@phdthesis {7109,
	title = {Discovering Structure without Labels},
	year = {2022},
	author = {Damrich, S}
}
@mastersthesis {7075,
	title = {Deep k-segments: a generalization of k-means},
	year = {2021},
	school = {Heidelberg University},
	author = {Ruiz, A}
}
@phdthesis {7066,
	title = {Deep Learning for Graph-Based Image Instance Segmentation},
	year = {2021},
	publisher = {Heidelberg University},
	author = {Bailoni, A}
}
@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}
}
@proceedings {7084,
	title = {Directed Probabilistic Watershed},
	volume = {34},
	year = {2021},
	author = {Fita, E and Damrich, S and Hamprecht, FA}
}
@proceedings {7038,
	title = {Das Objekt jenseits der Digitalisierung},
	volume = {7},
	year = {2020},
	abstract = {Der technische Fortschritt der letzten Jahrzehnte hat disruptive Ver{\"a}nderungen f{\"u}r Gesellschaft, Wirtschaft und Wissenschaft gebracht: Die Digitalisierung ist ein Resultat dessen und beeinflusst, wie wir auf Daten zugreifen, diese verarbeiten, analysieren und Ergebnisse verbreiten. Obwohl dadurch bereits ein Wandel eingeleitet worden ist, kann das Digitalisieren von Textdokumenten oder Bildern nicht das endg{\"u}ltige Ziel sein. Der Fokus aktueller Bestrebungen sollte vielmehr auf der M{\"o}glichkeit der Weiterverarbeitung von Digitalisaten liegen {\textendash} dies schlie{\ss}t eine intelligente Informationsverarbeitung ein. Der Wert der Digitalisierung besteht nicht in der blo{\ss}en Anh{\"a}ufung digitaler Sammlun- gen, sondern in der Tatsache, dass sie weitaus mehr erm{\"o}glicht als das Analoge und daf{\"u}r die notwendigen Grundvoraussetzungen schafft.
Die Problematik besteht nun darin, dass die meisten Verarbeitungs- und Analyse- methoden f{\"u}r digitale Daten noch analog oder diesen nachempfunden sind: So werden digitale Sammlungen und darin enthaltene Bilder h{\"a}ufig noch mit den eigenen Augen, in traditionell komparativer Weise betrachtet und evaluiert. Dass dies aufgrund der F{\"u}lle an Daten nicht effizient ist, muss an dieser Stelle nicht betont werden. Obwohl das ana- loge und das digitale Bild den gleichen Inhalt zeigen k{\"o}nnen, haben beide doch ganz unterschiedliche Substrate. Ein Unterschied besteht zum Beispiel darin, dass digitale Bilder im Gegensatz zu analogen einfach manipuliert und dupliziert werden k{\"o}nnen. Das Digitale ist nicht das Analoge in neuer Form, und so bedarf es genuin digitaler Methoden f{\"u}r die Verarbeitung digitaler Daten. Durch die Entwicklung computerge- st{\"u}tzter Verfahren entstehen neue M{\"o}glichkeiten, Inhalte zu erschlie{\ss}en: Dazu geh{\"o}ren Ans{\"a}tze zur Objektsuche oder das Gruppieren und Sortieren der Daten entsprechend benutzerdefinierter Dimensionen; dies schlie{\ss}t {\"u}bergeordnete Kategorien wie Stil oder Genre, aber auch nuancierte Begriffe wie Alter oder Gewichtung der Bildkomposition ein. Doch das Digitale und entsprechende Verfahren k{\"o}nnen noch weitaus mehr leisten: Generative Verfahren, wie die Bildsynthese und Stilisierung eines Bildes, erm{\"o}glichen eine Blick{\"a}nderung auf das Artefakt und schlie{\ss}lich die Modifizierung des Objekts selbst. Wie h{\"a}tte ein K{\"u}nstler eine uns sichtbare Szene gemalt und dargestellt? Und wie sieht ein Mensch in der Pose eines anderen aus? Dies sind Fragen, die durch die Anwendung com- putergest{\"u}tzter Methoden beantwortet werden k{\"o}nnen. F{\"u}r das Museum haben diese Ans{\"a}tze eine besondere Relevanz, da sie neue Arten des Betrachtens und Vermittelns von Kunstwerken oder zum Beispiel die Rekonstruktion verlorener Artefakte erlauben. In Zusammenarbeit von Mensch und Maschine entstehen so neue effektive Verfahren, die Inhalte erschlie{\ss}en, Verbindungen etablieren und neues Wissen generieren.},
	isbn = {978-3-948808-00-6},
	url = {http://www.deutsches-museum.de/fileadmin/Content/010_DM/060_Verlag/studies-7.pdf},
	author = {Sabine Lang and Bj{\"o}rn Ommer}
}
@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}
}
@proceedings {6932,
	title = {A Disentangling Invertible Interpretation Network for Explaining Latent Representations},
	year = {2020},
	abstract = {Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation.},
	url = {https://compvis.github.io/iin/},
	author = {Patrick Esser and Robin Rombach and Bj{\"o}rn Ommer}
}
@proceedings {6934,
	title = {DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning},
	year = {2020},
	url = {https://arxiv.org/abs/2004.13458},
	author = {Timo Milbich and Karsten Roth and Homanga Bharadhwaj and Samarth Sinha and Yoshua Bengio and Bj{\"o}rn Ommer and Joseph Paul Cohen}
}
@article {lu2019,
	title = {Decomposing infrared images of wind waves for quantitative separation into characteristic flow processes},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {57},
	year = {2019},
	pages = {8304{\textendash}8316},
	doi = {10.1109/TGRS.2019.2920280},
	author = {Guan-hung Lu and Wu-ting Tsai and Bernd J{\"a}hne}
}
@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.}
}
@conference {Li2019,
	title = {Deep Object Co-segmentation},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {11363 LNCS},
	year = {2019},
	pages = {638{\textendash}653},
	abstract = {This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on common objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.},
	isbn = {9783030208929},
	issn = {16113349},
	doi = {10.1007/978-3-030-20893-6_40},
	author = {Weihao Li and Omid Hosseini Jafari and Carsten Rother}
}
@mastersthesis {papst2019,
	title = {Development of a method for quantitative imaging of air-water gas exchange},
	volume = {Bachelors thesis},
	year = {2019},
	school = {Institut f{\"u}r Umweltphysik, Universit{\"a}t Heidelberg, Germany},
	type = {mastersMaster{\textquoteright}s thesis},
	doi = {10.11588/heidok.00027271},
	author = {Maximilian Papst}
}
@article {Savchynskyy2019,
	title = {Discrete Graphical Models {\textemdash} An Optimization Perspective},
	journal = {Foundations and Trends{\textregistered} in Computer Graphics and Vision},
	volume = {11},
	number = {3-4},
	year = {2019},
	pages = {160{\textendash}429},
	publisher = {Now Publishers},
