcin and Theobalt, Christian and Koch, Reinhard and Kolb, Andreas}
}
@article {davis2013,
	title = {Time-of-Flight Imaging: Algorithms, Sensors and Applications (Dagstuhl Seminar 12431)},
	journal = {Dagstuhl Reports},
	volume = {2},
	number = {10},
	year = {2013},
	pages = {79--104},
	publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
	abstract = {This report documents the program and the outcomes of Dagstuhl Seminar 12431 "Time-of-Flight Imaging: Algorithms, Sensors and Applications". 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.},
	issn = {2192-5283},
	doi = {http://dx.doi.org/10.4230/DagRep.2.10.79},
	url = {http://drops.dagstuhl.de/opus/volltexte/2013/3904},
	editor = {Davis, James and Bernd J{\"a}hne and Andreas Kolb and Raskar, Ramesh and Theobalt, Christian and Davis, James and Bernd J{\"a}hne and Raskar, Ramesh and Theobalt, Christian and Andreas Kolb}
}
@conference {Kappes-2013,
	title = {Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization},
	booktitle = {CVPR},
	year = {2013},
	author = {J{\"o}rg H. Kappes and Speth, Markus and Reinelt, Gerhard and Christoph Schn{\"o}rr}
}
@conference {Kappes-2013,
	title = {Towards Efficient and Exact MAP-Inference for Large Scale Discrete Computer Vision Problems via Combinatorial Optimization},
	booktitle = {CVPR},
	year = {2013},
	author = {J{\"o}rg H. Kappes and Markus Speth and Gerhard Reinelt and Christoph Schn{\"o}rr}
}
@phdthesis {kausler_13_tracking,
	title = {Tracking-by-Assignment as a Probabilistic Graphical Model with Applications in Developmental Biology},
	year = {2013},
	publisher = {University of Heidelberg},
	author = {Bernhard X. Kausler}
}
@conference {LNCS80810321,
	title = {Variational Image Segmentation and Cosegmentation with the Wasserstein Distance},
	booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition},
	volume = {8081},
	year = {2013},
	pages = {321--334},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-642-40394-1},
	author = {Swoboda, Paul and Christoph Schn{\"o}rr},
	editor = {Heyden, Anders and Kahl, Fredrik and Oskarsson, Magnus and Tai, Xue-Cheng and Olsson, Carl}
}
@conference {LNCS80810321,
	title = {Variational Image Segmentation and Cosegmentation with the Wasserstein Distance},
	booktitle = {Energy Minimization Methods in Computer Vision and Pattern Recognition},
	series = {Lecture Notes in Computer Science},
	volume = {8081},
	year = {2013},
	pages = {321{\textendash}334},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-642-40394-1},
	author = {Paul Swoboda and Christoph Schn{\"o}rr},
	editor = {Anders Heyden and Fredrik Kahl and Carl Olsson and Magnus Oskarsson and Xue-Cheng Tai}
}
@article {becker_13_variational,
	title = {Variational Recursive Joint Estimation of Dense Scene Structure and
Camera Motion from Monocular High Speed Traffic Sequences},
	journal = {International Journal of Computer Vision},
	volume = {105 (3)},
	year = {2013},
	note = {1},
	pages = {269-297},
	doi = {10.1007/s11263-013-0639-7},
	author = {Florian Becker and Frank Lenzen and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@article {Becker-et-al-13a,
	title = {Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences},
	journal = {International Journal of Computer Vision},
	volume = {105},
	year = {2013},
	pages = {269--297},
	publisher = {Springer US},
	issn = {0920-5691},
	doi = {10.1007/s11263-013-0639-7},
	url = {http://dx.doi.org/10.1007/s11263-013-0639-7},
	author = {Florian Becker and Frank Lenzen and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@article {Becker-et-al-13a,
	title = {Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences},
	journal = {International Journal of Computer Vision},
	volume = {105},
	year = {2013},
	pages = {269{\textendash}297},
	publisher = {Springer US},
	keywords = {Dense depth map, Recursive formulation, Structure from motion, Variational approach},
	issn = {0920-5691},
	doi = {10.1007/s11263-013-0639-7},
	url = {http://dx.doi.org/10.1007/s11263-013-0639-7},
	author = {Florian Becker and Lenzen, Frank and Kappes, J{\"o}rg H. and Christoph Schn{\"o}rr}
}
@conference {straehle_13_weakly,
	title = {Weakly supervised learning of image partitioning using decision trees with structured split criteria},
	booktitle = {ICCV 2013. Proceedings},
	year = {2013},
	note = {1},
	pages = {1849-1856},
	doi = {10.1109/ICCV.2013.232},
	author = {Christoph N. Straehle and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {marquez_13_when,
	title = {When is a confidence measure good enough?},
	journal = {submitted to CVPR 2013},
	year = {2013},
	note = {1},
	author = {Márquez-Valle, P. and Gil, D. and Hernàndez-Sabaté, A. and Daniel Kondermann}
}
@article {andres_11_3d,
	title = {3D Segmentation of SBFSEM Images of Neuropil by a Graphical Model over Supervoxel Boundaries},
	journal = {Medical Image Analysis},
	volume = {16 (2012)},
	year = {2012},
	note = {1},
	pages = {796-805},
	doi = {10.1016/j.media.2011.11.004},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Thorben Kr{\"o}ger and Helmstaedter, M. and K. L. Briggmann and Denk, W. and Fred A. Hamprecht}
}
@article {hanselmann_12_active,
	title = {Active Learning for Convenient Annotation and Classification of Secondary Ion Mass Spectrometry Images},
	journal = {Analytical Chemistry},
	volume = {85 (1)},
	year = {2012},
	note = {1},
	pages = {147-155},
	doi = {10.1021/ac3023313},
	author = {Hanselmann, M. and R{\"o}der, J. and Ullrich K{\"o}the and B. Y. Renard and Heeren, R. M. A. and Fred A. Hamprecht}
}
@article {roeder_12_active,
	title = {Active Learning with Distributional Estimates},
	journal = {UAI 2012. Proceedings},
	year = {2012},
	note = {1},
	pages = {715-725},
	author = {R{\"o}der, J. and Kunzmann, K. and Nadler, B. and Fred A. Hamprecht}
}
@phdthesis {kreshuk_12_automated,
	title = {Automated Analysis of Biomedical Data from Low to High Resolution},
	year = {2012},
	publisher = {University of Heidelberg},
	author = {Anna Kreshuk}
}
@article {zechmann_12_automated,
	title = {Automated vs. manual pattern recognition of 3D 1H MRSI data of patients with prostate cancer},
	journal = {Academic Radiology},
	volume = {19, 6},
	year = {2012},
	pages = {675-684},
	doi = {10.1016/j.acra.2012.02.014},
	author = {C. M. Zechmann and Bjoern H. Menze and B. Michael Kelm and Zamecnik, P. and Ikinger, U. and Waldherr, R. and Delorme, S. and Fred A. Hamprecht and Bachert, P.}
}
@conference {kappes_12_bundle,
	title = {A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation},
	booktitle = {CVPR. Proceedings},
	year = {2012},
	note = {1},
	pages = {1688-1695},
	doi = {10.1109/CVPR.2012.6247863},
	author = {J{\"o}rg H. Kappes and Savchynskyy, B. and Christoph Schn{\"o}rr}
}
@conference {Kappes2012,
	title = {A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation},
	booktitle = {CVPR},
	year = {2012},
	note = {in press},
	author = {J{\"o}rg H. Kappes and Savchynskyy, Bogdan and Christoph Schn{\"o}rr}
}
@conference {Shekhovtsov2012,
	title = {Curvature prior for MRF-based segmentation and shape inpainting},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7476 LNCS},
	year = {2012},
	pages = {41{\textendash}51},
	abstract = {Most image labeling problems such as segmentation and image reconstruction are fundamentally ill-posed and suffer from ambiguities and noise. Higher-order image priors encode high-level structural dependencies between pixels and are key to overcoming these problems. However, in general these priors lead to computationally intractable models. This paper addresses the problem of discovering compact representations of higher-order priors which allow efficient inference. We propose a framework for solving this problem that uses a recently proposed representation of higher-order functions which are encoded as lower envelopes of linear functions. Maximum a Posterior inference on our learned models reduces to minimizing a pairwise function of discrete variables. We show that our framework can learn a compact representation that approximates a low curvature shape prior and demonstrate its effectiveness in solving shape inpainting and image segmentation problems. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642327162},
	issn = {03029743},
	doi = {10.1007/978-3-642-32717-9_5},
	author = {Shekhovtsov, Alexander and Kohli, Pushmeet and Carsten Rother}
}
@conference {Shekhovtsov2012a,
	title = {Curvature prior for MRF-based segmentation and shape inpainting},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7476 LNCS},
	year = {2012},
	pages = {41{\textendash}51},
	abstract = {Most image labeling problems such as segmentation and image reconstruction are fundamentally ill-posed and suffer from ambiguities and noise. Higher-order image priors encode high-level structural dependencies between pixels and are key to overcoming these problems. However, in general these priors lead to computationally intractable models. This paper addresses the problem of discovering compact representations of higher-order priors which allow efficient inference. We propose a framework for solving this problem that uses a recently proposed representation of higher-order functions which are encoded as lower envelopes of linear functions. Maximum a Posterior inference on our learned models reduces to minimizing a pairwise function of discrete variables. We show that our framework can learn a compact representation that approximates a low curvature shape prior and demonstrate its effectiveness in solving shape inpainting and image segmentation problems. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642327162},
	issn = {03029743},
	doi = {10.1007/978-3-642-32717-9_5},
	url = {www.research.microsoft.com/vision/cambridge http://www.cs.ucl.ac.uk/staff/V.Kolmogorov/papers/StereoSegmentation_PAMI06.pdf\%5Cnpapers3://publication/uuid/F008E9F4-510D-4478-A3C0-1BFB22F6AEA0},
	author = {Shekhovtsov, Alexander and Kohli, Pushmeet and Carsten Rother}
}
@conference {Shekhovtsov2012b,
	title = {Curvature prior for MRF-based segmentation and shape inpainting},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {7476 LNCS},
	year = {2012},
	month = {sep},
	pages = {41{\textendash}51},
	abstract = {Most image labeling problems such as segmentation and image reconstruction are fundamentally ill-posed and suffer from ambiguities and noise. Higher-order image priors encode high-level structural dependencies between pixels and are key to overcoming these problems. However, in general these priors lead to computationally intractable models. This paper addresses the problem of discovering compact representations of higher-order priors which allow efficient inference. We propose a framework for solving this problem that uses a recently proposed representation of higher-order functions which are encoded as lower envelopes of linear functions. Maximum a Posterior inference on our learned models reduces to minimizing a pairwise function of discrete variables. We show that our framework can learn a compact representation that approximates a low curvature shape prior and demonstrate its effectiveness in solving shape inpainting and image segmentation problems. {\textcopyright} 2012 Springer-Verlag.},
	isbn = {9783642327162},
	issn = {03029743},
	doi = {10.1007/978-3-642-32717-9_5},
	url = {http://arxiv.org/abs/1109.1480},
	author = {Shekhovtsov, Alexander and Kohli, Pushmeet and Carsten Rother}
}
@conference {kausler_12_discrete,
	title = {A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness},
	booktitle = {ECCV 2012. Proceedings},
	volume = {7574},
	year = {2012},
	note = {1},
	pages = {144-157},
	doi = {10.1007/978-3-642-33712-3_11},
	author = {Bernhard X. Kausler and Schiegg, M. and Bj{\"o}rn Andres and Lindner, M. and Ullrich K{\"o}the and Leitte, H. and Wittbrodt, J. and Hufnagel, L. and Fred A. Hamprecht}
}
@article {savchynskyy_12_efficient,
	title = {Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing},
	journal = {UAI. Proceedings},
	year = {2012},
	note = {1},
	pages = {746-755},
	author = {Savchynskyy, B. and Schmidt, S. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {SavchynskyyUAI2012,
	title = {Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing},
	booktitle = {UAI 2012},
	year = {2012},
	note = {in press},
	author = {Savchynskyy, Bogdan and Schmidt, Stefan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@techreport {krall2012,
	title = {Gas Exchange Test Campaign at the Kyoto High Speed Wind-Wave Tank 2011},
	year = {2012},
	institution = {Institut f{\"u}r Umweltphysik, Universit{\"a}t Heidelberg},
	author = {Kerstin Ellen Krall}
}
@conference {andres_12_globally,
	title = {Globally Optimal Closed-Surface Segmentation for Connectomics},
	booktitle = {ECCV 2012. Proceedings, Part 3},
	number = {7574},
	year = {2012},
	note = {1},
	pages = {778-791},
	doi = {10.1007/978-3-642-33712-3_56},
	author = {Bj{\"o}rn Andres and Thorben Kr{\"o}ger and K. L. Briggmann and Denk, W. and Norogod, N. and G. W. Knott and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@incollection {nair2012,
	title = {Ground truth for evaluating time of flight imaging},
	volume = {8200},
	year = {2012},
	pages = {52--74},
	publisher = {Springer},
	doi = {10.1007/978-3-642-44964-2_4},
	author = {Nair, Rahul and Stephan Meister and Lambers, Martin and Balda, Michael and Hofmann, Hannes and Andreas Kolb and Daniel Kondermann and Bernd J{\"a}hne}
}
@conference {nair_12_high,
	title = {High Accuracy TOF and Stereo Sensor Fusion at Interactive Rates},
	booktitle = {submitted to ECCV 2012},
	volume = {7584},
	year = {2012},
	note = {1},
	pages = {1-11},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-33868-7_1},
	author = {Nair, R. and Frank Lenzen and Sch{\"a}fer, H. and Stephan Meister and Christoph S. Garbe and Daniel Kondermann}
}
@conference {Nair2012highaccuracy,
	title = {High accuracy TOF and stereo sensor fusion at interactive rates},
	booktitle = {Computer Vision--ECCV 2012. Workshops and Demonstrations},
	year = {2012},
	pages = {1--11},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	author = {Nair, Rahul and Frank Lenzen and Stephan Meister and Sch{\"a}fer, Henrik and Christoph S. Garbe and Daniel Kondermann}
}
@conference {Nair2012highaccuracy,
	title = {High accuracy TOF and stereo sensor fusion at interactive rates},
	booktitle = {Computer Vision{\textendash}ECCV 2012. Workshops and Demonstrations},
	year = {2012},
	pages = {1{\textendash}11},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	author = {Nair, Rahul and Lenzen, Frank and Meister, Stephan and Sch{\"a}fer, Henrik and Garbe, Christoph S. and Kondermann, Daniel}
}
@conference {andres_12_lazy,
	title = {The Lazy Flipper: Efficient Depth-Limited Exhaustive Search in Discrete Graphical Models},
	booktitle = {Computer Vision - {ECCV} 2012 - 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part {VII}},
	year = {2012},
	doi = {10.1007/978-3-642-33786-4_12},
	url = {http://dx.doi.org/10.1007/978-3-642-33786-4_12},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Andres12,
	title = {The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models},
	booktitle = {ECCV 2012},
	year = {2012},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Andres12,
	title = {The Lazy Flipper: Efficient Depth-limited Exhaustive Search in Discrete Graphical Models},
	booktitle = {ECCV 2012},
	year = {2012},
	author = {Bj{\"o}rn Andres and J{\"o}rg Hendrik Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {fiaschi_12_learning,
	title = {Learning to Count with Regression Forest and Structured Labels},
	journal = {ICPR 2012. Proceedings},
	year = {2012},
	note = {1},
	pages = {2685-2688},
	author = {Fiaschi, L. and Nair, R. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {andres2012opengm,
	title = {OpenGM: A C++ Library for Discrete Graphical Models},
	journal = {ArXiv e-prints},
	year = {2012},
	note = {Projectpage: http://hci.iwr.uni-heidelberg.de/opengm2/},
	author = {Bj{\"o}rn Andres and Thorsten Beier and Kappes, J{\"o}rg H.}
}
@article {meister_12_outdoor,
	title = {Outdoor Stereo Camera System for the Generation of Real-World Benchmark
Data Sets},
	journal = {Optical Engineering},
	volume = {51},
	year = {2012},
	pages = {021107-1},
	doi = {10.1117/1.OE.51.2.021107},
	author = {Stephan Meister and Bernd J{\"a}hne and Daniel Kondermann}
}
@article {meister2012,
	title = {Outdoor stereo camera system for the generation of real-world benchmark data sets},
	journal = {Opt. Eng.},
	volume = {51},
	year = {2012},
	pages = {021107},
	doi = {10.1117/1.OE.51.2.021107},
	author = {Stephan Meister and Bernd J{\"a}hne and Daniel Kondermann}
}
@article {renard_12_overcoming,
	title = {Overcoming species boundaries in peptide identification with BICEPS},
	journal = {Molecular and Cellular Proteomics},
	volume = {11},
	year = {2012},
	doi = {10.1074/mcp.M111.014167},
	author = {B. Y. Renard and Xu, B. and Kirchner, M. and Zickmann, F. and Winter, D. and Korten, S. and Brattig, N. and Tzur, A. and Fred A. Hamprecht and Steen, H.}
}
@conference {kraeuter2012,
	title = {Partitioning of the Trasfer Resistance between Air and Water},
	booktitle = {SOLAS Open Science Conference, Washington State, USA},
	year = {2012},
	doi = {10.5281/zenodo.12328},
	author = {Christine Kr{\"a}uter and Kerstin E. Richter and Evridiki Mesarchaki and Roland Rocholz and Jonathan Williams and Bernd J{\"a}hne}
}
@conference {kondermann_12_on,
	title = {On Performance Analysis of Optical Flow Algorithms},
	booktitle = {Outdoor and Large-Scale Real-Worls Scene Analysis, Dagstuhl-Workshop
2011},
	volume = {LNCS},
	number = {7474},
	year = {2012},
	pages = {329-355},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-34091-8_15},
	author = {Daniel Kondermann and Steffen Abraham and W. F{\"o}rstner and Gehrig, S. and Imiya, A. and Bernd J{\"a}hne and Klose, F. and Magor, M. and Helmut Mayer and Mester, R. and Pajdla, T. and Reulke, R. and Zimmer, H.}
}
@article {lou_12_quality,
	title = {Quality Classification of Microscopic Imagery with Weakly Supervised Learning},
	journal = {MICCAI-MLMI. Proceedings},
	year = {2012},
	note = {1},
	pages = {176-183},
	doi = {10.1007/978-3-642-35428-1_22},
	author = {Lou, X. and Fiaschi, L. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {straehle_12_seeded,
	title = {Seeded watershed cut uncertainty estimators for guided interactive segmentation},
	booktitle = {CVPR 2012. Proceedings},
	year = {2012},
	note = {1},
	pages = {765 - 772},
	doi = {10.1109/CVPR.2012.6247747},
	author = {Christoph N. Straehle and Ullrich K{\"o}the and Briggman, K. and Denk, W. and Fred A. Hamprecht}
}
@article {Kohli2012,
	title = {User-centric learning and evaluation of interactive segmentation systems},
	journal = {International Journal of Computer Vision},
	volume = {100},
	number = {3},
	year = {2012},
	month = {dec},
	pages = {261{\textendash}274},
	abstract = {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. {\textcopyright} 2012 The Author(s).},
	keywords = {Image segmentation, Interactive systems, Learning},
	issn = {09205691},
	doi = {10.1007/s11263-012-0537-4},
	author = {Kohli, Pushmeet and Nickisch, Hannes and Carsten Rother and Rhemann, Christoph}
}
@conference {kiefhaber2012,
	title = {Water Surface Topography Measurements with the Reflective Stereo Slope Gauge},
	booktitle = {AGU Ocean Science Meeting 2012, Salt Lake City},
	year = {2012},
	doi = {10.5281/zenodo.12333},
	author = {Daniel Kiefhaber and Julia Schaper and Roland Rocholz and Christopher J. Zappa and William E. Asher and Jessup, A.T. and Bernd J{\"a}hne}
}
@conference {meister_12_when,
	title = {When Can We Use KinectFusion for Ground Truth Acquisition?},
	booktitle = {Workshop on Color-Depth Camera Fusion in Robotics, IEEE International
Conference on Intelligent Robots and Systems},
	year = {2012},
	note = {1},
	author = {Stephan Meister and Izadi, S. and Kohli, P. and H{\"a}mmerle, M. and Carsten Rother and Daniel Kondermann}
}
@article {Meister2012,
	title = {When can we use KinectFusion for ground truth acquisition?},
	journal = {Proc Workshop on \ldots},
	year = {2012},
	pages = {3{\textendash}8},
	abstract = {Abstract{\textemdash}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},
	url = {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},
	author = {Meister, Stephan and Izadi, Shahram and Kohli, Pushmeet and M H{\"a}mmerle}
}
@mastersthesis {kimmich2011,
	year = {2011},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Kimmich, Dominikus}
}
@conference {schimpf2011b,
	title = {The 2009 SOPRAN active thermography pilot experiment in the Baltic Sea},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {358--367},
	doi = {10.5281/zenodo.14956},
	url = {http://hdl.handle.net/2433/156156},
	author = {Uwe Schimpf and Leila Nagel and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@booklet {kondermann2011,
	title = {Alles im Fluss --- Optischer Fluss f{\"u}r industrielle Anwendungen},
	year = {2011},
	url = {http://www.gitverlag.com/de/print/4/18/issues/2009/3381.html},
	author = {Daniel Kondermann}
}
@mastersthesis {kraeuter2011a,
	title = {Aufteilung des Transferwiderstands zwischen Luft und Wasser beim Austausch fl{\"u}chtiger Substanzen mittlerer L{\"o}slichkeit zwischen Ozean und Atmosph{\"a}re},
	year = {2011},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {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.},
	url = {http://www.ub.uni-heidelberg.de/archiv/13010},
	author = {Christine Kr{\"a}uter}
}
@article {kreshuk_11_automated2,
	title = {Automated Detection and Segmentation of Synaptic Contacts in Nearly Isotropic Serial Electron Microscopy Images},
	journal = {PLoS ONE},
	volume = {6 (10)},
	year = {2011},
	doi = {10.1371/journal.pone.0024899},
	author = {Anna Kreshuk and Christoph N. Straehle and Christoph Sommer and Ullrich K{\"o}the and Cantoni, M. and G. W. Knott and Fred A. Hamprecht}
}
@conference {kreshuk_11_automated,
	title = {Automated Segmentation of Synapses in 3D EM Data},
	booktitle = {Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011).
Proceedings},
	year = {2011},
	note = {1},
	pages = {220-223},
	doi = {10.1109/ISBI.2011.5872392},
	author = {Anna Kreshuk and Christoph N. Straehle and Christoph Sommer and Ullrich K{\"o}the and G. W. Knott and Fred A. Hamprecht}
}
@conference {straehle_11_carving,
	title = {Carving: Scalable Interactive Segmentation of Neural Volume Electron Microscopy Images},
	booktitle = {MICCAI 2011, Proceedings.},
	volume = {6891},
	year = {2011},
	note = {1},
	pages = {653-660},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-23623-5_82},
	author = {Christoph N. Straehle and Ullrich K{\"o}the and G. W. Knott and Fred A. Hamprecht}
}
@conference {rocholz2011a,
	title = {Combined visualization of wind waves and water surface temperature},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {496--506},
	doi = {10.5281/zenodo.14957},
	url = {http://hdl.handle.net/2433/156156},
	author = {Roland Rocholz and Sven Wanner and Uwe Schimpf and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@conference {kraeuter2011,
	title = {A comparative lab study of tansfer velocities of volatile tracers with widely varying solubilities},
	booktitle = {DPG Fr{\"u}hjahrstagung Dresden, Fachverband Umweltphysik},
	year = {2011},
	abstract = {Die L{\"o}slichkeit einer fl{\"u}chtigen Substanz in Wasser hat einen entscheidenden Einfluss auf den Gasaustausch zwischen Ozean und Atmosph{\"a}re. Bei Stoffen mit einer sehr hohen L{\"o}slichkeit wird der Austausch durch Diffusion in der luftseitigen Grenzschicht kontrolliert und bei solchen mit einer sehr niedrigen L{\"o}slichkeit 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 ber{\"u}cksichtigen. In einem ersten Laborexperiment am Aeolotron, einem ringf{\"o}rmigen Wind-Wellen-Kanal, wurden die Transferwiderst{\"a}nde vieler Gase mit unterschiedlichen L{\"o}slichkeiten bei verschiedenen Windgeschwindigkeiten (1,4 m/s bis 8,4 m/s) bestimmt. Die dimensionslosen L{\"o}slichkeiten der verwendeten Gase deckten einen Bereich von 5 Gr{\"o}{\ss}enordnungen 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 L{\"o}slichkeit in einen luftseitigen und wasserseitigen Teil konnte nachgewiesen werden.},
	doi = {10.5281/zenodo.12327},
	url = {http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/29},
	author = {Christine Kr{\"a}uter and Kerstin E. Richter and Bernd J{\"a}hne and Evridiki Mesarchaki and Jonathan Williams}
}
@conference {kaster_11_comparative,
	title = {Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations},
	booktitle = {LNCS},
	volume = {LNCS 6533},
	year = {2011},
	note = {1},
	pages = {74-85},
	publisher = {Springer, Heidelberg},
	organization = {Springer, Heidelberg},
	doi = {10.1007/978-3-642-18421-5_8},
	author = {F. O. Kaster and M.-A. Weber and Fred A. Hamprecht},
	editor = {Bjoern H. Menze and Bjoern H. Menze and Langs, G. and Criminisi, A. and Tu, Z.}
}
@conference {Nowozin2011a,
	title = {Decision tree fields},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2011},
	pages = {1668{\textendash}1675},
	abstract = {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. {\textcopyright} 2011 IEEE.},
	isbn = {9781457711015},
	doi = {10.1109/ICCV.2011.6126429},
	author = {Nowozin, Sebastian and Carsten Rother and Bagon, Shai and Sharp, Toby and Yao, Bangpeng and Kohli, Pushmeet}
}
@conference {lou_11_deltr,
	title = {DELTR: Digital Embryo Lineage Tree Reconstructor},
	booktitle = {Eighth IEEE International Symposium on Biomedical Imaging (ISBI). Proceedings},
	year = {2011},
	note = {1},
	pages = {1557-1560},
	doi = {10.1109/ISBI.2011.5872698},
	author = {Lou, X. and F. O. Kaster and Lindner, M. and Bernhard X. Kausler and Ullrich K{\"o}the and H{\"o}ckendorf, B. and Wittbrodt, J. and J{\"a}nicke, H. and Fred A. Hamprecht}
}
@mastersthesis {kimmich_11_automatisches,
	title = {Ein automatisches Lichtfeld-basiertes Inspektionsverfahren f\FCr Elektronikstecker},
	year = {2011},
	school = {University of Heidelberg},
	author = {Kimmich, D.}
}
@conference {schmidt_11_evaluation,
	title = {Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy
Minimization},
	booktitle = {EMMCVPR 2011},
	volume = {6819},
	year = {2011},
	note = {1},
	pages = {89-103},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-23094-3_7},
	author = {Schmidt, S. and Savchynskyy, B. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Schmidt11,
	title = {Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization},
	booktitle = {EMMCVPR},
	volume = {6819},
	year = {2011},
	pages = {89-103},
	publisher = {Springer},
	organization = {Springer},
	author = {Schmidt, Stefan and Savchynskyy, Bogdan and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Schmidt11,
	title = {Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization},
	booktitle = {EMMCVPR},
	series = {LNCS},
	volume = {6819},
	year = {2011},
	pages = {89-103},
	publisher = {Springer},
	organization = {Springer},
	author = {Stefan Schmidt and Bogdan Savchynskyy and J{\"o}rg Hendrik Kappes and Christoph Schn{\"o}rr}
}
@conference {mischler2011,
	title = {Experimental setup for the investigation of bubble mediated gas exchange},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {238--248},
	doi = {10.5281/zenodo.14953},
	url = {http://hdl.handle.net/2433/156156},
	author = {Wolfgang Mischler and Roland Rocholz and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@conference {kappes_11_globally,
	title = {Globally Optimal Image Partitioning by Multicuts},
	booktitle = {EMMCVPR},
	year = {2011},
	note = {1},
	pages = {31-44},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-23094-3_3},
	author = {J{\"o}rg H. Kappes and Speth, M. and Bj{\"o}rn Andres and Reinelt, G. and Christoph Schn{\"o}rr}
}
@conference {Kappes11,
	title = {Globally Optimal Image Partitioning by Multicuts},
	booktitle = {EMMCVPR},
	year = {2011},
	publisher = {Springer},
	organization = {Springer},
	author = {J{\"o}rg H. Kappes and Speth, Markus and Bj{\"o}rn Andres and Reinelt, Gerhard and Christoph Schn{\"o}rr}
}
@conference {Kappes11,
	title = {Globally Optimal Image Partitioning by Multicuts},
	booktitle = {EMMCVPR},
	year = {2011},
	publisher = {Springer},
	organization = {Springer},
	author = {J{\"o}rg Hendrik Kappes and Markus Speth and Bj{\"o}rn Andres and Gerhard Reinelt and Christoph Schn{\"o}rr}
}
@article {nair_11_high,
	title = {High Precision TOF-guided Depth from Stereo for Room Scanning},
	journal = {CVMP, Proceedings.},
	year = {2011},
	note = {1},
	author = {Nair, R. and Daniel Kondermann}
}
@conference {sommer_11_ilastik,
	title = {ilastik: Interactive Learning and Segmentation Toolkit},
	booktitle = {Eighth IEEE International Symposium on Biomedical Imaging (ISBI 2011).Proceedings},
	year = {2011},
	note = {1},
	pages = {230-233},
	doi = {10.1109/ISBI.2011.5872394},
	author = {Christoph Sommer and Christoph N. Straehle and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@phdthesis {kaster_11_image,
	title = {Image Analysis for the Life Sciences - Computer-assisted Tumor Diagnostics and Digital Embryomics},
	year = {2011},
	publisher = {University of Heidelberg},
	author = {F. O. Kaster}
}
@conference {kiefhaber2011b,
	title = {Improved optical instrument for the measurement of water wave statistics in the field},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {524--534},
	doi = {10.5281/zenodo.14958},
	url = {http://hdl.handle.net/2433/156156},
	author = {Daniel Kiefhaber and Roland Rocholz and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@phdthesis {Kappes-diss-2011,
	title = {Inference on Highly-Connected Discrete Graphical Models with Applications to Visual Object Recognition},
	year = {2011},
	publisher = {Ruprecht-Karls-Universit{\"a}t Heidelberg, Faculty of Mathematics and Computer Sciences},
	type = {phddoctoral thesis},
	address = {Heidelberg, Germany},
	url = {http://www.ub.uni-heidelberg.de/archiv/11872/},
	author = {J{\"o}rg Hendrik Kappes}
}
@article {Rother2011,
	title = {Interactive foreground extraction using graph cut},
	journal = {Advances in Markov \ldots},
	year = {2011},
	pages = {1{\textendash}20},
	abstract = {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},
	url = {http://research.microsoft.com/pubs/147408/rotheretalmrfbook-grabcut.pdf},
	author = {Carsten Rother and Kolmogorov, Vladimir}
}
@conference {richter2011a,
	title = {A laboratory study of the Schmidt number dependency of air-water gas transfer},
	booktitle = {Gas Transfer at Water Surfaces 2010},
	year = {2011},
	pages = {322--332},
	doi = {10.5281/zenodo.14955},
	url = {http://hdl.handle.net/2433/156156},
	author = {Kerstin E. Richter and Bernd J{\"a}hne},
	editor = {S. Komori and W. R. McGilles and R. Kurose}
}
@conference {kiefhaber2011a,
	title = {Mean square slope measurements in the field with the reflective stereo slope gauge},
	booktitle = {EGU General Assembly, Vienna},
	year = {2011},
	abstract = {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 J{\"a}hne (1992) and comprises a stereo camera setup, artificial light sources and a wind following system. The measurement principle is similar to Cox \& Munk{\textquoteright}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.},
	doi = {10.5281/zenodo.12331},
	author = {Daniel Kiefhaber and Roland Rocholz and Julia Schaper and G{\"u}nther Balschbach and Bernd J{\"a}hne}
}
@conference {kiefhaber2011,
	title = {Measurement of ocean wave statistics with the reflective stereo slope gauge},
	booktitle = {DPG Fr{\"u}hjahrstagung Dresden, Fachverband Umweltphysik},
	year = {2011},
	abstract = {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 J{\"a}hne (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 \& Munk{\textquoteright}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},
	url = {http://www.dpg-verhandlungen.de/year/2011/conference/dresden/part/up/session/1/contribution/30},
	author = {Daniel Kiefhaber and Roland Rocholz and G{\"u}nther Balschbach and Julia Schaper and Bernd J{\"a}hne}
}
@conference {Vicente2011,
	title = {Object cosegmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2011},
	pages = {2217{\textendash}2224},
	abstract = {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 "something" has to be an object, and (2) the "similarity" 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. {\textcopyright} 2011 IEEE.},
	isbn = {9781457703942},
	issn = {10636919},
	doi = {10.1109/CVPR.2011.5995530},
	author = {Vicente, Sara and Carsten Rother and Kolmogorov, Vladimir}
}
@conference {Bleyer2011,
	title = {Object stereo Joint stereo matching and object segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2011},
	pages = {3081{\textendash}3088},
	abstract = {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{\textquoteright}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. {\textcopyright} 2011 IEEE.},
	isbn = {9781457703942},
	issn = {10636919},
	doi = {10.1109/CVPR.2011.5995581},
	author = {Bleyer, Michael and Carsten Rother and Kohli, Pushmeet and Scharstein, Daniel and Sinha, Sudipta}
}
@article {kaster_11_object-oriented,
	title = {An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements},
	journal = {Computer Science - Research and Development},
	volume = {26},
	year = {2011},
	note = {1},
	pages = {65-85},
	doi = {10.1007/s00450-010-0143-z},
	author = {F. O. Kaster and Merkel, B. and Nix, O. and Fred A. Hamprecht}
}
@conference {menze_11_on,
	title = {On oblique random forests},
	booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2011. Proceedings.},
	year = {2011},
	note = {1},
	pages = {453-469},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-23783-6_29},
	author = {Bjoern H. Menze and B. Michael Kelm and Splitthoff, N. and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Lellmann2011,
	title = {Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem},
	booktitle = {Energy Min. Meth. Comp. Vis. Patt. Recogn.},
	volume = {6819},
	year = {2011},
	pages = {132--146},
	publisher = {Springer},
	organization = {Springer},
	author = {Lellmann, Jan and Frank Lenzen and Christoph Schn{\"o}rr},
	editor = {Boykov, Y. and Kahl, F. and Schmidt, F. R. and Lempitsky, V. F.}
}
@conference {Lellmann2011,
	title = {Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem},
	booktitle = {Energy Min. Meth. Comp. Vis. Patt. Recogn.},
	series = {LNCS},
	volume = {6819},
	year = {2011},
	pages = {132{\textendash}146},
	publisher = {Springer},
	organization = {Springer},
	author = {Lellmann, Jan and Lenzen, Frank and Christoph Schn{\"o}rr},
	editor = {Boykov, Y. and Kahl, F. and Lempitsky, V. F. and Schmidt, F. R.}
}
@conference {monroy:SCCH:2011,
	title = {Parametric Object Detection for Iconographic Analysis},
	booktitle = {Scientific Computing \& Cultural Heritage},
	year = {2011},
	url = {http://www.academia.edu/9439693/Parametric_Object_Detection_for_Iconographic_Analysis},
	author = {Monroy, A. and Thorben Kr{\"o}ger and Arnold, M. and Bj{\"o}rn Ommer}
}
@conference {andres_11_probabilistic,
	title = {Probabilistic Image Segmentation with Closedness Constraints},
	booktitle = {ICCV, Proceedings},
	year = {2011},
	note = {1},
	pages = {2611 - 2618},
	doi = {10.1109/ICCV.2011.6126550},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Andres11,
	title = {Probabilistic Image Segmentation with Closedness Constraints},
	booktitle = {Proceedings of ICCV},
	year = {2011},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Andres11,
	title = {Probabilistic Image Segmentation with Closedness Constraints},
	booktitle = {Proceedings of ICCV},
	year = {2011},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Thorsten Beier and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@conference {Pletscher2011,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {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. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
@conference {Pletscher2011a,
	title = {Putting MAP back on the map},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6835 LNCS},
	year = {2011},
	pages = {111{\textendash}121},
	abstract = {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. {\textcopyright} 2011 Springer-Verlag.},
	isbn = {9783642231223},
	issn = {03029743},
	doi = {10.1007/978-3-642-23123-0_12},
	author = {Pletscher, Patrick and Nowozin, Sebastian and Kohli, Pushmeet and Carsten Rother}
}
@conference {Gehler2011,
	title = {Recovering intrinsic images with a global sparsity prior on reflectance},
	booktitle = {Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011},
	year = {2011},
	abstract = {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.},
	isbn = {9781618395993},
	author = {Gehler, Peter Vincent and Carsten Rother and Kiefel, Martin and Zhang, Lumin and Sch{\"o}lkopf, Bernhard}
}
@article {hanselmann_11_sima,
	title = {SIMA: Simultaneous Multiple Alignment of LC/MS Peak Lists},
	journal = {Bioinformatics},
	volume = {27 (7)},
	year = {2011},
	note = {1},
	pages = {987-993},
	doi = {10.1093/bioinformatics/btr051},
	author = {Hanselmann, M. and Bj{\"o}rn Voss and B. Y. Renard and Lindner, M. and Ullrich K{\"o}the and Kirchner, M. and Fred A. Hamprecht}
}
@article {savchynskyy_11_study_supplement,
	title = {A Study of Nesterov{\textquoteright}s Scheme for Lagrangian Decomposition and MAP
Labeling},
	journal = {IEEE International Conference on Computer Vision and Pattern Recognition
(CVPR), accepted as oral presentation},
	year = {2011},
	note = {1},
	pages = {1817 - 1823},
	doi = {10.1109/CVPR.2011.5995652},
	author = {Savchynskyy, B. and J{\"o}rg H. Kappes and Schmidt, S. and Christoph Schn{\"o}rr}
}
@conference {Savchynskyy-CVPR2011,
	title = {A Study of Nesterov{\textquoteright}s Scheme for Lagrangian Decomposition and MAP Labeling},
	booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2011},
	author = {Savchynskyy, B. and J{\"o}rg H. Kappes and Schmidt, S. and Christoph Schn{\"o}rr}
}
@conference {Savchynskyy-CVPR2011,
	title = {A Study of Nesterov{\textquoteright}s Scheme for Lagrangian Decomposition and MAP Labeling},
	booktitle = {IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2011},
	author = {Savchynskyy, B. and Kappes, J. H. and Schmidt, S. and Schn{\"o}rr, C.}
}
@conference {maier-hein2011,
	title = {Towards mobile augmented reality for on-patient visualization of medical images},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin (2011)},
	year = {2011},
	pages = {389--393},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-642-19335-4_80},
	author = {Lena Maier-Hein and A. M. Franz and M. Fangerau and Schmidt, Mirko and Alexander Seitel and Sven Mersmann and T. Kilgus and A. Groch and K. Yung and Thiago R. dos Santos and Hans-Peter Meinzer},
	editor = {H. Handels and J. Ehrhardt and Hans-Peter Meinzer and T. Tolxdorff and T. M. Deserno}
}
@article {kelm_11_using,
	title = {Using Spatial Prior Knowledge in the Spectral Fitting of Magnetic Resonance Spectroscopic Images},
	journal = {NMR in Biomedicine},
	volume = {25(1)},
	year = {2011},
	note = {1},
	pages = {1-13},
	doi = {10.1002/nbm.1704},
	author = {B. Michael Kelm and F. O. Kaster and Henning, A. and M.-A. Weber and Bachert, P. and B{\"o}singer, P. and Fred A. Hamprecht and Bjoern H. Menze}
}
@conference {becker_11_variational,
	title = {Variational Recursive Joint Estimation of Dense Scene Structure and
Camera Motion from Monocular High Speed Traffic Sequences},
	booktitle = {2011 IEEE International Conference on Computer Vision ICCV},
	year = {2011},
	note = {1},
	pages = {1692-1699},
	doi = {10.1109/ICCV.2011.6126432},
	author = {Florian Becker and Frank Lenzen and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr},
	editor = {Metaxas, D. N. and Quan, L. and Van Gool, L. J. and Sanfeliu, A.}
}
@conference {Becker-et-al-11a,
	title = {Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	pages = {1692 -- 1699},
	doi = {10.1109/ICCV.2011.6126432},
	author = {Florian Becker and Frank Lenzen and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Becker-et-al-11a,
	title = {Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences},
	booktitle = {2011 IEEE International Conference on Computer Vision (ICCV)},
	year = {2011},
	pages = {1692 {\textendash} 1699},
	doi = {10.1109/ICCV.2011.6126432},
	author = {Florian Becker and Lenzen, Frank and Kappes, J{\"o}rg H. and Christoph Schn{\"o}rr}
}
@mastersthesis {kunz2011,
	title = {Visualisierung der wasserseitigen Massengrenzschicht beim konvektionsgetriebenen Gasaustausch mithilfe einer Lumineszenzmethode und Thermografie},
	year = {2011},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Jakob Kunz}
}
@article {pfannmoeller_11_visualizing,
	title = {Visualizing a homogeneous blend in bulk heterojunction polymer solar cells by analytical electron microscopy},
	journal = {Nano Letters},
	volume = {11},
	year = {2011},
	pages = {3099-3107},
	doi = {10.1021/nl201078t},
	author = {Pfannm{\"o}ller, M. and Fl{\"u}gge, H. and Benner, G. and Wacker, I. and Christoph Sommer and Hanselmann, M. and Schmale, S. and Schmidt, H. and Fred A. Hamprecht and Rabe, T. and Kowalsky, W. and Schr{\"o}der, R.}
}
@article {kreshuk_10_automated,
	title = {Automated detection and analysis of bimodal isotope peak distribution in H/D exchange mass spectrometry using HeXicon},