	abstract = {This monograph is about combinatorial optimization. More precisely, about a special class of combinatorial problems known as energy minimization or maximum a posteriori (MAP) inference in graphical models, closely related to weighted and valued constraint satisfaction problems and having tight connections to Markov random fields and quadratic pseudo-boolean optimization. What distinguishes this monograph from a number of other monographs on graphical models is its focus: It considers graphical models, or, more precisely, MAP-inference for graphical models, purely as a combinatorial optimization problem. Modeling, applications, probabilistic interpretations and many other aspects are either ignored here or find their place in examples and remarks only. Combinatorial optimization as a field is largely based on five fundamental topics: (i) integer linear programming and polyhedral optimization; (ii) totally unimodular matrices and the class of min-cost-flow problems; (iii) Lagrange decompositions and relaxations; (iv) dynamic programming and (v) submodularity, matroids and greedy algorithms. Each of these topics found its place in this monograph, although to a variable extent. The covering of each respective topic reflects its importance for the considered MAP-inference problem. Since optimization is the primary topic of this monograph, we mostly stick to the terminology widely used in optimization and where it was possible we tried to avoid the graphical models community-specific technical terms. The latter differ from sub-community to sub-community and, in our view, significantly complicate the information exchange between them. The same holds also for the presentation of material in this monograph. If there is a choice when introducing mathematical constructs or proving statements, we prefer more general mathematical tools applicable in the whole field of operations research rather than to stick to graphical modelspecific constructions. We additionally provide the graphical model-specific constructions if it turns out to be easier than the more general one. This way of presentation has two advantages. A reader familiar with a more general technique can grasp the new material faster. On the other hand, the monograph may serve as an introduction to combinatorial optimization for readers unfamiliar with this subject. To make the monograph even more suitable for both categories of readers we explicitly separate the mathematical optimization background chapters from those specific to graphical models. We believe, therefore, that the monograph can be useful for undergraduate and graduate students studying optimization or graphical models, as well as for experts in optimization who want to have a look into graphical models. Moreover, we believe that even experts in graphical models can find new views on the known facts as well as a novel presentation of less known results in the monograph. These are for instance (i) a simple and general proof of equivalence of different acyclic Lagrange decompositions of a graphical model; (ii) a general scheme for analyzing convergence of dual block-coordinate descent methods; (iii) a short and self-contained analysis of a linear programming relaxation for binary graphical models, its persistency properties and its relation to quadratic pseudo-boolean optimization. The present monograph is based on lectures given to undergraduate students of Technical University of Dresden and Heidelberg University. The selection of material is done in a way that it may serve as a basis for a semester course.},
	issn = {1572-2740},
	doi = {10.1561/0600000084},
	author = {Savchynskyy, Bogdan}
}
@proceedings {6299,
	title = {Divide and Conquer the Embedding Space for Metric Learning},
	year = {2019},
	keywords = {deep learning, metric learning},
	url = {https://github.com/CompVis/metric-learning-divide-and-conquer},
	author = {Sanakoyeu, A. and Tschernezki, V. and Uta B{\"u}chler and Bj{\"o}rn Ommer}
}
@mastersthesis {6273,
	title = {Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation},
	year = {2018},
	school = {Heidelberg University},
	author = {Cerrone, L}
}
@article {6229,
	title = {Deep Unsupervised Learning of Visual Similarities},
	journal = {Pattern Recognition},
	volume = {78},
	year = {2018},
	chapter = {331},
	abstract = {Exemplar learning of visual similarities in an unsupervised manner is a problem of paramount importance to Computer Vision. In this context, however, the recent breakthrough in deep learning could not yet unfold its full potential. With only a single positive sample, a great imbalance between one positive and many negatives, and unreliable relationships between most samples, training of Convolutional Neural networks is impaired. In this paper we use weak estimates of local similarities and propose a single optimization problem to extract batches of samples with mutually consistent relations. Conflicting relations are distributed over different batches and similar samples are grouped into compact groups. Learning visual similarities is then framed as a sequence of categorization tasks. The CNN then consolidates transitivity relations within and between groups and learns a single representation for all samples without the need for labels. The proposed unsupervised approach has shown competitive performance on detailed posture analysis and object classification.},
	doi = {https://doi.org/10.1016/j.patcog.2018.01.036},
	url = {https://authors.elsevier.com/a/1WXUt77nKSb25 },
	author = {Sanakoyeu, A. and Miguel Bautista and Bj{\"o}rn Ommer}
}
@mastersthesis {6252,
	title = {Dictionary Learning with Bayesian GANs for Few-Shot Classification},
	year = {2018},
	school = {Heidelberg University},
	author = {Weilbach, C}
}
@conference {6136,
	title = {Deep Semantic Feature Matching},
	booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2017},
	author = {Nikolai Ufer and Bj{\"o}rn Ommer}
}
@conference {6200,
	title = {Deep Unsupervised Similarity Learning using Partially Ordered Sets},
	booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
	year = {2017},
	author = {Miguel Bautista and Sanakoyeu, A. and Bj{\"o}rn Ommer}
}
@article {schilling2017,
	title = {On the design of a fractal calibration pattern for improved camera calibration},
	journal = {tm - Technisches Messen},
	volume = {84},
	year = {2017},
	pages = {440{\textendash}451},
	doi = {10.1515/teme-2017-0013},
	author = {Hendrik Schilling and Maximilian Diebold and Marcel Gutsche and Bernd J{\"a}hne}
}
@conference {Ramos2017,
	title = {Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling},
	booktitle = {IEEE Intelligent Vehicles Symposium, Proceedings},
	year = {2017},
	month = {dec},
	pages = {1025{\textendash}1032},