	journal = {International Journal of Mass Spectrometry},
	volume = {302},
	year = {2010},
	note = {1},
	pages = {125-131},
	doi = {10.1016/j.ijms.2010.08.025},
	author = {Anna Kreshuk and Stankiewicz, M. and Lou, X. and Kirchner, M. and Fred A. Hamprecht and Mayer, M. P.}
}
@article {kirchner_10_computational,
	title = {Computational Protein Profile Similarity Screening for Quantitative Mass Spectrometry Experiments},
	journal = {Bioinformatics},
	volume = {26 (1)},
	year = {2010},
	note = {1},
	pages = {77-83},
	doi = {10.1093/bioinformatics/btp607},
	author = {Kirchner, M. and B. Y. Renard and Ullrich K{\"o}the and Pappin, D. J. and Fred A. Hamprecht and Judith A. J. Steen and Steen, H.}
}
@conference {Vicente2010,
	title = {Cosegmentation revisited: Models and optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6312 LNCS},
	number = {PART 2},
	year = {2010},
	pages = {465{\textendash}479},
	abstract = {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 "reward" 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. {\textcopyright} 2010 Springer-Verlag.},
	isbn = {3642155510},
	issn = {16113349},
	doi = {10.1007/978-3-642-15552-9_34},
	author = {Vicente, Sara and Kolmogorov, Vladimir and Carsten Rother}
}
@article {lou_10_deuteration,
	title = {Deuteration Distribution Estimation with Improved Sequence Coverage for HX/MS Experiments},
	journal = {Bioinformatics},
	volume = {26(12)},
	year = {2010},
	note = {1},
	pages = {1535-1541},
	doi = {10.1093/bioinformatics/btq165},
	author = {Lou, X. and Kirchner, M. and B. Y. Renard and Ullrich K{\"o}the and Graf, C. and Lee, C. and Judith A. J. Steen and Steen, H. and Mayer, M. P. and Fred A. Hamprecht}
}
@mastersthesis {kiefhaber2010a,
	title = {Development of a Reflective Stereo Slope Gauge for the Measurement of Ocean Surface Wave Slope Statistics},
	year = {2010},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/12673/},
	author = {Daniel Kiefhaber}
}
@conference {andres_10_empirical,
	title = {An Empirical Comparison of Inference Algorithms for Graphical Models
with Higher Order Factors Using OpenGM},
	booktitle = {Pattern Recognition, Proc.~32th DAGM Symposium},
	number = {6376},
	year = {2010},
	note = {1},
	pages = {353-362},
	doi = {10.1007/978-3-642-15986-2_36},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Christoph Schn{\"o}rr and Fred A. Hamprecht}
}
@conference {Kappes-DAGM2010,
	title = {An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM},
	booktitle = {Pattern Recognition, Proc.~32th DAGM Symposium},
	year = {2010},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Christoph Schn{\"o}rr and Fred A. Hamprecht}
}
@article {renard_10_estimating,
	title = {Estimating the Confidence of Peptide Identifications without Decoy Databases},
	journal = {Analytical Chemistry},
	year = {2010},
	note = {1},
	pages = {4314-4318},
	doi = {10.1021/ac902892j},
	author = {B. Y. Renard and Timm, W. and Kirchner, M. and Judith A. J. Steen and Fred A. Hamprecht and Steen, H.}
}
@mastersthesis {kroeger_10_exploratory,
	title = {Exploratory and computational analysis of huge volume images for connectomics},
	year = {2010},
	note = {1},
	school = {University of Heidelberg},
	author = {Thorben Kr{\"o}ger}
}
@article {boerner_10_from,
	title = {From experimental setup to bioinformatics: An RNAi screening platform to identify host factors involved in HIV-1 replication},
	journal = {Biotechnology Journal},
	volume = {5},
	year = {2010},
	note = {1},
	pages = {39-49},
	doi = {10.1002/biot.200900226},
	author = {B{\"o}rner, K. and Hermle, J. and Christoph Sommer and Brown, N. P. and Knapp, B. and Glass, B. and Torralba, G. and Reymann, J. and Beil, N. and Beneke, J. and Pepperkok, R. and Schneider, R. and Ludwig, T.}
}
@conference {koethe_10_geometric,
	title = {Geometric Analysis of 3D Electron Microscopy Data},
	booktitle = {Proceedings of Workshop on Discrete Geometry and Mathematical Morphology (WADGMM)},
	year = {2010},
	note = {1},
	pages = {22-26},
	author = {Ullrich K{\"o}the and Bj{\"o}rn Andres and Thorben Kr{\"o}ger and Fred A. Hamprecht},
	editor = {Ullrich K{\"o}the and Montanvert, A. and Soille, P.}
}
@article {andres_10_how,
	title = {How to Extract the Geometry and Topology from Very Large 3D Segmentations},
	journal = {ArXiv e-prints},
	year = {2010},
	note = {1},
	url = {http://arxiv.org/abs/1009.6215},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Thorben Kr{\"o}ger and Fred A. Hamprecht}
}
@article {andres_10_lazy,
	title = {The Lazy Flipper: MAP Inference in Higher-Order Graphical Models by Depth-limited Exhaustive Search},
	journal = {ArXiv e-prints},
	year = {2010},
	note = {1},
	url = {http://arxiv.org/abs/1009.4102},
	author = {Bj{\"o}rn Andres and J{\"o}rg H. Kappes and Ullrich K{\"o}the and Fred A. Hamprecht}
}
@article {6395,
	title = {Learning an Interactive Segmentation System - Supplemental Material},
	year = {2010},
	author = {H. Nickisch and C. Rother and P. Kohli and C. Rhemann}
}
@conference {jehle_10_learning,
	title = {Learning of Optimal Illumination for Material Classification},
	booktitle = {Proceedings of the 32nd DAGM Symposium on Pattern Recognition, Darmstadt, Germany},
	number = {6376/2010},
	year = {2010},
	note = {1},
	pages = {563-572},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-15986-2_57},
	author = {Markus Jehle and Christoph Sommer and Bernd J{\"a}hne},
	editor = {Goesele, M. and Roth, S. and Schiele, B. and Schindler, K. and Kuijper, A.}
}
@conference {jehle2010,
	title = {Learning of optimal illumination for material classification},
	booktitle = {Pattern Recognition},
	volume = {6376},
	year = {2010},
	pages = {563--572},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-642-15986-2_57},
	author = {Markus Jehle and Christoph Sommer and Bernd J{\"a}hne},
	editor = {Goesele, Michael and Roth, Stefan and Schiele, Bernt and Schindler, Konrad and Kuijper, Arjan}
}
@article {kirchner_10_mgfp,
	title = {MGFp: An Open Mascot Generic Format Parser Library Implementation},
	journal = {Journal of Proteome Research},
	volume = {9 (5)},
	year = {2010},
	pages = {27622763},
	doi = {10.1021/pr100118f},
	author = {Kirchner, M. and Judith A. J. Steen and Fred A. Hamprecht and Steen, H.}
}
@conference {Rother2010,
	title = {Minimizing sparse higher order energy functions of discrete variables},
	year = {2010},
	pages = {1382{\textendash}1389},
	abstract = {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 {\textquotedblleft}sparse{\textquotedblright}, 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.},
	doi = {10.1109/cvpr.2009.5206739},
	author = {Carsten Rother and Kohli, Pushmeet and Wei Feng and Jiaya Jia}
}
@mastersthesis {kausler_10_modeling,
	title = {Modeling of Spectral Peaks for Mass-Spectrometry-based Proteomics},
	year = {2010},
	note = {1},
	school = {Universities of Karlsruhe and Heidelberg},
	author = {Bernhard X. Kausler}
}
@conference {kappes_10_mrf,
	title = {MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation},
	booktitle = {European Conference on Computer Vision (ECCV)},
	volume = {6313},
	year = {2010},
	note = {1},
	pages = {735--747},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-15558-1_53},
	author = {J{\"o}rg H. Kappes and Schmidt, S. and Christoph Schn{\"o}rr},
	editor = {Daniilidis, K. and Maragos, P. and Paragios, N.}
}
@conference {Kappes-ECCV2010,
	title = {MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation},
	booktitle = {European Conference on Computer Vision (ECCV)},
	volume = {6313},
	year = {2010},
	pages = {735--747},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	author = {J{\"o}rg H. Kappes and Schmidt, S. and Christoph Schn{\"o}rr},
	editor = {Daniilidis, K. and Maragos, P. and Paragios, N.}
}
@conference {Kappes-ECCV2010,
	title = {MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation},
	booktitle = {European Conference on Computer Vision (ECCV)},
	volume = {6313},
	year = {2010},
	pages = {735{\textendash}747},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	author = {Kappes, J. H. and Schmidt, S. and Schn{\"o}rr, C.},
	editor = {Daniilidis, K. and Maragos, P. and Paragios, N.}
}
@conference {kaster_10_object-oriented,
	title = {An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin 2010 -- Algorithmen, Systeme, Anwendungen},
	year = {2010},
	note = {1},
	pages = {97-101},
	publisher = {Springer},
	organization = {Springer},
	author = {F. O. Kaster and Kassemeyer, S. and Merkel, B. and Nix, O. and Fred A. Hamprecht},
	editor = {T. M. Deserno and H. Handels and T. Tolxdorff and Hans-Peter Meinzer}
}
@article {andres_10_runtime,
	title = {Runtime-Flexible Multi-dimensional Views and Arrays for C++98 and C++0x},
	journal = {ArXiv e-prints},
	year = {2010},
	note = {1},
	url = {http://arxiv.org/abs/1008.2909v1},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Thorben Kr{\"o}ger and Fred A. Hamprecht}
}
@conference {Rhemann2010,
	title = {A spatially varying PSF-based prior for alpha matting},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	pages = {2149{\textendash}2156},
	abstract = {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. {\textcopyright}2010 IEEE.},
	isbn = {9781424469840},
	issn = {10636919},
	doi = {10.1109/CVPR.2010.5539894},
	author = {Rhemann, Christoph and Carsten Rother and Kohli, Pushmeet and Gelautz, Margrit}
}
@article {Bergtholdt-et-al-IJCV-2010,
	title = {A Study of Parts-Based Object Class Detection Using Complete Graphs},
	journal = {Int.~J.~Comp.~Vision},
	volume = {87},
	number = {1-2},
	year = {2010},
	pages = {93-117},
	doi = {10.1007/s11263-009-0209-1},
	url = {http://www.springerlink.com/openurl.asp?genre=article\&id=doi:10.1007/s11263-009-0209-1},
	author = {Bergtholdt, Martin and J{\"o}rg H. Kappes and Schmidt, Stefan and Christoph Schn{\"o}rr}
}
@article {Bergtholdt-et-al-IJCV-2010,
	title = {A Study of Parts-Based Object Class Detection Using Complete Graphs},
	journal = {Int. J. Comp. Vision},
	volume = {87},
	number = {1-2},
	year = {2010},
	pages = {93-117},
	doi = {10.1007/s11263-009-0209-1},
	url = {http://www.springerlink.com/openurl.asp?genre=article\&id=doi:10.1007/s11263-009-0209-1},
	author = {Bergtholdt, Martin and Kappes, J{\"o}rg H. and Schmidt, Stefan and Christoph Schn{\"o}rr}
}
@conference {Bleyer2010,
	title = {Surface stereo with soft segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	pages = {1570{\textendash}1577},
	abstract = {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). {\textcopyright}2010 IEEE.},
	isbn = {9781424469840},
	issn = {10636919},
	doi = {10.1109/CVPR.2010.5539783},
	author = {Bleyer, Michael and Carsten Rother and Kohli, Pushmeet}
}
@article {berthe2010,
	title = {Three-dimensional, three-component wall-PIV},
	journal = {Exp. Fluids},
	volume = {48},
	year = {2010},
	pages = {online},
	doi = {10.1007/s00348-009-0777-4},
	author = {A. Berthe and Daniel Kondermann and Christensen, C. and Goubergrits, L. and Christoph S. Garbe and Affeld, K. and U. Kertzscher}
}
@conference {Glocker2010,
	title = {TriangleFlow: Optical flow with triangulation-based higher-order likelihoods},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {6313 LNCS},
	number = {PART 3},
	year = {2010},
	pages = {272{\textendash}285},
	abstract = {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. {\textcopyright} 2010 Springer-Verlag.},
	isbn = {364215557X},
	issn = {16113349},
	doi = {10.1007/978-3-642-15558-1_20},
	url = {http://vision.middlebury.edu/flow/},
	author = {Glocker, Ben and Heibel, T. Hauke and Navab, Nassir and Kohli, Pushmeet and Carsten Rother}
}
@conference {jaehne2010a,
	title = {Wind/wave-tunnel measurements of chemical enhancement of the carbon dioxide gas exchange rate},
	booktitle = {6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010},
	year = {2010},
	note = {abstract},
	doi = {10.5281/zenodo.14928},
	author = {Bernd J{\"a}hne and Kai Degreif and Joachim Kuss}
}
@article {jaeger_09_analysis,
	title = {Analysis of Single-Molecule Fluorescence Spectroscopic Data with a Markov Modulated Poisson Process},
	journal = {ChemPhysChem},
	volume = {10:14},
	year = {2009},
	note = {1},
	pages = {2486-2495},
	doi = {10.1002/cphc.200900331},
	author = {J{\"a}ger, M. and Kiel, A. and Herten, D.-P. and Fred A. Hamprecht}
}
@article {hansen2009bias,
	title = {Bias-correction of regression models: a case study on hERG inhibition},
	journal = {J. Chem. Inf. Model.},
	volume = {49},
	year = {2009},
	pages = {1486{\textendash}1496},
	doi = {http://dx.doi.org/10.1021/ci9000794},
	author = {Hansen, Katja and Rathke, Fabian and Schroeter, Timon and Rast, Georg and Fox, Thomas and Kriegl, Jan M and Mika, Sebastian}
}
@conference {kaster_09_classification,
	title = {Classification of Spectroscopic Images in the DIROlab Environment},
	booktitle = {World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany},
	volume = {25/V},
	number = {25050252},
	year = {2009},
	pages = {252--255},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-03904-1_70},
	author = {F. O. Kaster and B. Michael Kelm and C. M. Zechmann and M.-A. Weber and Fred A. Hamprecht and Nix, O.},
	editor = {D{\"o}ssel, Olaf and Schlegel, Wolfgang C.}
}
@article {menze_09_comparison,
	title = {A Comparison of Random Forest and its Gini Importance with Standard Chemometric Methods for the Feature Selection and Classification of Spectral Data},
	journal = {BMC Bioinformatics},
	volume = {10:213},
	year = {2009},
	note = {1},
	doi = {10.1186/1471-2105-10-213},
	author = {Bjoern H. Menze and B. Michael Kelm and Masuch, R. and Himmelreich, U. and Bachert, P. and Petrich, W. and Fred A. Hamprecht}
}
@conference {keraenen:2009,
	title = {Computational Analysis of Quantitative Changes in Gene Expression and Embryo Morphology between Species},
	booktitle = {Evolution-The Molecular Landscape},
	year = {2009},
	author = {Ker{\"a}nen, S. V. E. and DePace, A. and Luengo Hendriks, C. L. and Fowlkes, C. and Arbelaez, P. and Bj{\"o}rn Ommer and Brox, T. and Henriquez, C. and Wunderlich, Z. and Eckenrode, K. and B. Fischer and Hammonds, A. and Celniker, S. E.}
}
@conference {lellmann_09_convex2,
	title = {Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation},
	booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM 2009)},
	volume = {5567},
	year = {2009},
	note = {1},
	pages = {150-162},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-02256-2_13},
	author = {Lellmann, J. and J{\"o}rg H. Kappes and Yuan, J. and Florian Becker and Christoph Schn{\"o}rr and M{\'o}rken, K. and Lysaker, M.},
	editor = {Tai, X.-C. and Lie, K.-A.}
}
@conference {Lellmann-et-al-09a,
	title = {Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation},
	booktitle = {Scale Space and Variational Methods in Computer Vision (SSVM 2009)},
	volume = {5567},
	year = {2009},
	pages = {150-162},
	publisher = {Springer},
	organization = {Springer},
	author = {Lellmann, J. and J{\"o}rg H. Kappes and Yuan, J. and Florian Becker and Christoph Schn{\"o}rr},
	editor = {Tai, X.-C. and M{\'o}rken, K. and Lie, K.-A. and Lysaker, M.}
}
@article {hering2009,
	title = {Correlated speckle noise in white-light interferometry: theoretical analysis of measurement uncertainty},
	journal = {Appl. Optics},
	volume = {48},
	year = {2009},
	pages = {525--538},
	abstract = {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.},
	doi = {10.1364/AO.48.000525},
	author = {Marco Hering and Klaus K{\"o}rner and Bernd J{\"a}hne}
}
@article {kelm_09_estimating,
	title = {Estimating Kinetic Parameter Maps from Dynamic Contrast-Enhanced MRI using Spatial Prior Knowledge},
	journal = {IEEE Transaction on Medical Imaging},
	volume = {28:10},
	year = {2009},
	note = {1},
	pages = {1534-1547},
	doi = {10.1109/TMI.2009.2019957},
	author = {B. Michael Kelm and Bjoern H. Menze and Nix, O. and C. M. Zechmann and Fred A. Hamprecht}
}
@conference {baehnisch_09_fast,
	title = {Fast and Accurate 3D Edge Detection for Surface Reconstruction},
	booktitle = {Pattern Recognition},
	volume = {5748},
	year = {2009},
	pages = {111-120},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-03798-6},
	author = {B{\"a}hnisch, C. and Stelldinger, P. and Ullrich K{\"o}the}
}
@article {thieke_09_framework,
	title = {A framework and multi-application prototype for integrated radiological diagnostics and radiation therapy},
	journal = {Strahlentherapie und Onkologie},
	volume = {185},
	number = {Suppl. 1},
	year = {2009},
	pages = {81},
	author = {Thieke, C. and Nix, O. and Koehn, A. and Floca, R. and van Straaten, D. and Hahn, H. and Strauss, L. G. and Siems, U. and Graf, M. and Pruem, H. and Klein, J. and Laue, H. and F. O. Kaster}
}
@conference {Lempitsky2009,
	title = {Image segmentation with a bounding box prior},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2009},
	pages = {277{\textendash}284},
	abstract = {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. {\textcopyright}2009 IEEE.},
	isbn = {9781424444205},
	doi = {10.1109/ICCV.2009.5459262},
	author = {Lempitsky, Victor and Kohli, Pushmeet and Carsten Rother and Sharp, Toby}
}
@conference {kiefhaber2009,
	title = {Instrument development for combined height/slope/curvature statistics measurements of wind water waves in the field},
	booktitle = {Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009},
	year = {2009},
	abstract = {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 \& Munk{\textquoteright}s derivation of slope statistics from photographs of the Sun{\textquoteright}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.},
	author = {Daniel Kiefhaber and Roland Rocholz and Bernd J{\"a}hne}
}
@conference {Vicente2009,
	title = {Joint optimization of segmentation and appearance models},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2009},
	pages = {755{\textendash}762},
	abstract = {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. {\textcopyright}2009 IEEE.},
	isbn = {9781424444205},
	doi = {10.1109/ICCV.2009.5459287},
	author = {Vicente, Sara and Kolmogorov, Vladimir and Carsten Rother}
}
@phdthesis {kondermann2009a,
	title = {Modular Optical Flow Estimation with Applications to Fluid Dynamics},
	year = {2009},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Mathematik und Informatik, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/10184},
	author = {Daniel Kondermann}
}
@phdthesis {kondermann2009a,
	title = {Modular Optical Flow Estimation with Applications to Fluid Dynamics},
	year = {2009},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Mathematik und Informatik, Univ. Heidelberg},
	type = {phdDissertation},
	doi = {10.11588/heidok.00010184},
	author = {Kondermann, Daniel}
}
@conference {hanselmann_09_multivariate,
	title = {Multivariate Watershed Segmentation of Compositional Data},
	booktitle = {Proceedings of the 15th International Conference on Discrete Geometry for Computer Imagery (DGCI), in press},
	volume = {5810},
	year = {2009},
	note = {1},
	pages = {180-192},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-04397-0},
	author = {Hanselmann, M. and Ullrich K{\"o}the and B. Y. Renard and Kirchner, M. and Heeren, R. M. A. and Fred A. Hamprecht}
}