	abstract = {The detection of small road hazards, such as lost cargo, is a vital capability for self-driving cars. We tackle this challenging and rarely addressed problem with a vision system that leverages appearance, contextual as well as geometric cues. To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework. Here a variant of a fully convolutional network is proposed to predict a pixel-wise semantic labeling of (i) free-space, (ii) on-road unexpected obstacles, and (iii) background. The geometric cues are exploited using a state-of-The-Art detection approach that predicts obstacles from stereo input images via model-based statistical hypothesis tests. We present a principled Bayesian framework to fuse the semantic and stereo-based detection results. The mid-level Stixel representation is used to describe obstacles in a flexible, compact and robust manner. We evaluate our new obstacle detection system on the Lost and Found dataset, which includes very challenging scenes with obstacles of only 5 cm height. Overall, we report a major improvement over the state-of-The-Art, with a performance gain of 27.4\%. In particular, we achieve a detection rate of over 90\% for distances of up to 50 m. Our system operates at 22 Hz on our self-driving platform.},
	isbn = {9781509048045},
	doi = {10.1109/IVS.2017.7995849},
	url = {http://arxiv.org/abs/1612.06573},
	author = {Ramos, Sebastian and Gehrig, Stefan and Pinggera, Peter and Franke, Uwe and Carsten Rother}
}
@proceedings {6182,
	title = {Diverse M-best Solutions by Dynamic Programming},
	volume = {LNCS 10496},
	year = {2017},
	pages = {255-267},
	publisher = {Springer},
	doi = {10.1007/978-3-319-66709-6_21},
	author = {Haubold, C. and Uhlmann, V and Michael Unser and Fred A. Hamprecht}
}
@article {6209,
	title = {Diverse Shortest Paths for Bioimage Analysis},
	journal = {Bioinformatics},
	year = {2017},
	pages = {1-3},
	doi = {10.1093/bioinformatics/btx621},
	author = {Uhlmann, V and Haubold, C. and Fred A. Hamprecht and Michael Unser}
}
@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}
}
@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}
}
@mastersthesis {6144,
	title = {Deep Learning for Bioimage Analysis},
	year = {2016},
	school = {University of Heidelberg},
	author = {Schmidt, P}
}
@mastersthesis {6146,
	title = {Deep Learning for Diabetic Retinopathy Diagnostics},
	year = {2016},
	school = {University of Heidelberg},
	author = {Balles, L}
}
@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}
}
@inbook {6109,
	title = {Digital Connoisseur? How Computer Vision Supports Art History},
	booktitle = {Connoisseurship nel XXI secolo. Approcci, Limiti, Prospettive, A. Aggujaro \& S. Albl (ed.)},
	year = {2016},
	publisher = {Artemide},
	organization = {Artemide},
	address = {Rome},
	author = {Bell, P. and Bj{\"o}rn Ommer}
}
@conference {Zisler2016a,
	title = {Discrete Tomography by Continuous Multilabeling Subject to Projection Constraints},
	booktitle = {Proc. GCPR},
	year = {2016},
	author = {M. Zisler and Petra, S. and Schn{\"o}rr, Cl. and Christoph Schn{\"o}rr}
}
@conference {Astroem2016c,
	title = {Double-Opponent Vectorial Total Variation},
	booktitle = {Proc. ECCV},
	year = {2016},
	author = {Astr{\"o}m, F. and Schn{\"o}rr, C.}
}
@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 {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 {Zheng2014,
	title = {Dense semantic image segmentation with objects and attributes},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2014},
	pages = {3214{\textendash}3221},
	abstract = {The concepts of objects and attributes are both important for describing images precisely, since verbal descriptions often contain both adjectives and nouns (e.g. {\textquoteright}I see a shiny red chair{\textquoteright}). In this paper, we formulate the problem of joint visual attribute and object class image segmentation as a dense multi-labelling problem, where each pixel in an image can be associated with both an object-class and a set of visual attributes labels. In order to learn the label correlations, we adopt a boosting-based piecewise training approach with respect to the visual appearance and co-occurrence cues. We use a filtering-based mean-field approximation approach for efficient joint inference. Further, we develop a hierarchical model to incorporate region-level object and attribute information. Experiments on the aPASCAL, CORE and attribute augmented NYU indoor scenes datasets show that the proposed approach is able to achieve state-of-the-art results.},
	keywords = {Attributes, Image segmentation, Object recognition, Scene Understanding},
	isbn = {9781479951178},
	issn = {10636919},
	doi = {10.1109/CVPR.2014.411},
	url = {http://www.robots.ox.ac.uk/\~{}tvg/http://tu-dresden.de/inf/cvld},
	author = {Zheng, Shuai and Cheng, Ming Ming and Warrell, Jonathan and Sturgess, Paul and Vineet, Vibhav and Carsten Rother and Torr, Philip H.S.}
}
@conference {decker_14_detecting,
	title = {Detecting individual body parts improves mouse behavior classification},
	booktitle = {Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). Proceedings},
	year = {2014},
	note = {1},
	author = {Decker, C. and Fred A. Hamprecht}
}
@article {Eyjolfsdottir2014,
	title = {Detection of social actions in fruit flies},
	journal = {Lecture Notes in Computer Science},
	volume = {8690},
	number = {September 2014},
	year = {2014},
	pages = {772{\textendash}787},
	publisher = {Springer International Publishing},
	address = {Cham},
	abstract = {Spatio-temporal detection of actions and events in video is a challenging problem. Besides the difficulties related to recognition, a major challenge for detection in video is the size of the search space defined by spatio-temporal tubes formed by sequences of bounding boxes along the frames. Recently methods that generate unsupervised detection proposals have proven to be very effective for object detection in still images. These methods open the possibility to use strong but computationally expensive features since only a relatively small number of detection hypotheses need to be assessed. In this paper we make two contributions towards exploiting detection proposals for spatio-temporal detection problems. First, we extend a recent 2D object proposal method, to produce spatio-temporal proposals by a randomized supervoxel merging process. We introduce spatial, temporal, and spatio-temporal pairwise supervoxel features that are used to guide the merging process. Second, we propose a new efficient supervoxel method. We experimentally evaluate our detection proposals, in combination with our new supervoxel method as well as existing ones. This evaluation shows that our supervoxels lead to more accurate proposals when compared to using existing state-of-the-art supervoxel methods.},