@conference {Rhemann2009,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {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. {\textcopyright} 2009 IEEE.},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	url = {www.alphamatting.com.},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@conference {Rhemann2009a,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {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. {\textcopyright} 2009 IEEE.},
	keywords = {Benchmark, Evaluation, Matting},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	url = {www.alphamatting.com.},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@conference {Rhemann2009b,
	title = {A perceptually motivated online benchmark for image matting},
	booktitle = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009},
	volume = {2009 IEEE},
	year = {2009},
	pages = {1826{\textendash}1833},
	abstract = {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. {\textcopyright} 2009 IEEE.},
	isbn = {9781424439935},
	doi = {10.1109/CVPRW.2009.5206503},
	author = {Rhemann, Christoph and Carsten Rother and Wang, Jue and Gelautz, Margrit and Kohli, Pushmeet and Rott, Pamela}
}
@conference {schmidt2009,
	title = {A physical model of Time-of-Flight 3D imaging systems, including suppression of ambient light},
	booktitle = {3rd Workshop on Dynamic 3-D Imaging},
	volume = {5742},
	year = {2009},
	pages = {1--15},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-642-03778-8_1},
	author = {Schmidt, Mirko and Bernd J{\"a}hne},
	editor = {Reinhard Koch and Andreas Kolb}
}
@conference {meine_09_pixel,
	title = {Pixel Approximation Errors in Common Watershed Algorithms},
	booktitle = {Discrete Geometry for Computer Imagery},
	volume = {5810},
	year = {2009},
	note = {1},
	pages = {193-202},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-04397-0},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@conference {meine_09_topological,
	title = {Pixel Approximation Errors in Common Watershed Algorithms},
	booktitle = {Discrete Geometry for Computer Imagery},
	volume = {5810},
	year = {2009},
	pages = {193-202},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-04397-0},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@phdthesis {kondermann2009,
	title = {Postprocessing and Restoration of Optical Flows},
	year = {2009},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Mathematik und Informatik, Univ.\ Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/9681},
	author = {Claudia Kondermann}
}
@phdthesis {kondermann2009,
	title = {Postprocessing and Restoration of Optical Flows},
	year = {2009},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Mathematik und Informatik, Univ. Heidelberg},
	type = {phdDissertation},
	doi = {10.11588/heidok.00009681},
	author = {Kondermann, C.}
}
@article {goerlitz_09_processing,
	title = {Processing Spectral Data},
	journal = {Surface and Interface Analysis},
	volume = {41},
	year = {2009},
	note = {1},
	pages = {636-644},
	doi = {10.1002/sia.3066},
	author = {G{\"o}rlitz, L. and Bjoern H. Menze and B. Michael Kelm and Fred A. Hamprecht}
}
@mastersthesis {kassemeyer_09_quantification,
	title = {Quantification of Tumour Angiogenesis Using Pattern Recognition},
	year = {2009},
	school = {University of Heidelberg},
	author = {Kassemeyer, S.}
}
@conference {andres_09_quantitative,
	title = {Quantitative Assessment of Image Segmentation Quality by Random Walk Relaxation Times},
	booktitle = {Pattern Recognition. 31st DAGM Symposium, Jena, Germany, September 9-11, 2009. Proceedings},
	volume = {5748},
	year = {2009},
	note = {1},
	pages = {502-511},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-03798-6},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Bonea, A. and Nadler, B. and Fred A. Hamprecht}
}
@conference {erz2009,
	title = {Radiometric and spectrometric calibrations, and distance noise measurement of TOF cameras},
	booktitle = {3rd Workshop on Dynamic 3-D Imaging},
	volume = {5742},
	year = {2009},
	pages = {28--41},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-642-03778-8_3},
	author = {Michael Erz and Bernd J{\"a}hne},
	editor = {Reinhard Koch and Andreas Kolb}
}
@mastersthesis {kumpf2009,
	title = {Sichtfelddesign mittels Freiformspiegel f{\"u}r katadioptrische Systeme},
	year = {2009},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Kumpf, Tobias}
}
@incollection {garbe2009,
	title = {Spatiotemporal image analysis for flow measurements},
	volume = {106},
	year = {2009},
	pages = {289--305},
	publisher = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-642-01106-1_29},
	author = {Christoph S. Garbe and Daniel Kondermann and Markus Jehle and Bernd J{\"a}hne},
	editor = {W. Nitsche and C. Dobriloff}
}
@article {frank_09_theoretical,
	title = {Theoretical and Experimental Error Analysis of Continuous-Wave Time-Of-Flight Range Cameras},
	journal = {Optical Engineering},
	volume = {48, 013602},
	year = {2009},
	note = {1},
	doi = {10.1117/1.3070634},
	author = {Mario Frank and Matthias Plaue and Holger Rapp and Ullrich K{\"o}the and Bernd J{\"a}hne and Fred A. Hamprecht}
}
@article {frank2009,
	title = {Theoretical and experimental error analysis of continuous-wave time-of-flight range cameras},
	journal = {Opt. Eng.},
	volume = {48},
	year = {2009},
	pages = {013602},
	abstract = {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.},
	doi = {10.1117/1.3070634},
	author = {Mario Frank and Matthias Plaue and Holger Rapp and Ullrich K{\"o}the and Bernd J{\"a}hne and Fred A. Hamprecht}
}
@article {hanselmann_09_towards,
	title = {Towards Digital Staining using Imaging Mass Spectrometry and Random Forests},
	journal = {Journal of Proteome Research},
	volume = {8},
	year = {2009},
	note = {1},
	pages = {3558-3567},
	doi = {10.1021/pr900253y},
	author = {Hanselmann, M. and Ullrich K{\"o}the and Kirchner, M. and B. Y. Renard and Amstalden, E. R. and Glunde, K. and Heeren, R. M. A. and Fred A. Hamprecht}
}
@conference {garbe2009a,
	title = {Transport of dust across the Sahara from satellite image sequence analysis},
	booktitle = {Eos Transactions},
	volume = {90},
	year = {2009},
	note = {Fall Meet. Suppl.},
	pages = {EP21A-0565},
	author = {Christoph S. Garbe and Flatow, F. and Klinger, M. and Schepanski, K. and Tegen, I. and Rannacher, R.}
}
@conference {garbe2009c,
	title = {Transport of Sahara dust into the Atlantic Ocean from satellite image sequence analysis},
	booktitle = {SOLAS Open Science Conference},
	year = {2009},
	pages = {27},
	abstract = {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{\textquoteright}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.},
	author = {Christoph S. Garbe and Flatow, F. and Klinger, M. and Schepanski, K. and Tegen, I. and Rannacher, R.}
}
@incollection {berthe2009,
	title = {The wall PIV measurement technique for near wall flow fields in biofliud mechanics},
	volume = {106},
	year = {2009},
	pages = {11--20},
	publisher = {Springer},
	abstract = {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{\textquoteright}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{\textquoteright}s application on a displacement pediatric blood pump are presented.},
	doi = {10.1007/978-3-642-01106-1_2},
	author = {A. Berthe and Daniel Kondermann and Bernd J{\"a}hne and U. Ketzscher},
	editor = {W. Nitsche and C. Dobriloff}
}
@article {renard_09_when,
	title = {When Less Can Yield More - Computational Preprocessing of MS/MS Spectra for Peptide Identification Preprocessing},
	journal = {Proteomics},
	volume = {9},
	year = {2009},
	note = {1},
	pages = {4978-4984},
	doi = {10.1002/pmic.200900326},
	author = {B. Y. Renard and Kirchner, M. and Monigatti, F. and Ivanov, A. R. and Rappsilber, J. and Winter, D. and Judith A. J. Steen and Fred A. Hamprecht and Steen, H.}
}
@phdthesis {kirchner_08_analysis,
	title = {Analysis of Spectral Data},
	year = {2008},
	publisher = {University of Heidelberg},
	author = {Kirchner, M.}
}
@article {Szeliski2008,
	title = {A comparative study of energy minimization methods for Markov random fields with smoothness-based priors},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {30},
	number = {6},
	year = {2008},
	pages = {1068{\textendash}1080},
	publisher = {Springer-Verlag},
	abstract = {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/. {\textcopyright} 2008 IEEE.},
	keywords = {Belief propagation, Global optimization, Graph Cuts, Markov random fields, Performance evaluation},
	issn = {01628828},
	doi = {10.1109/TPAMI.2007.70844},
	url = {http://vision.middlebury.edu/MRF.},
	author = {Szeliski, Richard and Zabih, Ramin and Scharstein, Daniel and Veksler, Olga and Kolmogorov, Vladimir and Agarwala, Aseem and Tappen, Marshall and Carsten Rother}
}
@article {Szeliski2008a,
	title = {A comparative study of energy minimization methods for Markov random fields with smoothness-based priors},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {30},
	number = {6},
	year = {2008},
	month = {jun},
	pages = {1068{\textendash}1080},
	abstract = {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/. {\textcopyright} 2008 IEEE.},
	keywords = {Belief propagation, Global optimization, Graph Cuts, Markov random fields, Performance evaluation},
	issn = {01628828},
	doi = {10.1109/TPAMI.2007.70844},
	author = {Szeliski, Richard and Zabih, Ramin and Scharstein, Daniel and Veksler, Olga and Kolmogorov, Vladimir and Agarwala, Aseem and Tappen, Marshall and Carsten Rother}
}
@article {hanselmann_08_concise,
	title = {Concise Representation of MS Images by Probabilistic Latent Semantic Analysis},
	journal = {Analytical Chemistry},
	volume = {80},
	number = {24},
	year = {2008},
	note = {1},
	pages = {9649-9658},
	doi = {10.1021/ac801303x},
	author = {Hanselmann, M. and Kirchner, M. and B. Y. Renard and Amstalden, E. R. and Glunde, K. and Heeren, R. M. A. and Fred A. Hamprecht}
}
@techreport {Lellmann2008,
	title = {Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation},
	year = {2008},
	institution = {IWR, University of Heidelberg},
	url = {http://www.ub.uni-heidelberg.de/archiv/8759/},
	author = {Lellmann, J. and J{\"o}rg H. Kappes and Yuan, J. and Florian Becker and Christoph Schn{\"o}rr}
}
@article {steen_08_different,
	title = {Different Phosphorylation States of the Anaphase Promoting Complex in Response to Anti-Mitotic Drugs: A Quantitative Proteomic Analysis},
	journal = {Proceedings of the National Academy of Sciences},
	volume = {105},
	number = {16},
	year = {2008},
	note = {1},
	pages = {6069-6074},
	doi = {10.1073/pnas.0709807104},
	author = {Judith A. J. Steen and Steen, H. and Georgi, A. and Kenneth C. Parker and Springer, M. and Kirchner, M. and Fred A. Hamprecht and Kirschner, M. W.}
}
@article {scholz2008,
	title = {Double-pulse planar-LIF investigations using fluorescence motion analysis for mixture formation investigation},
	journal = {Exp. Fluids},
	volume = {45},
	number = {4},
	year = {2008},
	pages = {583--593},
	abstract = {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 "fuel/air ratio measurement by laser induced fluorescence (FARLIF)" 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 "fluorescence motion analysis" (FMA) two different evaluation approaches the "variational gradient based approach" and the "variational cross correlation based approach" 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 "variational gradient based approach" 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.},
	doi = {10.1007/s00348-008-0537-x},
	author = {J. Scholz and T. Wiersbinski and Paul Ruhnau and Daniel Kondermann and Christoph S. Garbe and R. Hain and Volker Beushausen}
}
@article {andres_08_errors-in-variables,
	title = {On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation},
	journal = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	note = {1},
	pages = {1-6},
	doi = {10.1109/CVPR.2008.4587571},
	author = {Bj{\"o}rn Andres and Claudia Kondermann and Daniel Kondermann and Ullrich K{\"o}the and Fred A. Hamprecht and Christoph S. Garbe}
}
@conference {andres2008,
	title = {On errors-in-variables regression with arbitrary covariance and its application to optical flow estimation},
	booktitle = {IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008},
	year = {2008},
	pages = {1--6},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {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.},
	doi = {10.1109/CVPR.2008.4587571},
	author = {Bj{\"o}rn Andres and Claudia Kondermann and Daniel Kondermann and Fred A. Hamprecht and Christoph S. Garbe}
}
@conference {Torresani2008,
	title = {Feature correspondence via graph matching: Models and global optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5303 LNCS},
	number = {PART 2},
	year = {2008},
	pages = {596{\textendash}609},
	abstract = {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. {\textcopyright} 2008 Springer Berlin Heidelberg.},
	isbn = {3540886850},
	issn = {03029743},
	doi = {10.1007/978-3-540-88688-4-44},
	author = {Torresani, Lorenzo and Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Torresani2008a,
	title = {Feature correspondence via graph matching: Models and global optimization},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5303 LNCS},
	number = {PART 2},
	year = {2008},
	pages = {596{\textendash}609},
	abstract = {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. {\textcopyright} 2008 Springer Berlin Heidelberg.},
	isbn = {3540886850},
	issn = {03029743},
	doi = {10.1007/978-3-540-88688-4-44},
	author = {Torresani, Lorenzo and Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Vicente2008,
	title = {Graph cut based image segmentation with connectivity priors},
	booktitle = {26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR},
	year = {2008},
	abstract = {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 "shrinking bias". 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. {\textcopyright}2008 IEEE.},
	isbn = {9781424422432},
	doi = {10.1109/CVPR.2008.4587440},
	author = {Vicente, Sara and Kolmogorov, Vladimir and Carsten Rother}
}
@incollection {acker2008,
	title = {Inverse Problems and Parameter Identification in Image Processing},
	year = {2008},
	pages = {111--151},
	publisher = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-540-75632-3_4},
	author = {Jens F. Acker and Benjamin Berkels and Kristian Bredies and Mamadou S. Diallo and Marc Droske and Christoph S. Garbe and Matthias Holschneider and Jaroslav Hron and Claudia Kondermann and Michail Kulesh and Peter Maass and Nadine Olischl{\"a}ger and Heinz-Otto Peitgen},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@conference {Kappes-et-al-08,
	title = {MAP-Inference for Highly-Connected Graphs with DC-Programming},
	booktitle = {Pattern Recognition -- 30th DAGM Symposium},
	volume = {5096},
	year = {2008},
	pages = {1--10},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Kappes-et-al-08,
	title = {MAP-Inference for Highly-Connected Graphs with DC-Programming},
	booktitle = {Pattern Recognition {\textendash} 30th DAGM Symposium},
	series = {lncs},
	volume = {5096},
	year = {2008},
	pages = {1{\textendash}10},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Kappes, J. H. and Schn{\"o}rr, C.}
}
@article {menze_08_mimicking,
	title = {Mimicking the human expert: pattern recognition for an automated assessment of data quality in MRSI},
	journal = {Magnetic Resonance in Medicine},
	volume = {59},
	year = {2008},
	note = {1},
	pages = {1457-1466},
	doi = {10.1002/mrm.21519},
	author = {Bjoern H. Menze and B. Michael Kelm and M.-A. Weber and Bachert, P. and Fred A. Hamprecht}
}
@conference {Enzweiler-et-al-08c,
	title = {Monocular Pedestrian Recognition Using Motion Parallax},
	booktitle = {Proc. IEEE Symposium on Intelligent Vehicles},
	year = {2008},
	pages = {792-797},
	author = {Markus Enzweiler and Pascal Kanter and Dariu M. Gavrila}
}
@conference {kondermann2008b,
	title = {Motion Estimation Based on a Temporal Model of Fluid Flows},
	booktitle = {13th International Symposium on Flow Visualization},
	year = {2008},
	pages = {1-10},
	author = {Daniel Kondermann and Claudia Kondermann and A. Berthe and U. Kertzscher and Christoph S. Garbe}
}
@article {renard_08_nitpick,
	title = {NITPICK: Peak Identification for Mass Spectrometry Data},
	journal = {BMC Bioinformatics},
	volume = {9},
	year = {2008},
	pages = {355},
	doi = {10.1186/1471-2105-9-355},
	author = {B. Y. Renard and Kirchner, M. and Steen, H. and Judith A. J. Steen and Fred A. Hamprecht}
}
@incollection {garbe_08_nonlinear,
	title = {Nonlinear Analysis of Multi-Dimensional Signals},
	year = {2008},
	note = {1},
	pages = {231-288},
	publisher = {Springer},
	doi = {10.1007/978-3-540-75632-3_7},
	author = {Christoph S. Garbe and Kai Krajsek and Pavel Pavlov and Bj{\"o}rn Andres and Matthias M{\"u}hlich and Ingo Stuke and Cicero Mota and Martin B{\"o}hme and Martin Haker and Schuchert, T. and Hanno Scharr and Til Aach and Erhardt Barth},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@incollection {garbe2008,
	title = {Nonlinear analysis of multi-dimensional signals: local adaptive estimation of complex motion and orientation patterns},
	year = {2008},
	pages = {231-288},
	publisher = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-540-75632-3_7},
	author = {Christoph S. Garbe and Kai Krajsek and Pavel Pavlov and Bj{\"o}rn Andres and Matthias M{\"u}hlich and Ingo Stuke and Cicero Mota and Martin B{\"o}hme and Martin Haker and Tobias Schucher and Hanno Scharr and Til Aach and Erhardt Barth},
	editor = {R. Dahlhaus and J. Kurths and Timmer, J. and Peter Maass}
}
@phdthesis {klappstein2008,
	title = {Optical-Flow based Detection of Moving Objects in Traffic Scenes},
	year = {2008},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Mathematik und Informatik, Univ.\ Heidelberg},
	abstract = {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{\textquoteright}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.},
	url = {http://www.ub.uni-heidelberg.de/archiv/8591/},
	author = {Klappstein, Jens}
}
@conference {Kohli2008,
	title = {On partial optimality in multi-label MRFs},
	booktitle = {Proceedings of the 25th International Conference on Machine Learning},
	year = {2008},
	pages = {480{\textendash}487},
	abstract = {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).},
	isbn = {9781605582054},
	doi = {10.1145/1390156.1390217},
	author = {Kohli, Pushmeet and Shekhovtsov, Alexander and Carsten Rother and Kolmogorov, Vladimir and Torr, Philip}
}
@conference {kondermann2008,
	title = {Postprocessing of optical flows via surface measures and motion inpainting},
	booktitle = {Pattern Recognition},
	volume = {5096},
	year = {2008},
	pages = {355--364},
	abstract = {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.},
	doi = {10.1007/978-3-540-69321-5_36},
	author = {Claudia Kondermann and Daniel Kondermann and Christoph S. Garbe}
}
@article {koethe_08_reliable-image-analysis,
	title = {Reliable Low-Level Image Analysis},
	year = {2008},
	publisher = {Department Informatik, University of Hamburg},
	address = {Hamburg},
	abstract = {What information give <em>discrete images</em> about the <em>continuous world</em>?

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{\textquoteright}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.</div>},
	author = {Ullrich K{\"o}the}
}
@article {koenig_08_robust,
	title = {Robust Prediction of the MASCOT Score for an Improved Quality Assessment in Mass Spectrometric Proteomics},
	journal = {Journal of Proteome Research},
	volume = {7},
	number = {9},
	year = {2008},
	note = {1},
	pages = {3708-3717},
	doi = {10.1021/pr700859x},
	author = {K{\"o}nig, T. and Bjoern H. Menze and Kirchner, M. and Monigatti, F. and Kenneth C. Parker and Patterson, T. and Judith Jebanthirajah Steen and Fred A. Hamprecht and Steen, H.}
}
@conference {andres_08_segmentation,
	title = {Segmentation of SBFSEM Volume Data of Neural Tissue by Hierarchical Classification},
	booktitle = {Pattern Recognition. 30th DAGM Symposium Munich, Germany, June 10-13, 2008. Proceedings},
	volume = {5096},
	year = {2008},
	note = {1},
	pages = {142-152},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-540-69321-5_15},
	author = {Bj{\"o}rn Andres and Ullrich K{\"o}the and Helmstaedter, M. and Denk, W. and Fred A. Hamprecht},
	editor = {Gerhard Rigoll}
}
@conference {kondermann2008a,
	title = {A statistical confidence measure for optical flows},
	booktitle = {Proceedings of the ECCV},
	volume = {5304},
	year = {2008},
	pages = {290--301},
	abstract = {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.},
	doi = {10.1007/978-3-540-88690-7_22},
	author = {Claudia Kondermann and Mester, R. and Christoph S. Garbe}
}
@article {meine_08_topological,
	title = {A Topological Sampling Theorem for Robust Boundary Reconstruction
and Image Segmentation},
	journal = {Discrete Applied Mathematics},
	volume = {157},
	number = {3},
	year = {2008},
	pages = {524-541},
	doi = {10.1016/j.dam.2008.05.031},
	author = {Meine, H. and Ullrich K{\"o}the and Stelldinger, P.}
}
@article {jaeger_08_weakly,
	title = {Weakly Supervised Learning of a Classifier for Unusual Event Detection},
	journal = {IEEE Transactions on Image Processing},
	volume = {17},
	number = {9},
	year = {2008},
	note = {1},
	pages = {1700-1708},
	doi = {10.1109/TIP.2008.2001043},
	author = {J{\"a}ger, M. and Knoll, C. and Fred A. Hamprecht}
}
@incollection {koethe_08_what,
	title = {What Can We Learn from Discrete Images about the Continuous World},
	volume = {4992},
	year = {2008},
	pages = {4-19},
	publisher = {Springer},
	doi = {10.1007/978-3-540-79126-3_2},
	author = {Ullrich K{\"o}the}
}
@conference {kondermann2007,
	title = {An adaptive confidence measure for optical flows based on linear subspace projections},
	booktitle = {Proceedings of the 29th DAGM Symposium on Pattern Recognition},
	volume = {4713},
	year = {2007},
	pages = {132--141},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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\%.},
	doi = {10.1007/978-3-540-74936-3_14},
	author = {Claudia Kondermann and Daniel Kondermann and Bernd J{\"a}hne and Christoph S. Garbe and Christoph Schn{\"o}rr and Bernd J{\"a}hne},
	editor = {Fred A. Hamprecht}
}
@article {kirchner_07_amsrpm,
	title = {amsrpm: Robust Point Matching in Retention Time Alignment of LC/MS Data with R},
	journal = {Journal of Statistical Software},
	volume = {18},
	number = {4},
	year = {2007},
	note = {1},
	pages = {1-12},
	url = {http://www.jstatsoft.org/v18/i04/paper},
	author = {Kirchner, M. and Saussen, B. and Steen, H. and Judith A. J. Steen and Fred A. Hamprecht}
}
@conference {Kolmogorov2007a,
	title = {Applications of parametric maxflow in computer vision},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2007},
	abstract = {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{\textquoteright}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 "PDE cuts" [5]. We present experimental results for image segmentation, 3D reconstruction, and the cosegmentation problem. {\textcopyright}2007 IEEE.},
	doi = {10.1109/ICCV.2007.4408910},
	author = {Kolmogorov, Vladimir and Boykov, Yuri and Carsten Rother}
}
@conference {Kolmogorov2007b,
	title = {Applications of parametric maxflow in computer vision},
	booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
	year = {2007},
	abstract = {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{\textquoteright}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 "PDE cuts" [5]. We present experimental results for image segmentation, 3D reconstruction, and the cosegmentation problem. {\textcopyright}2007 IEEE.},
	doi = {10.1109/ICCV.2007.4408910},
	author = {Kolmogorov, Vladimir and Boykov, Yuri and Carsten Rother}
}
@article {kelm_07_automated,
	title = {Automated Estimation of Tumor Probability in Prostate MRSI: Pattern Recognition vs. Quantification},
	journal = {Magnetic Resonance in Medicine},
	volume = {57},
	number = {1},
	year = {2007},
	note = {1},
	pages = {150-159},
	doi = {10.1002/mrm.21112},
	author = {B. Michael Kelm and Bjoern H. Menze and C. M. Zechmann and Baudendistel, K. T. and Fred A. Hamprecht}
}
@conference {Kannan2007,
	title = {Clustering appearance and shape by learning jigsaws},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2007},
	pages = {657{\textendash}664},
	abstract = {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 {\textquoteright}jigsaw pieces{\textquoteright}, 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.},
	isbn = {9780262195683},
	issn = {10495258},
	doi = {10.7551/mitpress/7503.003.0087},
	author = {Kannan, Anitha and Winn, John and Carsten Rother}
}
@conference {Kannan2007a,
	title = {Clustering appearance and shape by learning jigsaws},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2007},
	pages = {657{\textendash}664},
	abstract = {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 {\textquoteright}jigsaw pieces{\textquoteright}, 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.},
	isbn = {9780262195683},
	issn = {10495258},
	doi = {10.7551/mitpress/7503.003.0087},
	author = {Kannan, Anitha and Winn, John and Carsten Rother}
}
@article {weber_07_comparison,
	title = {Comparison of correctness of manuel and automatic evaluation of MR-spectrum with prostrate cancer},
	journal = {Der Urologe},
	volume = {46},
	number = {9},
	year = {2007},
	pages = {1252},
	doi = {10.1007/s00120-007-1488-1},
	author = {C. Weber and C. M. Zechmann and B. Michael Kelm and Zamecnik, R. and Hendricks, D. and Waldherr, R. and Fred A. Hamprecht and Delorme, S. and Bachert, P. and Ikinger, U.}
}
@incollection {jaehne2007d,
	title = {Data analysis},
	year = {2007},
	pages = {1437--1491},
	publisher = {Springer},
	chapter = {25},
	abstract = {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.},
	doi = {10.1007/978-3-540-30299-5_25},
	author = {Bernd J{\"a}hne and Michael Klar and Markus Jehle},
	editor = {C. Tropea and J. Foss and A. Yarin}
}
@conference {scholz2007,
	title = {Double-pulse planar-LIF investigations using fluorescence motion analysis for mixture formation investigation},
	booktitle = {7th International Symposium on Particle Image Velocimetry Rome, 11. - 14.Sept.},
	year = {2007},
	author = {J. Scholz and T. Wiersbinski and Paul Ruhnau and Daniel Kondermann and Christoph S. Garbe and R. Hain and Volker Beushausen}
}
@phdthesis {kelm_07_evaluation,
	title = {Evaluation of Vector-Valued Clinical Image Data Using Probabilistic Graphical Models: Quantification and Pattern Recognition},
	year = {2007},
	publisher = {University of Heidelberg},
	author = {B. Michael Kelm}
}
@conference {menze_07_from,
	title = {From eigenspots to fisherspots -- latent spaces in the nonlinear detection of spot patterns in a highly variable background},
	booktitle = {Advances in data analysis},
	volume = {33},
	year = {2007},
	note = {1},
	pages = {255-262},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-540-70981-7_29},
	author = {Bjoern H. Menze and B. Michael Kelm and Fred A. Hamprecht},
	editor = {Lenz, H.-J. and Decker, R.}
}
@article {Blake2007,
	title = {Fusion of stereo, colour and contrast},
	journal = {Springer Tracts in Advanced Robotics},
	volume = {28},
	year = {2007},
	issn = {16107438},
	doi = {10.1007/978-3-540-48113-3_27},
	url = {www.research.microsoft.com/vision/cambridge},
	author = {Blake, A and Criminisi, A and Cross, G and Kolmogorov, V and Carsten Rother}
}
@conference {Karim-et-al-07,
	title = {Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification},
	booktitle = {Pattern Recognition -- 29th DAGM Symposium},
	volume = {4713},
	year = {2007},
	pages = {395-404},
	publisher = {Springer},
	organization = {Springer},
	author = {Karim, R. and Bergtholdt, M. and J{\"o}rg H. Kappes and Christoph Schn{\"o}rr}
}
@conference {Karim-et-al-07,
	title = {Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification},
	booktitle = {Pattern Recognition {\textendash} 29th DAGM Symposium},
	series = {LCNS},
	volume = {4713},
	year = {2007},
	pages = {395-404},
	publisher = {Springer},
	organization = {Springer},
	author = {Karim, R. and Bergtholdt, M. and Kappes, J. H. and Schn{\"o}rr, C.}
}
@booklet {Kolmogorov2007,
	title = {Minimizing nonsubmodular functions with graph cuts - A review},
	howpublished = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {29},
	number = {7},
	year = {2007},
	pages = {1274{\textendash}1279},
	abstract = {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. {\textcopyright} 2007 IEEE.},
	keywords = {energy minimization, Markov random fields, Min cut/max flow, Quadratic pseudo-Boolean optimization, Texture restoration},
	issn = {01628828},
	doi = {10.1109/TPAMI.2007.1031},
	author = {Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Rother2007a,
	title = {Optimizing binary MRFs via extended roof duality},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2007},
	abstract = {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 "roof duality" was recently introduced into computer vision. We study two methods which extend this approach. First, we discuss an efficient implementation of the "probing" 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. {\textcopyright} 2007 IEEE.},
	isbn = {1424411807},
	issn = {10636919},
	doi = {10.1109/CVPR.2007.383203},
	author = {Carsten Rother and Kolmogorov, Vladimir and Lempitsky, Victor and Szummer, Martin}
}
@article {Rother2007,
	title = {Optimizing Binary MRFs via Extended Roof Duality Technical Report MSR-TR-2007-46},
	journal = {Computing},
	year = {2007},
	abstract = {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 "roof duality" was recently introduced into computer vision. We study two methods which extend this approach. First, we discuss an efficient implementation of the "probing" 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.},
	url = {http://research.microsoft.com/vision/cambridge/},
	author = {Carsten Rother and Kolmogorov, Vladimir and Lempitsky, Victor and Szummer, Martin}
}
@mastersthesis {koenig_07_quality,
	title = {Quality Control in Mass Spectrometry},
	year = {2007},
	note = {1},
	school = {University of Heidelberg},
	author = {K{\"o}nig, T.}
}
@conference {goerlitz_07_semi,
	title = {Semi-Supervised Tumor Detection in MRSI With Discriminative Random Fields},
	booktitle = {Pattern Recognition},
	volume = {4713},
	year = {2007},
	note = {1},
	pages = {224-233},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-540-74936-3_23},
	author = {G{\"o}rlitz, L. and Bjoern H. Menze and M.-A. Weber and B. Michael Kelm},
	editor = {Fred A. Hamprecht and Fred A. Hamprecht and Christoph Schn{\"o}rr and Bernd J{\"a}hne}
}
@conference {Schmidt-et-al-07,
	title = {Spine Detection and Labeling Using a Parts-Based Graphical Model},
	booktitle = {Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007)},
	volume = {4584},
	year = {2007},
	pages = {122-133},
	publisher = {Springer},
	organization = {Springer},
	author = {Schmidt, S. and J{\"o}rg H. Kappes and Bergtholdt, M. and Pekar, V. and Dries, S. and Bystrov, D. and Christoph Schn{\"o}rr}
}
@conference {Schmidt-et-al-07,
	title = {Spine Detection and Labeling Using a Parts-Based Graphical Model},
	booktitle = {Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007)},
	series = {LCNS},
	volume = {4584},
	year = {2007},
	pages = {122-133},
	publisher = {Springer},
	organization = {Springer},
	author = {Schmidt, S. and Kappes, J. H. and Bergtholdt, M. and Pekar, V. and Dries, S. and Bystrov, D. and Schn{\"o}rr, C.}
}
@incollection {Schnoerr-et-al-06a,
	title = {Variational Reconstruction with DC-Programming},
	year = {2007},
	publisher = {Birkh{\"a}user},
	address = {Boston},
	author = {Schn{\"o}rr, C. and Sch{\"u}le, T. and Weber, S.},
	editor = {Herman, G. and Kuba, A.}
}
@conference {kelm_06_bayesian,
	title = {Bayesian Estimation of Smooth Parameter Maps for Dynamic Contrast-Enhanced MR Images with Block-ICM},
	booktitle = {Proc Computer Vision and Pattern Recognition Workshop (Mathematical Methods in Biomedical Image Analysis)},
	year = {2006},
	note = {1},
	pages = {96-103},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	doi = {10.1109/CVPRW.2006.41},
	author = {B. Michael Kelm and M{\"u}ller, N. and Bjoern H. Menze and Fred A. Hamprecht},
	editor = {Schmid, C. and Soatto, S. and Tomasi, C.}
}
@conference {Weber-et-al-06b,
	title = {A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography},
	booktitle = {Discrete Geometry for Computer Imagery (DGCI 2006)},
	volume = {4245},
	year = {2006},
	pages = {146-156},
	publisher = {Springer},
	organization = {Springer},
	author = {Weber, S. and Nagy, A. and Sch{\"u}le, T. and Christoph Schn{\"o}rr and Kuba, A.}
}
@conference {Weber-et-al-06a,
	title = {Binary Tomography with Deblurring},
	booktitle = {Combinatorial Image Analysis},
	volume = {4040},
	year = {2006},
	pages = {375-388},
	publisher = {Springer},
	organization = {Springer},
	author = {Weber, S. and Sch{\"u}le, T. and Christoph Schn{\"o}rr and Kuba, A.}
}
@conference {zechmann_06_can,
	title = {Can man still beat the machine? Automated vs. manual pattern recognition of 3D MRSI data of prostate cancer patients},
	booktitle = {Proceedings of the 16th ISMRM},
	year = {2006},
	author = {C. M. Zechmann and B. Michael Kelm and Zamecnik, P. and Ikinger, U. and Waldherr, R. and R{\"o}ll, S. and Delorme, S. and Fred A. Hamprecht and Bachert, P.}
}
@conference {kelm_06_claret,
	title = {CLARET: a tool for fully automated evaluation of MRSI with pattern recognition methods.},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin 2006 - Algorithmen, Systeme, Anwendungen},
	year = {2006},
	note = {1},
	pages = {51-55},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/3-540-32137-3_7},
	url = {http://www.efmi-wg-mip.net/service/bvm2006},
	author = {B. Michael Kelm and Bjoern H. Menze and Neff, T. and C. M. Zechmann and Fred A. Hamprecht},
	editor = {H. Handels and G. Bebis and et al.}
}
@inbook {carlsohn_06_spectral,
	title = {Color image processing},
	volume = {7(17)},
	year = {2006},
	pages = {393-419},
	publisher = {CRC Press},
	organization = {CRC Press},
	chapter = {Spectral Imaging and Applications},
	author = {Carlsohn, M. F. and Bjoern H. Menze and B. Michael Kelm and Fred A. Hamprecht and Kercek, A. and Leitner, R. and Polder, G.},
	editor = {Lukac, R. and Plataniotis, K.N.}
}
@conference {kelm_06_combining,
	title = {Combining Generative and Discriminative Methods for Pixel Classification with Multi-Conditional Learning.},
	booktitle = {ICPR 2006},
	volume = {2},
	year = {2006},
	note = {1},
	pages = {828-832},
	doi = {10.1109/ICPR.2006.384},
	author = {B. Michael Kelm and Pal, C. and McCallum, A.}
}
@conference {Kolmogorov2006a,
	title = {Comparison of energy minimization algorithms for highly connected graphs},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {3952 LNCS},
	year = {2006},
	pages = {1{\textendash}15},
	abstract = {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. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	isbn = {3540338349},
	issn = {16113349},
	doi = {10.1007/11744047_1},
	author = {Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Kolmogorov2006b,
	title = {Comparison of energy minimization algorithms for highly connected graphs},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {3952 LNCS},
	year = {2006},
	pages = {1{\textendash}15},
	abstract = {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. {\textcopyright} Springer-Verlag Berlin Heidelberg 2006.},
	isbn = {3540338349},
	issn = {16113349},
	doi = {10.1007/11744047_1},
	author = {Kolmogorov, Vladimir and Carsten Rother}
}
@conference {Rother2006,
	title = {Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	volume = {1},
	year = {2006},
	pages = {994{\textendash}1000},
	abstract = {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. {\textcopyright} 2006 IEEE.},
	isbn = {0769525970},
	issn = {10636919},
	doi = {10.1109/CVPR.2006.91},
	url = {http://research.microsoft.com/vision/cambridge/},
	author = {Carsten Rother and Kolmogorov, Vladimir and Minka, Tom and Blake, Andrew}
}
@conference {jehle2006c,
	title = {Direct estimation of the wall shear rate using parametric motion models in 3D},
	booktitle = {Proceedings of the 28th DAGM Symposium on Pattern Recognition},
	volume = {4174},