	isbn = {978-3-319-10604-5},
	issn = {16113349},
	doi = {10.1007/978-3-319-10605-2},
	url = {http://link.springer.com/10.1007/978-3-319-10605-2 http://www.ncbi.nlm.nih.gov/pubmed/31629782},
	author = {Eyjolfsdottir, Eyrun and Branson, Steve and Burgos-Artizzu, Xavier P. and Hoopfer, Eric D. and Schor, Jonathan and Anderson, David J. and Perona, Pietro},
	editor = {Fleet, David and Pajdla, Tomas and Schiele, Bernt and Tuytelaars, Tinne}
}
@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 {wanner2013datasets,
	title = {Datasets and Benchmarks for Densely Sampled 4D Light Fields},
	booktitle = {Vision, Modeling \& Visualization},
	year = {2013},
	pages = {225--226},
	author = {Sven Wanner and Stephan Meister and Bastian Goldl{\"u}cke}
}
@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 {schaefer_13_depth,
	title = {Depth and Intensity Based Edge Detection in Time-of-Flight Images},
	booktitle = {3D Imaging, Modeling, Processing, Visualization and Transmission
(3DIMPVT), 2013 International Conference on},
	year = {2013},
	note = {1},
	pages = {111-118},
	publisher = {IEEE},
	organization = {IEEE},
	doi = {10.1109/3DV.2013.23},
	author = {Sch{\"a}fer, H. and Frank Lenzen and Christoph S. Garbe}
}
@conference {Schaefer2013depth,
	title = {Depth and Intensity Based Edge Detection in Time-of-Flight Images},
	booktitle = {3DV-Conference, 2013 International Conference on},
	year = {2013},
	pages = {111-118},
	doi = {10.1109/3DV.2013.23},
	author = {Sch{\"a}fer, Henrik and Frank Lenzen and Christoph S. Garbe}
}
@conference {Hornacek2013,
	title = {Depth super resolution by rigid body self-similarity in 3D},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2013},
	pages = {1123{\textendash}1130},
	abstract = {We tackle the problem of jointly increasing the spatial resolution and apparent measurement accuracy of an input low-resolution, noisy, and perhaps heavily quantized depth map. In stark contrast to earlier work, we make no use of ancillary data like a color image at the target resolution, multiple aligned depth maps, or a database of high-resolution depth exemplars. Instead, we proceed by identifying and merging patch correspondences within the input depth map itself, exploiting patch wise scene self-similarity across depth such as repetition of geometric primitives or object symmetry. While the notion of {\textquoteright}single-image{\textquoteright} super resolution has successfully been applied in the context of color and intensity images, we are to our knowledge the first to present a tailored analogue for depth images. Rather than reason in terms of patches of 2D pixels as others have before us, our key contribution is to proceed by reasoning in terms of patches of 3D points, with matched patch pairs related by a respective 6 DoF rigid body motion in 3D. In support of obtaining a dense correspondence field in reasonable time, we introduce a new 3D variant of Patch Match. A third contribution is a simple, yet effective patch up scaling and merging technique, which predicts sharp object boundaries at the target resolution. We show that our results are highly competitive with those of alternative techniques leveraging even a color image at the target resolution or a database of high-resolution depth exemplars. {\textcopyright} 2013 IEEE.},
	keywords = {dense matching, depth super resolution, optimization},
	issn = {10636919},
	doi = {10.1109/CVPR.2013.149},
	author = {Horn{\'a}{\v c}ek, Michael and Rhemann, Christoph and Gelautz, Margrit and Carsten Rother}
}
@conference {jaehne2013a,
	title = {Der Standard EMVA 1288 zur Charakterisierung von Kameras und Bildsensoren: von 2D- zu 3D-Kameras},
	booktitle = {Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Beitr{\"a}ge der Oldenburger 3D-Tage 2013},
	year = {2013},
	pages = {388--399},
	publisher = {Wichmann},
	organization = {Wichmann},
	url = {http://www.ub.uni-heidelberg.de/archiv/17699},
	author = {Bernd J{\"a}hne},
	editor = {Thomas Luhmann and M{\"u}ller, Christina}
}
@mastersthesis {bauer2013,
	title = {Development of an imaging polarimeter for water wave slope measurements},
	year = {2013},
	school = {Institut f{\"u}r Umwelphysik, Univerist{\"a}t Heidelberg, Germany},
	url = {http://www.ub.uni-heidelberg.de/archiv/15899},
	author = {Paulus Salomon Bauer}
}
@article {Lellmann2013,
	title = {Discrete and Continuous Models for Partitioning Problems},
	journal = {Int.~J.~Comp.~Visionz},
	volume = {104},
	number = {3},
	year = {2013},
	pages = {241-269},
	doi = {http://dx.doi.org/10.1007/s11263-013-0621-4},
	author = {Lellmann, J. and Lellmann, B. and Widmann, F. and Christoph Schn{\"o}rr}
}
@techreport {Schmidt,
	title = {Discriminative Non-blind Deblurring},
	year = {2013},
	abstract = {Non-blind deblurring is an integral component of blind approaches for removing image blur due to camera shake. Even though learning-based deblurring methods exist, they have been limited to the generative case and are compu-tationally expensive. To this date, manually-defined models are thus most widely used, though limiting the attained restoration quality. We address this gap by proposing a dis-criminative approach for non-blind deblurring. One key challenge is that the blur kernel in use at test time is not known in advance. To address this, we analyze existing approaches that use half-quadratic regularization. From this analysis, we derive a discriminative model cascade for image deblurring. Our cascade model consists of a Gaus-sian CRF at each stage, based on the recently introduced regression tree fields. We train our model by loss minimization and use synthetically generated blur kernels to generate training data. Our experiments show that the proposed approach is efficient and yields state-of-the-art restoration quality on images corrupted with synthetic and real blur.},
	author = {Schmidt, Uwe and Carsten Rother and Nowozin, Sebastian and Jancsary, Jeremy and Roth, Stefan}
}
@book {jaehne2012e,
	title = {Digitale Bildverarbeitung und Bildgewinnung},
	year = {2012},
	publisher = {Springer Vieweg},
	organization = {Springer Vieweg},
	isbn = {978-3-642-04951-4},
	doi = {10.1007/978-3-642-04952-1},
	url = {http://www.springer.com/engineering/signals/book/978-3-642-04951-4},
	author = {Bernd J{\"a}hne}
}
@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}
}
@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}
}
@conference {lenzen_11_denoising,
	title = {Denoising Time-Of-Flight Data with Adaptive Total Variation},
	booktitle = {Proceedings ISVC},
	year = {2011},
	note = {1},
	pages = {337-346},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-24028-7_31},
	author = {Frank Lenzen and Sch{\"a}fer, H. and Christoph S. Garbe}
}
@booklet {jaehne2011,
	title = {Der Ozean im Labor, Bildverarbeitung in den Umweltwissenschaften},
	year = {2011},
	url = {www.laborundmore.de/},
	author = {Bernd J{\"a}hne}
}
@booklet {jaehne2011c,
	title = {Der Standard 1288 der European Machine Vision Association (EMVA 1288): Was macht die Qualit{\"a}t aus?},
	year = {2011},
	url = {http://www.qe-online.de/home},
	author = {Bernd J{\"a}hne}
}
@booklet {jaehne2011b,
	title = {Der Standard EMVA 1288: Objektive Charakterisierung von Bildsensoren und digitalen Kameras},
	year = {2011},
	url = {www.elektroniknet.de},
	author = {Bernd J{\"a}hne}
}
@conference {schlecht:ICIP:2011,
	title = {Detecting Gestures in Medieval Images},
	booktitle = {Proceedings of the International Conference on Image Processing},
	year = {2011},
	pages = {1309--1312},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Schlecht, J. and Bernd Carque and Bj{\"o}rn Ommer}
}
@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}
}
@article {frank_09_denoising,
	title = {Denoising of Continuous-Wave Time-Of-Flight Depth Images Using Confidence Measures},
	journal = {Optical Engineering},
	volume = {48, 077003},
	year = {2009},
	note = {1},
	doi = {10.1117/1.3159869},
	author = {Mario Frank and Matthias Plaue and Fred A. Hamprecht}
}
@conference {becker_08_decomposition,
	title = {Decomposition of Quadratric Variational Problems},
	booktitle = {Pattern Recognition -- 30th DAGM Symposium},
	volume = {5096},
	year = {2008},
	note = {1},
	pages = {325--334},
	author = {Florian Becker and Christoph Schn{\"o}rr}
}
@conference {Becker-et-al-08a,
	title = {Decomposition of Quadratric Variational Problems},
	booktitle = {Pattern Recognition -- 30th DAGM Symposium},
	volume = {5096},
	year = {2008},
	pages = {325--334},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	author = {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}
}
@incollection {jaehne2007c,
	title = {Data acquisition by imaging detectors},
	year = {2007},
	pages = {1419--1436},
	publisher = {Springer},
	chapter = {24},
	abstract = {Imaging sensors convert radiative energy into an electrical signal and such sensors are available that cover the wide spectrum from gamma rays to the infrared. They accumulate an electrical signal during the exposure time and convert all the signals of an array of detectors into a time-serial analog or digital data stream. The dominate and most successful devices to perform this task are charge coupled devices (CCD). However directly addressable imaging sensors on the basis of CMOS fabrication technology are becoming more and more promising because the image acquisition, digitalization and preprocessing can be integrated on a single chip; hence yielding very fast frame rates. This chapter provides a comprehensive survey of the available imaging sensors, details the parameters that control their performance and gives practical tips to select the best camera for different imaging tasks.},
	doi = {10.1007/978-3-540-30299-5_24},
	author = {Bernd J{\"a}hne},
	editor = {C. Tropea and J. Foss and A. Yarin}
}
@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}
}
@article {Yuan-et-al-jmiv-06,
	title = {Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation},
	journal = {J.~Math.~Imag.~Vision},
	volume = {28},
	year = {2007},
	pages = {67-80},
	author = {Yuan, J. and Christoph Schn{\"o}rr and M{\'e}min, E.}
}
@conference {Sellen2007,
	title = {Do life-logging technologies support memory for the past? An experimental study using sensecam},
	booktitle = {Conference on Human Factors in Computing Systems - Proceedings},
	year = {2007},
	pages = {81{\textendash}90},
	abstract = {We report on the results of a study using SenseCam, a "life- logging" technology in the form of a wearable camera, which aims to capture data about everyday life in order to support people{\textquoteright}s memory for past, personal events. We find evidence that SenseCam images do facilitate people{\textquoteright}s ability to connect to their past, but that images do this in different ways. We make a distinction between "remembering" the past, and "knowing" about it, and provide evidence that SenseCam images work differently over time in these capacities. We also compare the efficacy of user-captured images with automatically captured images and discuss the implications of these findings and others for how we conceive of and make claims about life-logging technologies. {\textcopyright} Copyright 2007 ACM.},
	keywords = {Capture, Episodic or autobiographical memory, Images, Life-logging, Personal digital archives, SenseCam},
	isbn = {1595935932},
	doi = {10.1145/1240624.1240636},
	author = {Sellen, Abigail and Fogg, Andrew and Aitken, Mike and Hodges, Steve and Carsten Rother and Wood, Ken}
}
@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 {hader_06_data,
	title = {Data Mining auf multidimensionalen und komplexen Daten in der industriellen Bildverarbeitung},
	year = {2006},
	publisher = {University of Heidelberg},
	author = {S. Hader}
}
@article {menze_06_detection,
	title = {Detection of ancient settlement mounds - Archaeological survey based on the SRTM terrain model},
	journal = {Photgrammetric Engineering \& Remote Sensing},
	volume = {3},
	year = {2006},
	note = {1},
	pages = {321-327},
	author = {Bjoern H. Menze and Ur, J. A. and Sherratt, A. G.}
}
@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}
}
@mastersthesis {lerch_06_discontinuity,
	title = {Discontinuity Preserving Filtering of Spectral Images},
	year = {2006},
	school = {University of Heidelberg},
	author = {Lerch, K.}
}
@conference {StaRuhChr06,
	title = {A Distributed Parameter Approach to Dynamic Image Motion},
	booktitle = {ECCV 2006, International Workshop on The Representation and Use of Prior Knowledge in Vision},
	year = {2006},
	publisher = {LNCS, Springer},
	organization = {LNCS, Springer},
	author = {Stahl, A. and Paul Ruhnau and Christoph Schn{\"o}rr}
}