	year = {2006},
	pages = {434--443},
	abstract = {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.},
	issn = {0302-9743},
	doi = {10.1007/11861898_44},
	author = {Markus Jehle and Bernd J{\"a}hne and U. Kertzscher}
}
@phdthesis {kraus2006,
	title = {DOASIS a framework design for DOAS},
	year = {2006},
	publisher = {Technische Informatik, Univ.\ Mannheim},
	author = {S. Kraus}
}
@phdthesis {kraus2006,
	title = {DOASIS a framework design for DOAS},
	year = {2006},
	publisher = {Technische Informatik, Univ. Mannheim},
	type = {phdDissertation},
	author = {Kraus, S.}
}
@article {lichy_06_einsatz,
	title = {Einsatz der 1H-MR-spektroskopischen Bildgebung in der Strahlentherapie: Cholin als Marker f{\"u}r die Bestimmung der relativen Wahrscheinlichkeit eines Tumorprogresses nach Bestrahlung glialer Hirntumoren},
	journal = {Zeitung f{\"u}r R{\"o}ntgenforschung},
	volume = {178},
	year = {2006},
	pages = {627-633},
	doi = {10.1055/s-2006-926744},
	author = {M. P. Lichy and Bachert, P. and Fred A. Hamprecht and M.-A. Weber and Debus, J. and Schulz-Ertner, D. and Kauczor, H.-U. and Schlemmer, H.-P.}
}
@conference {degreif2006a,
	title = {Gas exchange measurements: the chemically enhanced gas transfer of carbon dioxide at the water surface},
	booktitle = {Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006},
	year = {2006},
	publisher = {Deutsche Physikalische Gesellschaft},
	organization = {Deutsche Physikalische Gesellschaft},
	url = {http://www.dpg-verhandlungen.de/2006/heidelberg/up.html},
	author = {Kai Degreif and Joachim Kuss and Bernd J{\"a}hne}
}
@incollection {schimpf2006a,
	title = {Infrared imaging: a novel tool to investigate the influence of surface slicks on air-sea gas transfer},
	year = {2006},
	pages = {239--252},
	publisher = {Springer},
	abstract = {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.},
	doi = {10.1007/3-540-33271-5_21},
	author = {Uwe Schimpf and Nelson M. Frew and Bernd J{\"a}hne and Heinrich H{\"u}hnerfuss},
	editor = {Martin Gade and Gerald M. Korenowski}
}
@conference {Bergtholdt-et-al-06b,
	title = {Learning of Graphical Models and Efficient Inference for Object Class Recognition},
	booktitle = {Proc. DAGM 2006},
	series = {LCNS},
	volume = {375-388},
	year = {2006},
	pages = {375-388},
	publisher = {Springer},
	organization = {Springer},
	author = {Bergtholdt, Martin and Kappes, J{\"o}rg H. and Christoph Schn{\"o}rr}
}
@conference {menze_06_machine-based,
	title = {Machine-based rejection of low quality spectra and estimation of brain tumor probabilities from magnetic resonance spectroscopic images},
	booktitle = {Bildverarbeitung f{\"u}r die Medizin},
	year = {2006},
	note = {1},
	pages = {31-36},
	doi = {10.1007/3-540-32137-3_7},
	author = {Bjoern H. Menze and B. Michael Kelm and Heck, D. and M. P. Lichy and Fred A. Hamprecht},
	editor = {H. Handels and G. Bebis and et al.}
}
@article {Bruhn-et-al-06,
	title = {A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods},
	journal = {Int.~J.~Computer Vision},
	volume = {70},
	number = {3},
	year = {2006},
	pages = {257-277},
	author = {Bruhn, Andr{\'e}s and Weickert, Joachim and Kohlberger, Timo and Christoph Schn{\"o}rr}
}
@article {Bruhn-et-al-06,
	title = {A Multigrid Platform for Real-Time Motion Computation with Discontinuity-Preserving Variational Methods},
	journal = {Int. J. Computer Vision},
	volume = {70},
	number = {3},
	year = {2006},
	pages = {257-277},
	author = {Bruhn, Andr{\'e}s and Weickert, Joachim and Kohlberger, Timo and Christoph Schn{\"o}rr}
}
@article {menze_06_optimal,
	title = {Optimal Classification of Long Echo Time in vivo Magnetic Resonance Spectra in the Detection of Recurrent Brain Tumor},
	journal = {NMR in Biomedicine},
	volume = {19},
	number = {5},
	year = {2006},
	note = {1},
	pages = {599-609},
	doi = {10.1002/nbm.1041},
	author = {Bjoern H. Menze and M. P. Lichy and Bachert, P. and B. Michael Kelm and Schlemmer, H. P. and Fred A. Hamprecht}
}
@article {Kolmogorov2006,
	title = {Probabilistic fusion of stereo with color and contrast for bilayer segmentation},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {28},
	number = {9},
	year = {2006},
	pages = {1480{\textendash}1492},
	abstract = {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. {\textcopyright} 2006 IEEE.},
	keywords = {3D/stereo scene analysis, Computer vision, Dynamic programming, Image processing and computer vision, Parameter learning},
	issn = {01628828},
	doi = {10.1109/TPAMI.2006.193},
	url = {http://research.microsoft.com/vision/cambridge},
	author = {Kolmogorov, Vladimir and Criminisi, Antonio and Blake, Andrew and Cross, Geoffrey and Carsten Rother}
}
@conference {Kirk2006,
	title = {Understanding photowork},
	booktitle = {Conference on Human Factors in Computing Systems - Proceedings},
	volume = {2},
	year = {2006},
	pages = {761{\textendash}770},
	abstract = {In this paper we introduce the notion of "photowork" 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 {\textquoteright}search{\textquoteright} and {\textquoteright}browse{\textquoteright} 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 "search" 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.},
	keywords = {Browsing, Content-based image retrieval, Digital photo albums, Photowork, Searching, Use of images},
	isbn = {1595931783},
	doi = {10.1145/1124772.1124885},
	author = {Kirk, David S and Sellen, Abigail J and Carsten Rother and Wood, Kenneth R}
}
@techreport {koenig_05_application,
	title = {On the Application of Multiscale Motion Estimation in Intravascular Elastography},
	year = {2005},
	institution = {University of Heidelberg},
	author = {K{\"o}nig, T. and Fred A. Hamprecht and K{\"u}cherer, H.}
}
@conference {kelm_05_automatische,
	title = {Automatische Lokalisation von Tumoren in 1H-NMR-spektroskopischen in vivo Aufnahmen},
	booktitle = {VDI-Berichte},
	volume = {1883},
	year = {2005},
	note = {1},
	pages = {457-466},
	author = {B. Michael Kelm and Bjoern H. Menze and Fred A. Hamprecht}
}
@booklet {jaeger_05_automatisierte,
	title = {Automatisierte Klassifikation von Laserschwei\DFprozessen durch Nutzung von 3D Signalverarbeitungs-Algorithmen},
	year = {2005},
	publisher = {Robert Bosch GmbH, Schwieberdingen and IWR, Uni Heidelberg},
	author = {J{\"a}ger, M. and Knoll, C. and Fred A. Hamprecht}
}
@conference {Kolmogorov2005,
	title = {Bi-layer segmentation of binocular stereo video},
	booktitle = {Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005},
	volume = {II},
	year = {2005},
	pages = {407{\textendash}414},
	abstract = {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.},
	isbn = {0769523722},
	doi = {10.1109/CVPR.2005.91},
	url = {http://research.microsoft.com/vision/cambridge},
	author = {Kolmogorov, V and Criminisi, A and Blake, A and Cross, G and Carsten Rother}
}
@incollection {Weber-et-al-05b,
	title = {Binary Tomography by Iterating Linear Programs},
	year = {2005},
	note = {(13 pages)},
	publisher = {Springer},
	author = {Weber, S. and Schn{\"o}rr, C. and Sch{\"u}le, T. and Hornegger, J.},
	editor = {Klette, R. and Kozera, R. and Noakes, L. and Weickert, J.}
}
@phdthesis {klar2005,
	title = {Design of an endoscopic 3D Particle-Tracking Velocimetry system and its application in flow measurements within a gravel layer},
	year = {2005},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {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.},
	url = {http://www.ub.uni-heidelberg.de/archiv/5961},
	author = {Michael Klar}
}
@conference {Rother2005,
	title = {Digital tapestry [automatic image synthesis]},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	pages = {589{\textendash}596},
	abstract = {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 {\textquoteright}thumbnail{\textquoteright} 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 "almost" 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.},
	isbn = {0-7695-2372-2},
	doi = {10.1109/CVPR.2005.130},
	url = {http://research.microsoft.com/ http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1467321\%5Cnhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467321},
	author = {Carsten Rother and Kumar, Sanjiv and Kolmogorov, Vladimir and Blake, Andrew}
}
@conference {Bruhn-et-al-05a,
	title = {Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time},
	booktitle = {Scale-Space 2005},
	series = {lncs},
	volume = {3459},
	year = {2005},
	pages = {279{\textendash}290},
	publisher = {Springer},
	organization = {Springer},
	author = {Bruhn, Andr{\'e}s and Weickert, Joachim and Kohlberger, Timo and Christoph Schn{\"o}rr}
}
@article {Kohlberger-et-al-04b,
	title = {Domain decomposition for variational optical flow computation},
	journal = {IEEE Trans. Image Proc.},
	volume = {14},
	number = {8},
	year = {2005},
	pages = {1125-1137},
	author = {Kohlberger, T. and Schn{\"o}rr, C. and Bruhn, A. and Weickert, J.}
}
@inbook {schimpf2005,
	title = {Infrared imaging: a novel tool to investigate the influence of surface slicks on air-sea gas transfer},
	year = {2005},
	pages = {239-252},
	publisher = {Springer},
	organization = {Springer},
	chapter = {3},
	doi = {10.1007/3-540-33271-5_21},
	author = {Uwe Schimpf and Nelson M. Frew and Bernd J{\"a}hne and Heinrich H{\"u}hnerfuss},
	editor = {Martin Gade and Gerald M. Korenowski}
}
@article {kuensch_05_optimal,
	title = {Optimal lattices for sampling},
	journal = {IEEE Transactions on Information Theory},
	volume = {51},
	number = {0055},
	year = {2005},
	pages = {634-647},
	doi = {10.1109/TIT.2004.840864},
	author = {K{\"u}nsch, H. R. and Agrell, E. and Fred A. Hamprecht}
}
@incollection {Heiler-et-al-05,
	title = {Semidefinite Clustering for Image Segmentation with A-priori Knowledge},
	volume = {3663},
	year = {2005},
	pages = {309{\textendash}317},
	publisher = {Springer},
	author = {Heiler, M. and Keuchel, J. and Schn{\"o}rr, C.}
}
@article {Bruhn-et-al-4b,
	title = {Variational optic flow computation in real-time},
	journal = {IEEE Trans. Image Proc.},
	volume = {14},
	number = {5},
	year = {2005},
	pages = {608{\textendash}615},
	author = {Bruhn, Andr{\'e}s and Weickert, Joachim and Feddern, Christian and Kohlberger, Timo and Christoph Schn{\"o}rr}
}
@article {Ruhnau-et-al-05,
	title = {Variational Optical Flow Estimation for Particle Image Velocimetry},
	journal = {Experiments in Fluids},
	volume = {38},
	year = {2005},
	pages = {21--32},
	author = {Paul Ruhnau and Kohlberger, T. and Nobach, H. and Christoph Schn{\"o}rr}
}
@article {Ruhnau-et-al-05,
	title = {Variational Optical Flow Estimation for Particle Image Velocimetry},
	journal = {Experiments in Fluids},
	volume = {38},
	year = {2005},
	pages = {21{\textendash}32},
	author = {Ruhnau, P. and Kohlberger, T. and Nobach, H. and Schn{\"o}rr, C.}
}
@conference {klar2004a,
	title = {Analysis of subsurface gravel layer flow caused by turbulent open-channel flow using 3D PTV and pressure sensor techniques},
	booktitle = {BAW-Workshop Soil and Bed Stability - Interaction Effects between Geotechnics and Hydraulic Engineering},
	year = {2004},
	author = {Michael Klar and Markus Jehle and Bernd J{\"a}hne and Detert, M. and Jirka G. H.}
}
@conference {jehle2004,
	title = {Bewegungsdetektion und Geschwindigkeitsanalyse in Bildfolgen zur Untersuchung von Sedimentverlagerungen},
	booktitle = {Mitteilungen des Instituts f{\"u}r Grundbau und Bodenmechanik},
	volume = {77},
	year = {2004},
	pages = {371-392},
	author = {Markus Jehle and Michael Klar and H.-J. K{\"o}hler and Heibaum, M.}
}
@conference {wshs-04,
	title = {Binary Tomography by Iterating Linear Programs from Noisy Projections},
	booktitle = {Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA{\textquoteright}04)},
	series = {LNCS},
	volume = {3322},
	year = {2004},
	pages = {38{\textendash}51},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Weber, S. and Sch{\"u}le, T. and Hornegger, J. and Schn{\"o}rr, C.},
	editor = {Klette, R. and {\v Z}uni{\'c}, J.}
}
@conference {Yuan-et-al-04b,
	title = {Convex Set-Based Estimation of Image Flows},
	booktitle = {ICPR 2004 {\textendash} 17th Int. Conf. on Pattern Recognition},
	volume = {1},
	year = {2004},
	month = {Aug. 23-26},
	pages = {124-127},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Cambridge, UK},
	author = {Yuan, J. and Schn{\"o}rr, C. and Kohlberger, T. and Ruhnau, P.}
}
@conference {Rother2004,
	title = {"GrabCut" - Interactive foreground extraction using iterated graph cuts},
	booktitle = {ACM Transactions on Graphics},
	volume = {23},
	number = {3},
	year = {2004},
	pages = {309{\textendash}314},
	abstract = {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 "border matting" 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.},
	keywords = {Alpha Matting, Foreground extraction, Graph Cuts, Image Editing, Interactive Image Segmentation},
	issn = {07300301},
	doi = {10.1145/1015706.1015720},
	author = {Carsten Rother and Kolmogorov, Vladimir and Blake, Andrew}
}
@conference {Keuchel-et-al-04a,
	title = {Hierarchical Image Segmentation based on Semidefinite Programming},
	booktitle = {Pattern Recognition, Proc. 26th DAGM Symposium},
	series = {lncs},
	volume = {3175},
	year = {2004},
	pages = {120-128},
	publisher = {Springer},
	organization = {Springer},
	author = {Keuchel, J. and Heiler, M. and Schn{\"o}rr, C.}
}
@article {zappa2004,
	title = {Microbreaking and the enhancement of air-water transfer velocity},
	journal = {J. Geophys. Res.},
	volume = {109},
	year = {2004},
	pages = {C08S16},
	abstract = {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, <S 2>, 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.},
	doi = {10.1029/2003JC001897},
	author = {Christopher J. Zappa and William E. Asher and Jessup, A. T. and J. Klinke and S. R. Long}
}
@conference {Kohlberger-et-al-04a,
	title = {Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing},
	booktitle = {Computer Vision {\textendash} ECCV 2004},
	series = {lncs},
	volume = {3024},
	year = {2004},
	pages = {205-216},
	publisher = {Springer},
	organization = {Springer},
	author = {Kohlberger, T. and Schn{\"o}rr, C. and Bruhn, A. and Weickert, J.},
	editor = {Pajdla, T. and Matas, J.}
}
@conference {detert2004,
	title = {Pressure fluctuations within subsurface gravel bed caused by turbulent open-channel flow},
	booktitle = {Proc. of River Flow 2004},
	year = {2004},
	pages = {695-701},
	publisher = {A. A. Balkema Publishers},
	organization = {A. A. Balkema Publishers},
	author = {Detert, M. and G. H. Jirka and Markus Jehle and Michael Klar and Bernd J{\"a}hne and H.-J. K{\"o}hler and Wenka, T.}
}
@article {wenig2004,
	title = {Retrieval and analysis of stratospheric NO$_2$ from the Global Ozone Monitoring Experiment},
	journal = {J. Geophys. Res.},
	volume = {109},
	number = {D4},
	year = {2004},
	pages = {D04315, 1--11},
	abstract = {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.},
	issn = {0148-0227},
	doi = {10.1029/2003JD003652},
	author = {Mark Wenig and Kuhl, S. and S. Beirle and Bucsela, E. and Bernd J{\"a}hne and Ulrich Platt and Gleason, J. and T. Wagner}
}
@phdthesis {kuesters2004,
	title = {Simultane Tiefen- und Flussbestimmung pflanzlicher Oberfl{\"a}chen},
	year = {2004},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {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.},
	url = {http://www.ub.uni-heidelberg.de/archiv/4818},
	author = {K{\"u}sters, R.}
}
@conference {klar2004c,
	title = {Simultaneous 3D PTV and micro-pressure sensor equipment for flow analysis in subsurface gravel layer},
	booktitle = {Proc. of River Flow 2004},
	year = {2004},
	pages = {703--712},
	publisher = {A. A. Balkema Publishers},
	organization = {A. A. Balkema Publishers},
	author = {Michael Klar and Markus Jehle and Detert, M. and G. H. Jirka and Bernd J{\"a}hne and H.-J. K{\"o}hler and Wenka, T.}
}
@mastersthesis {kirchner_04_spatial,
	title = {Spatial extensions to self-modeling curve resolution},
	year = {2004},
	school = {University of Heidelberg},
	author = {Kirchner, M.}
}
@incollection {Ruhnau-et-al-04,
	title = {Variational Optical Flow Estimation for Particle Image Velocimetry},
	year = {2004},
	month = {Sept},
	publisher = {Deutsche Gesellschaft f{\"u}r Laser-Anemometrie GALA e.V.},
	chapter = {30},
	address = {Karlsruhe},
	author = {Ruhnau, P. and Kohlberger, T. and Nobach, H. and Schn{\"o}rr, C.},
	editor = {Ruck, B. and Leder, A. and Dopheide}
}
@article {Keuchel-et-al-02a,
	title = {Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming},
	volume = {25},
	number = {11},
	year = {2003},
	pages = {1364{\textendash}1379},
	author = {Keuchel, J. and Schn{\"o}rr, C. and Schellewald, C. and Daniel Cremers}
}
@conference {Kohlberger-et-al-03c,
	title = {Domain Decomposition for Parallel Variational Optical Flow Computation},
	booktitle = {Pattern Recognition, Proc. 25th DAGM Symposium},
	series = {LNCS},
	volume = {2781},
	year = {2003},
	pages = {196{\textendash}203},
	publisher = {Springer},
	organization = {Springer},
	author = {Kohlberger, T. and Schn{\"o}rr, C. and Bruhn, A. and Weickert, J.},
	editor = {Michaelis, B. and Krell, G.}
}
@techreport {Kohlberger-et-al-03a,
	title = {Domain Decomposition for Variational Optical Flow Computation},
	number = {07/2003},
	year = {2003},
	note = {\em IEEE Trans. Image Processing\/, revised and resubmitted},
	month = {May},
	institution = {Dept. Math. and Comp. Science},
	type = {Comp. Science Series, Technical Report},
	address = {University of Mannheim, Germany},
	author = {Kohlberger, T. and Schn{\"o}rr, C. and Bruhn, A. and Weickert, J.}
}
@phdthesis {kirchgessner2003,
	title = {Extraktion physiologischer Koordinatensysteme von Pflanzenwurzeln und -bl{\"a}ttern aus Bildsequenzen},
	year = {2003},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {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.},
	url = {http://www.ub.uni-heidelberg.de/archiv/3482},
	author = {Norbert Kirchge{\ss}ner}
}
@conference {jaehne2003a,
	title = {Image sequence analysis in environmental and live sciences},
	booktitle = {Proceedings of the 25th DAGM Symposium on Pattern Recognition},
	volume = {2781},
	year = {2003},
	note = {invited},
	pages = {608--617},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/978-3-540-45243-0_77},
	author = {Bernd J{\"a}hne},
	editor = {B. Michaelis and G. Krell}
}
@conference {schimpf2003a,
	title = {Observational studies of parameters influencing air--sea gas exchange},
	booktitle = {Geophysical Research Abstracts},
	volume = {5},
	year = {2003},
	pages = {09328},
	author = {Uwe Schimpf and Nelson M. Frew and Kalkenings, R. and Christoph S. Garbe and Bernd J{\"a}hne}
}
@conference {Bruhn-et-al-03a,
	title = {Real-Time Optic Flow Computation with Variational Methods},
	booktitle = {Proc. Computer Analysis of Images and Patterns (CAIP{\textquoteright}03)},
	series = {LNCS},
	volume = {2756},
	year = {2003},
	pages = {222-229},
	publisher = {Springer},
	organization = {Springer},
	author = {Bruhn, Andr{\'e}s and Weickert, Joachim and Feddern, Christian and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Petkov, N. and Westenberg, M.A.}
}
@article {Cremers-et-al-02c,
	title = {Shape Statistics in Kernel Space for Variational Image Segmentation},
	journal = {Pattern Recognition},
	volume = {36},
	number = {9},
	year = {2003},
	pages = {1929--1943},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr}
}
@article {Cremers-et-al-02c,
	title = {Shape Statistics in Kernel Space for Variational Image Segmentation},
	journal = {Pattern Recognition},
	volume = {36},
	number = {9},
	year = {2003},
	pages = {1929{\textendash}1943},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr}
}
@conference {Kohlberger-et-al-03b,
	title = {Variational Dense Motion Estimation Using the Helmholtz Decomposition},
	booktitle = {Scale Space Methods in Computer Vision},
	series = {LNCS},
	volume = {2695},
	year = {2003},
	pages = {432{\textendash}448},
	publisher = {Springer},
	organization = {Springer},
	author = {Kohlberger, T. and M{\'e}min, E. and Schn{\"o}rr, C.},
	editor = {Griffin, L.D. and Lillholm, M.}
}
@techreport {Bruhn-et-al-03b,
	title = {Variational Optic Flow Computation in Real-Time},
	number = {89},
	year = {2003},
	note = {\em IEEE Trans. Image Processing\/, revised and resubmitted},
	month = {May},
	institution = {Dept. Math. and Comp. Science},
	type = {Fachrichtung 6.1 {\textendash} Mathematik, Technical Report},
	address = {Saarland University, Germany},
	author = {Bruhn, Andr{\'e}s and Weickert, Joachim and Feddern, Christian and Kohlberger, Timo and Christoph Schn{\"o}rr}
}
@incollection {klar2002,
	title = {3D-Str{\"o}mungsmessung in Kiesporen},
	year = {2002},
	pages = {247--250},
	publisher = {Wichmann},
	url = {http://d-nb.info/96618503X},
	author = {Michael Klar and P. Stybalkowski and Hagen Spies and Bernd J{\"a}hne},
	editor = {Thomas Luhmann}
}
@article {Keuchel-et-al-03a,
	title = {Automatic Land Cover Analysis for Tenerife by Supervised Classification using Remotely Sensed Data},
	journal = {Remote Sensing of Environment},
	year = {2002},
	note = {Subitted},
	author = {Keuchel, J. and Naumann, S. and Heiler, M. and Siegmund, A.}
}
@conference {long2002,
	title = {A closer look at short waves generated by wave interactions with adverse currents},
	booktitle = {Gas Transfer at Water Surfaces},
	volume = {127},
	year = {2002},
	pages = {121--128},
	publisher = {American Geophysical Union},
	organization = {American Geophysical Union},
	author = {S. R. Long and J. Klinke},
	editor = {E. S. Saltzman and M. A. Donelan and R. Wanninkhof and W. M. Drennan}
}
@conference {Bruhn-et-al-02a,
	title = {Designing 3{\textendash}D Nonlinear Diffusion Filters for High Performance Cluster Computing},
	booktitle = {Pattern Recognition, Proc. 24th DAGM Symposium},
	series = {lncs},
	volume = {2449},
	year = {2002},
	pages = {290{\textendash}297},
	publisher = {Springer},
	organization = {Springer},
	address = {Z{\"u}rich, Switzerland},
	author = {Bruhn, Andr{\'e}s and Jakob, Tobias and Fischer, Markus and Kohlberger, Timo and Weickert, Joachim and Br{\"u}ning, Ulrich and Christoph Schn{\"o}rr},
	editor = {van Gool, L.}
}
@conference {zappa2002,
	title = {Effect of microscale wave breaking on air-water gas transfer},
	booktitle = {Gas Transfer at Water Surfaces},
	volume = {127},
	year = {2002},
	pages = {23--29},
	publisher = {American Geophysical Union},
	organization = {American Geophysical Union},
	doi = {10.1029/GM127p0023},
	author = {Christopher J. Zappa and William E. Asher and Jessup, A. T. and J. Klinke and S. R. Long},
	editor = {E. S. Saltzman and M. A. Donelan and R. Wanninkhof and W. M. Drennan}
}
@conference {scharr2002,
	title = {A linear model for simultaneous estimation of 3D motion and depth},
	booktitle = {Proceedins of IEEE Workshop on Motion and Video Computing 2002, Orlando},
	year = {2002},
	doi = {10.1109/MOTION.2002.1182240},
	author = {Hanno Scharr and K{\"u}sters, R.}
}
@conference {klar2002a,
	title = {A miniaturized 3-D particle-tracking velocimetry system to measure the pore flow within a gravel layer},
	booktitle = {Proc. 11th Int. Symp. Applications of Laser Techniques to Fluid Mechanics},
	year = {2002},
	pages = {2.3},
	url = {http://in3.dem.ist.utl.pt/lxlaser2002/papers.asp},
	author = {Michael Klar and P. Stybalkowski and Hagen Spies and Bernd J{\"a}hne}
}
@conference {Cremers-et-al-02a,
	title = {Nonlinear Shape Statistics in Mumford-Shah Based Segmentation},
	booktitle = {Computer Vision -- ECCV 2002)},
	volume = {2351},
	year = {2002},
	pages = {93--108},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Heyden, A. and Sparr, G. and Johansen, P. and Nielsen, M.}
}
@conference {Cremers-et-al-02a,
	title = {Nonlinear Shape Statistics in Mumford-Shah Based Segmentation},
	booktitle = {Computer Vision {\textendash} ECCV 2002)},
	series = {lncs},
	volume = {2351},
	year = {2002},
	pages = {93{\textendash}108},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Heyden, A. and Sparr, G. and Nielsen, M. and Johansen, P.}
}
@conference {Keuchel-et-al-02b,
	title = {Unsupervised Image Partitioning with Semidefinite Programming},
	booktitle = {Pattern Recognition, Proc. 24th DAGM Symposium},
	series = {lncs},
	volume = {2449},
	year = {2002},
	pages = {141{\textendash}149},
	publisher = {Springer},
	organization = {Springer},
	address = {Z{\"u}rich, Switzerland},
	author = {Keuchel, J. and Schn{\"o}rr, C. and Schellewald, C. and Daniel Cremers},
	editor = {van Gool, L.}
}
@conference {raisch2002,
	title = {Velocity and feature estimation of actin filaments using active contours in noisy fluorescence image sequences},
	booktitle = {Proc. 2nd IASTED Int. Conf. Visualization, Imaging and Image Processing},
	year = {2002},
	pages = {645--650},
	abstract = {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.},
	author = {Florian Raisch and Hanno Scharr and Norbert Kirchge{\ss}ner and Bernd J{\"a}hne and Rainer H. A. Fink and D. Uttenweiler}
}
@mastersthesis {klar2001,
	title = {3D Particle-Tracking Velocimetry applied to Turbulent Open-Channel Flow over a Gravel Layer},
	year = {2001},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Michael Klar}
}
@conference {Keuchel-et-al-01a,
	title = {Convex Relaxations for Binary Image Partitioning and Perceptual Grouping},
	booktitle = {Mustererkennung 2001},
	series = {Lect. Notes Comp. Science},
	volume = {2191},
	year = {2001},
	month = {Sept. 12{\textendash}14},
	pages = {353{\textendash}360},
	publisher = {Springer},
	organization = {Springer},
	address = {Munich, Germany},
	author = {Keuchel, J. and Schellewald, C. and Daniel Cremers and Schn{\"o}rr, C.},
	editor = {Radig, B. and Florczyk, S.}
}
@article {hamprecht_01_fibrillation,
	title = {Fibrillation power: An alternative method of ECG spectral analysis for prediction of countershock success in a porcine model of ventricular fibrillation},
	journal = {Resuscitation},
	volume = {50},
	year = {2001},
	pages = {287-296},
	author = {Fred A. Hamprecht and Achleitner, U. and Krismer, A. C. and Lindner, K. H. and Wenzel, V. and Strohmenger, H.-U. and Thiel, W. and van Gunsteren, W. F.}
}
@incollection {barron2001,
	title = {The fusion of image and range flow},
	year = {2001},
	pages = {174--192},
	publisher = {Springer},
	doi = {10.1007/3-540-45134-X_13},
	author = {John L. Barron and Hagen Spies},
	editor = {R. Klette and Huang, T. and Gimel{\textquoteright}farb, G.}
}
@conference {Schellewald-et-al-01a,
	title = {Image labeling and grouping by minimizing linear functionals over cones},
	booktitle = {Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR{\textquoteright}01)},
	series = {Lect. Notes Comp. Science},
	volume = {2134},
	year = {2001},
	month = {Sept. 3{\textendash}5},
	pages = {267{\textendash}282},
	publisher = {Springer},
	organization = {Springer},
	address = {INRIA, Sophia Antipolis, France},
	author = {Schellewald, C. and Keuchel, J. and Schn{\"o}rr, C.},
	editor = {Figueiredo, M. and Zerubia, J. and Jain, A.K.}
}
@conference {wenig2001a,
	title = {Image sequence analysis of satellite NO$_2$ concnetration maps},
	booktitle = {Pattern Recognition, 23rd DAGM Symposium Munich},
	volume = {2191},
	year = {2001},
	pages = {223--230},
	publisher = {Springer},
	organization = {Springer},
	abstract = {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.},
	doi = {10.1007/3-540-45404-7_30},
	author = {Mark Wenig and Carsten Leue and S. Kraus and T. Wagner and Ulrich Platt and Bernd J{\"a}hne},
	editor = {B. Radig and S. Florczyk}
}
@conference {Cremers-et-al-01b,
	title = {Nonlinear Shape Statistics via Kernel Spaces},
	booktitle = {Mustererkennung 2001},
	volume = {2191},
	year = {2001},
	pages = {269--276},
	publisher = {Springer},
	organization = {Springer},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {B. Radig and S. Florczyk}
}
@conference {Cremers-et-al-01b,
	title = {Nonlinear Shape Statistics via Kernel Spaces},
	booktitle = {Mustererkennung 2001},
	series = {Lect. Notes Comp. Science},
	volume = {2191},
	year = {2001},
	month = {Sept. 12{\textendash}14},
	pages = {269{\textendash}276},
	publisher = {Springer},
	organization = {Springer},
	address = {Munich, Germany},
	author = {Daniel Cremers and Kohlberger, Timo and Christoph Schn{\"o}rr},
	editor = {Radig, B. and Florczyk, S.}
}
@mastersthesis {kuesters2001,
	title = {Objektverfolgung und Bildfusion zur Untersuchung wachsender Pflanzenwurzeln},
	year = {2001},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {K{\"u}sters, R.}
}
@incollection {huang2001,
	title = {Ocean wave spectra and integral properties},
	year = {2001},
	pages = {82--123},
	publisher = {Cambridge University Press},
	doi = {10.1017/CBO9780511552076.005},
	author = {N. E. Huang and Y. Toba and Z. Shen and J. Klinke and Bernd J{\"a}hne and M. L. Banner},
	editor = {I. S. F. Jones and Y Toba}
}
@article {hamprecht_01_preliminary,
	title = {Preliminary results on the prediction of countershock success with fibrillation power},
	journal = {Resuscitation},
	volume = {50},
	year = {2001},
	pages = {297-299},
	author = {Fred A. Hamprecht and Jost, D. and R{\"u}ttimann, M. and Calamai, F. and Kowalski, J. J.}
}
@article {leue2001,
	title = {Quantitative analysis of NO$_x$ emissions from Global Ozone Monitoring Experiment satellite image sequences},
	journal = {J. Geophys. Res.},
	volume = {106},
	number = {D6},
	year = {2001},
	pages = {5493--5505},
	abstract = {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 {\textpm} 20) Tg N yr-1 is estimated. The accuracy of this method is comparable to that of established statistical approaches.},
	doi = {10.1029/2000JD900572},
	author = {Carsten Leue and Mark Wenig and T. Wagner and Klimm, O. and Ulrich Platt and Bernd J{\"a}hne}
}
@conference {kirchgessner2001,
	title = {Root Growth Analysis in Physiological Coordinates},
	booktitle = {International Conference on Image Analysis and Processing (ICIAP{\textquoteright}01)},
	year = {2001},
	author = {Norbert Kirchge{\ss}ner and Hagen Spies and Hanno Scharr and Ulrich Schurr}
}
@conference {kirchgessner2001a,
	title = {Root Growth Measurements in Object Coordinates},
	booktitle = {Proceedings of the 23th DAGM Symposium on Pattern Recognition},
	year = {2001},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/3-540-45404-7_31},
	author = {Norbert Kirchge{\ss}ner and Hagen Spies and Hanno Scharr and Ulrich Schurr}
}
@conference {klinke2001,
	title = {Wavenumber Spectra of Short Wind Waves: Laboratory Measurements and Interpretation},
	booktitle = {IGARSS {\textquoteright}01, Geoscience and Remote Sensing Symposium, Sydney, NSW, Australia},
	volume = {2},
	year = {2001},
	pages = {965-967},
	abstract = {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},
	doi = {10.1109/IGARSS.2001.976695},
	author = {J. Klinke and Kudryavtsev, V. N. and Makin, V. K. and Bernd J{\"a}hne}
}
@conference {kirchgessner2000,
	title = {3D-Modellierung von Pflan-zen-bl{\"a}t-tern mittels eines Depth-from-Motion Verfahrens},
	booktitle = {Proceedings of the 22th DAGM Symposium on Pattern Recognition},
	year = {2000},
	pages = {381--388},
	doi = {10.1007/978-3-642-59802-9_48},
	author = {Norbert Kirchge{\ss}ner and Hanno Scharr and Ulrich Schurr}
}
@conference {spies2000c,
	title = {Dense structure estimation via regularised optical flow},
	booktitle = {VMV 2000},
	year = {2000},
	pages = {57--64},
	publisher = {Aka GmbH, Berlin},
	organization = {Aka GmbH, Berlin},
	author = {Hagen Spies and Norbert Kirchge{\ss}ner and Hanno Scharr and Bernd J{\"a}hne},
	editor = {Girod, G. and Greiner, G. and Hans-Peter Seidel and H. Niemann}
}
@mastersthesis {klimm2000,
	title = {Einfluss von Bodenalbedo und Wolkenbedeckung auf satellitengest{\"u}tzte O$_4$-Spurenstoffmessungen},
	year = {2000},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ. \ Heidelberg},
	author = {Klimm, Oliver}
}
@conference {scharr2000c,
	title = {Flame front analysis in turbulent combustion},
	booktitle = {Mustererkennung 2000},
	year = {2000},
	pages = {325--332},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-59802-9_41},
	author = {Hanno Scharr and Bernd J{\"a}hne and S. B{\"o}ckle and J. Kazenwadel and T. Kunzelmann and C. Schulz and N. Kr{\"u}ger},
	editor = {G. Sommer and Ch. Perwasser}
}
@conference {klinke2000,
	title = {Generation of short waves by wave-current interaction},
	booktitle = {Geoscience and Remote Sensing Symposium, 2000. Proceedings. IGARSS 2000. IEEE 2000 International},
	year = {2000},
	pages = {1084--1086},
	doi = {10.1109/IGARSS.2000.858029},
	author = {J. Klinke and S. R. Long}
}
@conference {spies2000,
	title = {Material transport and structure changes at soil-water interfaces},
	booktitle = {Filters and Drainage in Geotechnical and Environmental Engineering},
	year = {2000},
	pages = {91--97},
	author = {Hagen Spies and Horst Hau{\ss}ecker and H.-J. K{\"o}hler}
}
@incollection {jaehne2000a,
	title = {Performance characteristics of low-level motion estimation in spatiotemporal images},
	year = {2000},
	note = {invited, Workshop Schloss Dagstuhl, March 16-20, 1998},
	pages = {pp. 139-152},
	publisher = {Kluwer, Dordrecht},
	doi = {10.1007/978-94-015-9538-4_12},
	author = {Bernd J{\"a}hne and Horst Hau{\ss}ecker},
	editor = {R. Klette and H. S. Stiehl and K. Vincken and M. Viergever}
}
@incollection {kuemmerlen2000,
	title = {Thermography to measure water relations of plant leaves},
	year = {2000},
	pages = {636--637},
	publisher = {Academic Press},
	chapter = {A14},
	author = {Bernd K{\"u}mmerlen and Stefan Dauwe and Dominik Schmund and Ulrich Schurr and Bernd J{\"a}hne and Horst Hau{\ss}ecker}
}
@inbook {stoehr1999,
	title = {4D particle tracking velocimetry applied to gas-liquid reactors},
	booktitle = {In Scientific Computing in Chemical Engineering II - Simulation, Image Processing, Optimization and Control.},
	year = {1999},
	pages = {270--279},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {M. St{\"o}hr and Christoph S. Garbe and D. Engelmann and Peter Gei{\ss}ler and Gomes, S. and Frank Hering and Bernd J{\"a}hne and F. Keil and W. Mackens and H. Vo{\ss} and Wagner, H.-G..},
	editor = {J. Werther}
}
@conference {koehler1999,
	title = {Fluidisation and deformation of submerged soil due to fluctuating water level},
	booktitle = {XII Europ. Conference on Soil Mechanics and Geotechnical Engineering},