@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.}
}
@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}
}
@booklet {goerlitz_05_detektion,
	title = {Detektion von Partikeln in Intensit{\"a}tsbildern mit Hilfe eines morphologischen Skalenraumes},
	year = {2005},
	publisher = {Robert-Bosch GmbH, University of Heidelberg},
	author = {G{\"o}rlitz, L. and Fred A. Hamprecht and Staudacher, M.}
}
@mastersthesis {schwarz2005,
	title = {Development of a depth resolving boundary layer visualiziation f{\"u}r gas exchange at free water surfaces},
	year = {2005},
	school = {University of Heidelberg},
	abstract = {In this thesis, a measurement setup is introduced which makes it possible to directly measure two-dimensional, vertical concentration of gases in the water sided boundary layer using Laser-Induced Fluorescence (LIF). While it is impossible to gain knowledge of the physical processes involved in gas exchange using measurements of transfer rates and mass balances, the introduced method makes it possible to directly visualize the physical processes of matter transport in the boundary layer. The measurement method is based on two basic principles: First a fluorescence indicator is used, whose fluorescence intensity is proportional to the local pH-value, thus allowing a spatial resolved measurement of the concentrations of dissolved alkaline or acidic gases can directly be visualized. Second, to create a depth resolution, a second, absorbing dye is added, whose absoprtion maximum lies inside the fluorescence spectrum, so that spectra from different depths show changes in their spectral shape due to the different light path lengths through the absorber. Thus the measured spectrum is the superposition of all depth spectra, which provide the basis of a linear inverse problem. Models for the reconstruction of the depth information will be introduced in the course of this thesis, and the solvability will be analyzed. As the stability of the solution of the inverse problem is almost exclusively determined by the invertibility of the basis function matrix, a confocal microscope was constructed, which allowed the direct measurement of depth spectra. Through this it was made possible to numerically analyze and evaluate the conditioning of the matrix invertibility.},
	author = {Schwarz, T. S.}
}
@book {jaehne2005e,
	title = {Digital Image Processing},
	year = {2005},
	publisher = {Springer},
	organization = {Springer},
	edition = {6},
	doi = {10.1007/3-540-27563-0},
	author = {Bernd J{\"a}hne}
}
@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}
}
@book {jaehne2005b,
	title = {Digitale Bildverarbeitung},
	year = {2005},
	publisher = {Springer},
	organization = {Springer},
	edition = {6},
	doi = {10.1007/b138991},
	author = {Bernd J{\"a}hne}
}
@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}
}
@conference {Yuan-et-al-05a,
	title = {Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation},
	booktitle = {Scale-Space 2005},
	series = {lncs},
	volume = {3459},
	year = {2005},
	pages = {267{\textendash}278},
	publisher = {Springer},
	organization = {Springer},
	author = {Yuan, J. and Ruhnau, P. and M{\'e}min, E. and Schn{\"o}rr, C.}
}
@article {Schuele-et-al-04a,
	title = {Discrete Tomography By Convex-Concave Regularization and D.C. Programming},
	journal = {Discr. Appl. Math.},
	volume = {151},
	year = {2005},
	month = {Oct},
	pages = {229-243},
	author = {Sch{\"u}le, T. and Schn{\"o}rr, C. and Weber, S. and Hornegger, J.}
}
@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.}
}
@phdthesis {hilsenstein2004,
	title = {Design and Implementation of a Passive Stereo-Infrared Imaging System for the Surface Reconstruction of Water Waves},
	year = {2004},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {To quantify air-sea exchange processes, an understanding of how they are influenced by water waves is necessary. This work presents a passive, infrared stereo-imaging system for the reconstruction of a wavy water surface. The system does not require a sub-merged light source, so it is suitable for field operation. The structure of the thesis is as follows. Previous work on water wave imaging is reviewed and the major problems with stereo-based reconstruction of water surfaces are identified: transparency, lack of texture and specular reflections. It is shown that many of these problems can be avoided by imaging at infrared wavelengths. Following a review of infrared radiometry, the key ingredients of surface reconstruction using the stereo principle are explained, including camera calibration, epipolar geometry and disparity estimation. A description of the stereo infrared camera system used in this work is given. An experimental validation of the system was performed at the Heidelberg wind-wave channel. Several stereo infrared image sequences of water waves recorded at this facility are used to demonstrate that a dense surface reconstruction of water waves is possible using this system. The accuracy of the reconstruction is experimentally assessed using a flat water surface as a reference plane.},
	url = {http://www.ub.uni-heidelberg.de/archiv/4601},
	author = {Volker Hilsenstein}
}
@phdthesis {senet2004,
	title = {Dynamics of dispersive boundaries: the determination of spatial hydrographic parameter maps from optical sea surface image sequences},
	year = {2004},
	publisher = {Universit{\"a}t Hamburg, FB Informatik},
	author = {Senet, Christian M.}
}
@techreport {Schuele-et-al-03,
	title = {Discrete Tomography By Convex-Concave Regularization and D.C. Programming},
	number = {15},
	year = {2003},
	note = {\em Discr. Appl. Math.\/, accepted for publication},
	month = {December},
	institution = {Dept. Math. and Comp. Science},
	type = {Comp. Science Series, Technical Report},
	address = {University of Mannheim, Germany},
	author = {Sch{\"u}le, T. and Schn{\"o}rr, C. and Weber, S. and Hornegger, J.}
}
@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.}
}
@conference {spies2002b,
	title = {Dense parameter fields from total least squares},
	booktitle = {Proceedings of the 24th DAGM Symposium on Pattern Recognition},
	volume = {LNCS 2449},
	year = {2002},
	pages = {379--386},
	publisher = {Springer},
	organization = {Springer},
	abstract = {A method for the interpolation of parameter fields estimated by total least squares is presented. This is applied to the study of dynamic processes where the motion and further values such as divergence or brightness changes are parameterised in a partial differential equation. For the regularisation we introduce a constraint that restricts the solution only in the subspace determined by the total least squares procedure. The performance is illustrated on both synthetic and real test data.},
	doi = {10.1007/3-540-45783-6_46},
	author = {Hagen Spies and Christoph S. Garbe},
	editor = {Luc Van Gool}
}
@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.}
}
@article {Cremers-et-al-02b,
	title = {Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford{\textendash}Shah functional},
	journal = {Int. J. Computer Vision},
	volume = {50},
	number = {3},
	year = {2002},
	pages = {295{\textendash}313},
	author = {Daniel Cremers and Tischh{\"a}user, Florian and Weickert, Joachim and Christoph Schn{\"o}rr}
}
@book {jaehne2002,
	title = {Digital Image Processing},
	year = {2002},
	publisher = {Springer},
	organization = {Springer},
	edition = {5th},
	doi = {10.1007/978-3-662-04781-1},
	url = {http://d-nb.info/963597833},
	author = {Bernd J{\"a}hne}
}
@book {jaehne2002a,
	title = {Digitale Bildverarbeitung},
	year = {2002},
	publisher = {Springer Verlag},
	organization = {Springer Verlag},
	edition = {F{\"u}nfte},
	doi = {10.1007/978-3-662-06731-4},
	url = {http://d-nb.info/963601830},
	author = {Bernd J{\"a}hne}
}
@conference {Cremers-et-al-01a,
	title = {Diffusion{\textendash}Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework},
	booktitle = {IEEE First Workshop on Variational and Level Set Methods in Computer Vision},
	year = {2001},
	pages = {237{\textendash}244},
	publisher = {IEEE Comp. Soc.},
	organization = {IEEE Comp. Soc.},
	address = {Vancouver, Canada},
	author = {Daniel Cremers and Christoph Schn{\"o}rr and Weickert, Joachim}
}
@conference {spies2000b,
	title = {Dense range flow from depth and intensity data},
	booktitle = {ICPR},
	year = {2000},
	pages = {131--134},
	doi = {10.1109/ICPR.2000.905290},
	author = {Hagen Spies and Bernd J{\"a}hne and John L. Barron}
}
@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}
}
@conference {Cremers-et-al-00a,
	title = {Diffusion Snakes Using Statistical Shape Knowledge},
	booktitle = {Proc. Algebraic Frames for the Perception-Action Cycle},
	series = {lncs},
	volume = {1888},
	year = {2000},
	month = {Sept. 10-11},
	pages = {164{\textendash}174},
	publisher = {Springer},
	organization = {Springer},
	address = {Kiel},
	author = {Daniel Cremers and Christoph Schn{\"o}rr and Weickert, Joachim and Schellewald, Christian},
	editor = {Sommer, G. and Zeevi, Y.}
}
@incollection {geissler1999a,
	title = {Depth-from-focus for the measurement of size distributions of small particles},
	volume = {3: Systems and Applications},
	year = {1999},
	pages = {623-646},
	publisher = {Academic Press},
	chapter = {29},
	author = {Peter Gei{\ss}ler and Thomas Scholz and Bernd J{\"a}hne and Horst Hau{\ss}ecker and Peter Gei{\ss}ler}
}
@phdthesis {schmundt1999,
	title = {Development of an Optical Flow Based System for the Precise Measurement of Plant Growth},
	year = {1999},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Dominik Schmund}
}
@conference {spies1999a,
	title = {Differential range flow estimation},
	booktitle = {Proceedings of the 21th DAGM Symposium on Pattern Recognition},
	year = {1999},
	note = {DAGM award},
	pages = {309--316},
	doi = {10.1007/978-3-642-60243-6_36},
	author = {Hagen Spies and Horst Hau{\ss}ecker and Bernd J{\"a}hne and John L. Barron}
}
@mastersthesis {rennekamp1998,
	title = {Datenbank gest{\"u}tzte Verwaltung kalibrierter Bildsequenzen zur Qualit{\"a}tsbewertung von Algorithmen},
	year = {1998},
	school = {Fakult{\"a}t f{\"u}r Physik und Astronomie Universit{\"a}t Heidelberg},
	author = {Rennekamp, Frank}
}
@phdthesis {geissler1998,
	title = {Depth-from-Focus zur Messung der Gr{\"o}{\ss}enverteilung durch Wellenbrechen erzeugter Blasenpopulationen},
	year = {1998},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Peter Gei{\ss}ler}
}
@article {hamprecht_98_development,
	title = {Development and assessment of new exchange-correlation functionals},
	journal = {Journal of Chemical Physics},
	volume = {109},
	year = {1998},
	pages = {6264-6271},
	doi = {10.1063/1.477267},
	author = {Fred A. Hamprecht and Cohen, A. J. and Tozer, D. J. and Handy, N. C.}
}
@conference {Wiehler-et-al-98,
	title = {Dynamic Circular Cellular Networks for Adaptive Smoothing of Multi{\textendash}Dimensional Signals},
	booktitle = {Proc. 5th IEEE Int. Workshop on Cellular Neural Networks and their Applications},
	year = {1998},
	month = {April, 14{\textendash}17},
	address = {London},
	author = {Wiehler, K. and Grigat, R.{\textendash}R. and Heers, J. and Schn{\"o}rr, C. and Stiehl, H.S.}
}
@conference {Fornland-Schnoerr-97,
	title = {Determining the Dominant Plane from Uncalibrated Stereo Vision by a Robust and Convergent Iterative Approach without Correspondence},
	booktitle = {Proc. Int. Conf. Comp. Vision and Patt. Recog. (CVPR{\textquoteright}97)},
	year = {1997},
	month = {June 17-19},
	address = {San Juan, Puerto Rico},
	author = {Fornland, P{\"a}r and Christoph Schn{\"o}rr}
}
@book {jaehne1997h,
	title = {Digital Image Processing - Concepts, Algorithms, and Scientific Applications},
	year = {1997},
	note = {Includes CD-ROM},
	publisher = {Springer},
	organization = {Springer},
	edition = {4},
	doi = {10.1007/978-3-662-03477-4},
	url = {http://d-nb.info/950918016},
	author = {Bernd J{\"a}hne}
}
@book {jaehne1997g,
	title = {Digitale Bildverarbeitung},
	year = {1997},
	note = {Mit CD-ROM},
	publisher = {Springer},
	organization = {Springer},
	edition = {4},
	doi = {10.1007/978-3-662-06733-8},
	url = {http://d-nb.info/949866776},
	author = {Bernd J{\"a}hne}
}
@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.}
}
@mastersthesis {scharr1996,
	title = {Digitale Bildverarbeitung und Papier Texturanalyse mittels Pyramiden und Grauwertstatistiken am Beispiel der Papierformation},
	year = {1996},