	year = {1999},
	pages = {1109--1115},
	author = {H.-J. K{\"o}hler and Hagen Spies and Oliver Beringer and Horst Hau{\ss}ecker}
}
@conference {klinke1999,
	title = {Observations of free and bound gravity-capillary Waves},
	booktitle = {The Wind-Driven Air-Sea Interface, Electromagnetic and Acoustic Sensing, Wave Dynamics and Turbulent Fluxes},
	year = {1999},
	pages = {87--88},
	doi = {10.5281/zenodo.14908},
	author = {J. Klinke and Bernd J{\"a}hne and S. R. Long}
}
@incollection {jaehne1999k,
	title = {Principles of Filter Design},
	volume = {2},
	year = {1999},
	pages = {125--151},
	publisher = {Academic Press},
	author = {Bernd J{\"a}hne and Hanno Scharr and K{\"o}rkel, S. and Bernd J{\"a}hne and Horst Hau{\ss}ecker and Peter Gei{\ss}ler}
}
@conference {zhang1999b,
	title = {A study of advection of short wind waves by long waves from surface slope images},
	booktitle = {The Wind-Driven Air-Sea Interface, Electromagnetic and Acoustic Sensing, Wave Dynamics and Turbulent Fluxes},
	year = {1999},
	pages = {93--97},
	doi = {10.5281/zenodo.14909},
	author = {Zhang, X. and J. Klinke and Bernd J{\"a}hne}
}
@incollection {kuemmerlen1999,
	title = {Thermography to measure water relations of plant leaves},
	volume = {3: Systems and Applications},
	year = {1999},
	pages = {763-781},
	publisher = {Academic Press},
	chapter = {36},
	author = {Bernd K{\"u}mmerlen and Stefan Dauwe and Dominik Schmund and Ulrich Schurr and Bernd J{\"a}hne and Peter Gei{\ss}ler and Horst Hau{\ss}ecker}
}
@mastersthesis {kirchgessner1999,
	title = {Voruntersuchungen zur 3D-Wuchsanalyse von Pflanzenbl{\"a}ttern und die Modellierung der Blattadern},
	year = {1999},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Norbert Kirchge{\ss}ner}
}
@mastersthesis {kuemmerlen1998,
	title = {Infrarot-Thermographie zum Studium physiologischer Parameter von Pflanzenbl{\"a}ttern},
	year = {1998},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Bernd K{\"u}mmerlen}
}
@conference {balschbach1998,
	title = {Multichannel shape from shading techniques for moving specular surfaces},
	booktitle = {ECCV 1998},
	volume = {1407},
	year = {1998},
	pages = {170--184},
	publisher = {Springer, Berlin},
	organization = {Springer, Berlin},
	doi = {10.1007/BFb0054740},
	author = {G{\"u}nther Balschbach and J. Klinke and Bernd J{\"a}hne},
	editor = {Burkhardt, H. and Neumann, B.}
}
@article {kubinyi_98_threedimensional,
	title = {Threedimensional Quantitative Similarity-Activity Relationships (3DQSiAR) from SEAL Similarity Matrices},
	journal = {Journal of Medicinal Chemistry},
	volume = {41},
	year = {1998},
	pages = {2553-2564},
	author = {Kubinyi, H. and Fred A. Hamprecht and Mietzner, T.}
}
@conference {hering1997,
	title = {A comprehensive study of algorithms for multidimensional flow field diagnostics},
	booktitle = {Proc. Optical 3D Measurement Techniques IV, Zurich, Sept. 29 - Oct. 2, 1997},
	year = {1997},
	pages = {436--443},
	publisher = {Wichmann},
	organization = {Wichmann},
	author = {Frank Hering and Horst Hau{\ss}ecker and Jochen Dieter and T. Netzsch and Bernd J{\"a}hne},
	editor = {A. Gr{\"u}n and H. Kahmen}
}
@conference {dieter1997,
	title = {Measurements of slope statistics on a wind driven water surface},
	booktitle = {Optical 3D Measurement Techniques 4 - Applications in architecture, quality control, robotics, navigation, medical imaging and animation},
	year = {1997},
	publisher = {Wichmann},
	organization = {Wichmann},
	author = {Jochen Dieter and Lauer, Hermann and Bernd J{\"a}hne and A. Gr{\"u}n},
	editor = {H. Kahmen}
}
@conference {geissler1997a,
	title = {A multi-camera system for in-shore measurements of bubble size distributions beneath breaking waves},
	booktitle = {Optical 3-D Measurement Techniques IV, Zurich, Sept. 29 - Oct. 2, 1997},
	year = {1997},
	pages = {251--258},
	publisher = {Wichmann},
	organization = {Wichmann},
	author = {Peter Gei{\ss}ler and Bernd J{\"a}hne},
	editor = {A. Gr{\"u}n and H. Kahmen}
}
@conference {balschbach1997,
	title = {Multichannel shape from shading techniques for reconstruction of specular surfaces},
	booktitle = {Tagungsband Herbsttagung des Graduiertenkollegs "3D Bildanalyse und -synthese"},
	year = {1997},
	publisher = {H.-P. Seidel, B. Girod, H. Niemann (Hrsg.)},
	organization = {H.-P. Seidel, B. Girod, H. Niemann (Hrsg.)},
	author = {G{\"u}nther Balschbach and J. Klinke and Bernd J{\"a}hne}
}
@conference {scharr1997,
	title = {Numerische Isotropieoptimierung von FIR-Filtern mittels Quergl{\"a}ttung},
	booktitle = {Proceedings of the 19th DAGM Symposium on Pattern Recognition, Braunschweig},
	year = {1997},
	pages = {199--208},
	doi = {10.1007/978-3-642-60893-3_39},
	author = {Hanno Scharr and K{\"o}rkel, S. and Bernd J{\"a}hne},
	editor = {E. Paulus and F. M. Wahl}
}
@incollection {liss1997,
	title = {Report Group 1 - Physical processes in the microlayer and the air-sea exchange of trace gases},
	year = {1997},
	pages = {1--33},
	publisher = {Cambridge University Press},
	doi = {10.1017/CBO9780511525025.002},
	author = {Peter S. Liss and A. J. Watson and E. J. Bock and Bernd J{\"a}hne and William E. Asher and Nelson M. Frew and L. Hasse and Gerald M. Korenowski and L. Merlivat and L. F. Phillips and P. Schl{\"u}ssel and D. K. Woolf},
	editor = {Peter S. Liss and Robert A. Duce}
}
@conference {geissler1996,
	title = {A 3D-sensor for the measurement of particle concentration from image sequences},
	booktitle = {Proc. 18th Int. Congr. for Photogrammetry and Remote Sensing},
	volume = {31},
	number = {B5},
	year = {1996},
	pages = {194--199},
	doi = {10.5281/zenodo.14895},
	url = {http://www.isprs.org/proceedings/XXXI/congress/part5/},
	author = {Peter Gei{\ss}ler and Bernd J{\"a}hne and P. Waldh{\"a}usl},
	editor = {K. Kraus}
}
@conference {koehler1996,
	title = {Detection of particle movements at soil interfaces due to changing hydraulic load conditions, localised by a digital image processing technique},
	booktitle = {Proc. Geofilters 1996, Montreal},
	year = {1996},
	publisher = {Ecole Polytechnique Montreal},
	organization = {Ecole Polytechnique Montreal},
	author = {H.-J. K{\"o}hler and Horst Hau{\ss}ecker and Bernd J{\"a}hne},
	editor = {Lafleur, J. and Rollin, A. L.}
}
@conference {spedding1996,
	title = {Estimating $\omega(k)$ in an unsteady, wind-generated surface wave field from the 2D complex wavelet transform of the surface slope},
	booktitle = {Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993},
	year = {1996},
	pages = {373--382},
	publisher = {RSMAS, University of Miami},
	organization = {RSMAS, University of Miami},
	doi = {10.5281/zenodo.14564},
	author = {G. R. Spedding and J. Klinke and S. R. Long},
	editor = {M. A. Donelan and W. H. Hui and W. J. Plant}
}
@conference {hering1996a,
	title = {A novel system for the combined measurement of wave- and flow-fields beneath wind induced water waves},
	booktitle = {Proc. 18th Int. Congr. for Photogrammetry and Remote Sensing},
	volume = {31},
	number = {B5},
	year = {1996},
	pages = {231--236},
	doi = {10.5281/zenodo.14894},
	url = {http://www.isprs.org/proceedings/XXXI/congress/part5/},
	author = {Frank Hering and G{\"u}nther Balschbach and Bernd J{\"a}hne and P. Waldh{\"a}usl},
	editor = {K. Kraus}
}
@phdthesis {klinke1996,
	title = {Optical Measurements of Small-Scale Wind Generated Water Surface Waves in the Laboratory and the Field},
	year = {1996},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {J. Klinke}
}
@conference {jaehne1996a,
	title = {The role of active vision in exploring growth, transport, and exchange processes},
	booktitle = {Aktives Sehen in technischen und biologischen Systemen, Workshop der GI-Fachgruppe 1.0.4. Bildverstehen Hamburg, 3--4. December 1996},
	volume = {4},
	year = {1996},
	pages = {194--202},
	publisher = {infix},
	organization = {infix},
	author = {Bernd J{\"a}hne and Horst Hau{\ss}ecker and Frank Hering and G{\"u}nther Balschbach and J. Klinke and Martin Lell and Dominik Schmund and Michael Schultz and Ulrich Schurr and Mark Stitt and Ulrich Platt},
	editor = {B Mertsching}
}
@conference {klinke1996a,
	title = {Wave number spectra of short wind waves: implications from laboratory studies},
	booktitle = {Proc.\ The Air-Sea Interface, Radio and Acoustic Sensing, Turbulence and Wave Dynamics, Marseille, 24--30. June 1993},
	year = {1996},
	pages = {367--372},
	publisher = {RSMAS, University of Miami},
	organization = {RSMAS, University of Miami},
	doi = {10.5281/zenodo.14001},
	author = {J. Klinke and Bernd J{\"a}hne},
	editor = {M. A. Donelan and W. H. Hui and W. J. Plant}
}
@conference {bock1995b,
	title = {Description of the science plan for the April 1995 CoOP experiment, {\textquoteleft}gas transfer in coastal waters{\textquoteright}, performed from the research vessel New Horizon},
	booktitle = {Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer},
	year = {1995},
	pages = {801--810},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10408},
	author = {E. J. Bock and J. B. Edson and Nelson M. Frew and A. Karachintsev and W. R. McGilles and R. K. Nelson and K. Hansen and Bernd J{\"a}hne and T. Hara and B. M. Uz and Bernd J{\"a}hne and Jochen Dieter and J. Klinke and Horst Hau{\ss}ecker},
	editor = {E. C. Monahan}
}
@conference {haussecker1995d,
	title = {Effiziente Filterstrukturen auf Mehrgitter-Datenstrukturen},
	booktitle = {Bildverarbeitung{\textquoteright}95 - Forschen, Entwickeln, Anwenden},
	year = {1995},
	pages = {43--57},
	publisher = {Technische Akademie Esslingen},
	organization = {Technische Akademie Esslingen},
	doi = {10.5281/zenodo.14709},
	author = {Horst Hau{\ss}ecker and Bernd J{\"a}hne and H.-J. K{\"o}hler},
	editor = {Ahlers, R. -J.}
}
@conference {klinke1995,
	title = {Measurements of short ocean waves during the MBL ARI West Coast Experiment},
	booktitle = {Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer},
	year = {1995},
	pages = {165--173},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10399},
	author = {J. Klinke and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {klinke1995b,
	title = {A new instrument for the optical measurement of the fine structure of the water surface in the field},
	booktitle = {IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves},
	number = {19},
	year = {1995},
	pages = {388},
	abstract = {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.},
	author = {J. Klinke and Bernd J{\"a}hne}
}
@conference {klinke1995a,
	title = {Spatial measurement of short ocean waves during the MBL-ARI West Coast Experiment},
	booktitle = {IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves},
	number = {19},
	year = {1995},
	pages = {390},
	abstract = {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.},
	author = {J. Klinke and Bernd J{\"a}hne}
}
@conference {frew1995a,
	title = {Variation of air--water gas transfer with wind stress and surface viscoelasticity},
	booktitle = {Air-water Gas Transfer, Selected Papers from the Third International Symposium on Air-Water Gas Transfer},
	year = {1995},
	pages = {529--541},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10405},
	author = {Nelson M. Frew and E. J. Bock and W. R. McGilles and A. Karachintsev and T. Hara and Thomas M{\"u}nsterer and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {Schnoerr-dagm-94,
	title = {Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale},
	booktitle = {Mustererkennung 1994},
	series = {Informatik Xpress},
	volume = {5},
	year = {1994},
	month = {September},
	pages = {178{\textendash}185},
	publisher = {Technische Universit{\"a}t Wien},
	organization = {Technische Universit{\"a}t Wien},
	author = {Schn{\"o}rr, C.},
	editor = {Kropatsch, W.G. and Bischof, H.}
}
@conference {Heikkonen-icpr-94,
	title = {Building Trajectories via Selforganization from Spatiotemporal Features},
	booktitle = {12th Int. Conf. on Pattern Recognition},
	year = {1994},
	month = {Oct 9-13},
	address = {Jerusalem, Israel},
	author = {Heikkonen, J. and Koikkalainen, P. and Schn{\"o}rr, C.}
}
@conference {Sprengel-Schnoerr-dagm-94,
	title = {Detection of Visual Data Transitions in Nonlinear Parameter Space},
	booktitle = {Mustererkennung 1994},
	series = {Informatik Xpress},
	volume = {5},
	year = {1994},
	month = {September},
	pages = {315{\textendash}323},
	publisher = {Technische Universit{\"a}t Wien},
	organization = {Technische Universit{\"a}t Wien},
	author = {Sprengel, R. and Schn{\"o}rr, C. and Neumann, B.},
	editor = {Kropatsch, W.G. and Bischof, H.}
}
@mastersthesis {kandlbinder1994,
	title = {Gasaustauschmessungen mit Sauerstoff},
	year = {1994},
	note = {IUP D-385},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Thomas Kandlbinder}
}
@article {jaehne1994d,
	title = {Imaging of short ocean wind waves: a critical theoretical review},
	journal = {J. Opt. Soc. Am. A},
	volume = {11},
	year = {1994},
	pages = {2197--2209},
	abstract = {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.},
	doi = {10.1364/JOSAA.11.002197},
	author = {Bernd J{\"a}hne and J. Klinke and Stefan Waas}
}
@conference {klinke1994,
	title = {Measurements of the small-scale structure of the water surface with a new optical instrument},
	booktitle = {Proc. 2nd Inter. Conf. on Air-Sea Interaction and on Meteorology and Oceanography of the Coastal Zone, Lisbon, 22.--27. September 1994},
	year = {1994},
	author = {J. Klinke and Bernd J{\"a}hne}
}
@conference {Schnoerr-et-al-93,
	title = {Architekturkonzepte zur Bildauswertung},
	booktitle = {Grundlagen und Anwendungen der K{\"u}nstlichen Intelligenz, 17. Fachtagung f{\"u}r K{\"u}nstliche Intelligenz},
	year = {1993},
	pages = {268{\textendash}274},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Berlin},
	author = {Schn{\"o}rr, C. and Niemann, H. and Kopecz, J.},
	editor = {Herzog, O. and Christaller, T. and Sch{\"u}tt, D.}
}
@conference {jaehne1993,
	title = {Image sequence analysis of ocean wind waves},
	booktitle = {Imaging in Transport Processes},
	year = {1993},
	pages = {257--268},
	publisher = {Begell House Publishers},
	organization = {Begell House Publishers},
	url = {http://www.dl.begellhouse.com/references/1bb331655c289a0a,36adf33e6f249361.html},
	author = {Bernd J{\"a}hne and J. Klinke and Peter Gei{\ss}ler and Frank Hering},
	editor = {S. Sideman and K. Hijikata}
}
@conference {jaehne1993d,
	title = {Shape from shading techniques for short ocean wind waves},
	booktitle = {Imaging in Transport Processes},
	year = {1993},
	pages = {269--281},
	publisher = {Begell House Publishers},
	organization = {Begell House Publishers},
	url = {http://www.dl.begellhouse.com/references/1bb331655c289a0a,36adf33e6f249361.html},
	author = {Bernd J{\"a}hne and Stefan Waas and J. Klinke},
	editor = {S. Sideman and K. Hijikata}
}
@article {jaehne1993f,
	title = {Wellenamplitudenmessungen mittels videometrischer Bildverarbeitung},
	journal = {Mitteilungsblatt der Bundesanstalt f{\"u}r Wasserbau},
	volume = {70},
	year = {1993},
	pages = {27--62},
	author = {Bernd J{\"a}hne and H.-J. K{\"o}hler and Ralf Rath and Dietmar Wierzimok}
}
@conference {klinke1992,
	title = {2D wave number spectra of short wind waves: results from wind-wave facilities and extrapolation to the ocean},
	booktitle = {Optics of the Air-Sea Interface: Theory and Measurement},
	volume = {1749},
	year = {1992},
	pages = {245-257},
	doi = {10.1117/12.138853},
	author = {J. Klinke and Bernd J{\"a}hne},
	editor = {L. Estep}
}
@conference {jaehne1992,
	title = {A critical theoretical review of optical techniques for short ocean wave measurements},
	booktitle = {Optics of the Air-Sea Interface: Theory and Measurements},
	volume = {1749},
	year = {1992},
	pages = {204--215},
	doi = {10.1117/12.138849},
	author = {Bernd J{\"a}hne and Stefan Waas and J. Klinke},
	editor = {L. Estep}
}
@mastersthesis {klinke1991,
	title = {Zweidimensionale Wellenzahlspektren von kleinskaligen winderzeugten Wasseroberfl{\"a}chenwellen},
	year = {1991},
	note = {IUP D-358},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {J. Klinke}
}
@conference {jaehne1989,
	title = {Energy balance in small-scale waves: an experimental approach using optical slope measuring technique and image processing},
	booktitle = {Radar Scattering from Modulated Wind Waves},
	year = {1989},
	pages = {105--120},
	publisher = {Kluwer Academic Publishers},
	organization = {Kluwer Academic Publishers},
	doi = {10.1007/978-94-009-2309-6_10},
	author = {Bernd J{\"a}hne},
	editor = {Komen, G. J. and W. A. Oost}
}
@inbook {halsema1989a,
	title = {First results of the VIERS-1 experiment},
	booktitle = {Radar Scattering from Modulated Wind Waves},
	year = {1989},
	pages = {49--57},
	publisher = {Kluwer Academic Publishers},
	organization = {Kluwer Academic Publishers},
	doi = {10.1007/978-94-009-2309-6_5},
	author = {Halsema, D. and Bernd J{\"a}hne and C. J. Calkoen and P. Snoeij},
	editor = {W. A. Oost and Komen, G. J. and W. A. Oost}
}
@mastersthesis {klein1988,
	title = {Entwicklung einer Videoaufnahmetechnik und eines Computerbildauswertungsverfahren zur Aufnahme und Auswertung von Blasenverteilungen},
	year = {1988},
	note = {IUP D-237},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Ralf Klein}
}