	school = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	abstract = {Bei der Beurteilung der Qualit{\"a}t von Papier ist die G{\"u}te der Formation ein wichtiges Kriterium. Definitionsgem{\"a}{\ss}, nach ISO-Norm 4046-1978, versteht man unter Formation die Art und Weise, wie Fasern verteilt, angeordnet und vermischt sind, um ein Papier zu bilden [Praast und G{\"o}ttsching, 1991]. F{\"u}r Papiermacher und -verarbeiter ist die Formation die Verteilung von hellen und dunklen Flecken, Wolken bzw. Flocken die man sieht, wenn man ein unbedrucktes Blatt Papier gegen das Licht h{\"a}lt (siehe auch [Bolam, 1961]). Die beiden ausschlaggebenden Merkmale sind die mittlere Flockengr{\"o}{\ss}e und ihr Kontrast gegen den Hintergrund. Visuell ist die G{\"u}te der Massenverteilung auch f{\"u}r den Laien z.B. mittels paarweisem Vergleich (PV) der Proben einfach und zuverl{\"a}ssig durchzuf{\"u}hren (siehe [Hradezky, 1977]). Bisher k{\"o}nnen industrielle Formationsme{\ss}ger{\"a}te den Kontrast sehr gut, aber die Flockengr{\"o}{\ss}e nur relativ unzuverl{\"a}ssig bestimmen. Zudem sind die zuverl{\"a}ssigsten Methoden sehr rechenintensiv.},
	author = {Hanno Scharr}
}
@conference {vliet1995,
	title = {Delft Hydraulics Large Wind-Wave Flume},
	booktitle = {Air-Water Gas Transfer---Selected Papers from the Third International Symposium of Air--Water Gas Transfer in Heidelberg},
	year = {1995},
	pages = {499--502},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10402},
	author = {van Vliet, P. and Frank Hering and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {geissler1995b,
	title = {Depth-from-Focus Verfahren zur absoluten Gr{\"o}{\ss}en- und Konzentrationsbestimmung kleiner Teilchen},
	booktitle = {Bildverarbeitung{\textquoteright}95 - Forschen, Entwickeln, Anwenden},
	year = {1995},
	pages = {365--380},
	publisher = {Technische Akademie Esslingen},
	organization = {Technische Akademie Esslingen},
	doi = {10.5281/zenodo.14710},
	author = {Peter Gei{\ss}ler and Thomas Scholz and Bernd J{\"a}hne and Schmidt, C. and H. Suhr and G. Wehnert},
	editor = {Ahlers, R. -J.}
}
@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}
}
@book {jaehne1995d,
	title = {Digital Image Processing --- Concepts, Algorithms, and Scientific Applications},
	year = {1995},
	publisher = {Springer},
	organization = {Springer},
	edition = {3},
	doi = {10.1007/978-3-662-03174-2},
	url = {http://d-nb.info/945151500},
	author = {Bernd J{\"a}hne}
}
@phdthesis {netsch1995,
	title = {Dreidimensionale Particle Tracking Velocimetry},
	year = {1995},
	publisher = {IWR, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://d-nb.info/945379838},
	author = {T. Netzsch}
}
@conference {muensterer1995,
	title = {Dual-tracer measurements of concentration profiles in the aqueous mass boundary layer},
	booktitle = {Air-water Gas Transfer, Selected Papers from the Third International Symposium on Air--Water Gas Transfer},
	year = {1995},
	pages = {637--648},
	publisher = {AEON},
	organization = {AEON},
	doi = {10.5281/zenodo.10406},
	author = {Thomas M{\"u}nsterer and Hans J{\"u}rgen Mayer and Bernd J{\"a}hne and Bernd J{\"a}hne},
	editor = {E. C. Monahan}
}
@conference {jaehne1994c,
	title = {Depth from focus with one image},
	booktitle = {Proc. Conference on Computer Vision and Pattern Recognition (CVPR {\textquoteright}94), Seattle, 20.-23. June 1994},
	year = {1994},
	pages = {713--717},
	doi = {10.1109/CVPR.1994.323885},
	author = {Bernd J{\"a}hne and Peter Gei{\ss}ler}
}
@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 {geissler1993,
	title = {Depth-from-focus Bildanalyseverfahren zur Messung der Konzentration und Gr{\"o}{\ss}e von Blasen und Mikroorganismen},
	year = {1993},
	school = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	author = {Peter Gei{\ss}ler}
}
@conference {geissler1993a,
	title = {Depth-from-focus zur Bestimmung der Konzentration und Gr{\"o}{\ss}e von Gasblasen},
	booktitle = {Proc. 15. DAGM-Symposium Mustererkennung},
	year = {1993},
	note = {DAGM award},
	pages = {560--567},
	publisher = {Springer},
	organization = {Springer},
	author = {Peter Gei{\ss}ler and Bernd J{\"a}hne and S. J. P{\"o}ppl},
	editor = {H. Handels}
}
@book {jaehne1993h,
	title = {Digital Image Processing --- Concepts, Algorithms, and Scientific Applications},
	year = {1993},
	publisher = {Springer},
	organization = {Springer},
	edition = {2},
	doi = {10.1007/978-3-662-21817-4},
	url = {http://d-nb.info/931193567},
	author = {Bernd J{\"a}hne}
}
@book {jaehne1993g,
	title = {Digitale Bildverarbeitung},
	year = {1993},
	publisher = {Springer},
	organization = {Springer},
	edition = {3},
	doi = {10.1007/978-3-662-22662-9},
	url = {http://d-nb.info/931125731},
	author = {Bernd J{\"a}hne}
}
@conference {netzsch1992,
	title = {Dreidimensionale Messung turbulenter Str{\"o}mung mit Bildverarbeitung},
	booktitle = {Proc. 14. DAGM-Symposium zur Mustererkennung},
	year = {1992},
	pages = {150--153},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-642-77785-1_18},
	author = {T. Netzsch and Bernd J{\"a}hne},
	editor = {Fuchs, S. and Hoffmann, R.}
}
@article {Schnoerr-ijcv-91,
	title = {Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class},
	journal = {ijcv},
	volume = {6},
	number = {1},
	year = {1991},
	pages = {25{\textendash}38},
	author = {Schn{\"o}rr, C.}
}
@book {jaehne1991c,
	title = {Digital Image Processing - Concepts, Algorithms, and Scientific Applications},
	year = {1991},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-662-11565-7},
	url = {http://d-nb.info/911090657},
	author = {Bernd J{\"a}hne}
}
@book {jaehne1991b,
	title = {Digitale Bildverarbeitung},
	year = {1991},
	publisher = {Springer},
	organization = {Springer},
	edition = {2},
	doi = {10.1007/978-3-662-06734-5},
	url = {http://d-nb.info/910564256},
	author = {Bernd J{\"a}hne}
}
@conference {halsema1991,
	title = {Dual-polarized scatterometer measurements of wind and mechanically generated waves in a very large wind/wave flume, the VIERS-1 project},
	booktitle = {Proc.\ 5th Int. Coll. Physical Measurements and Signatures in Remote Sensing, Courchevel, France},
	volume = {ESA SP-319},
	year = {1991},
	pages = {247--252},
	author = {van Halsema, D. and C. J. Calkoen and W. A. Oost and P. Snoeij and J. Vogelzang and Bernd J{\"a}hne}
}
@conference {snoeij1990a,
	title = {Dual-polarized scatterometer measurements of generated Wind and Gravity Wave in a Very Large Wind/Wave Tank},
	booktitle = {Proc. IGARSS{\textquoteright}90},
	year = {1990},
	pages = {2157--2160},
	doi = {10.1109/IGARSS.1990.688958},
	author = {P. Snoeij and C. J. Calkoen and W. A. Oost and van Halsema, D. and J. Vogelzang and Bernd J{\"a}hne}
}
@book {jaehne1989b,
	title = {Digitale Bildverarbeitung},
	year = {1989},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-662-06734-5},
	url = {http://d-nb.info/890489467},
	author = {Bernd J{\"a}hne}
}
@phdthesis {libner1987,
	title = {Die Konstantflu{\ss}methode: Ein neuartiges, schnelles und lokales Me{\ss}verfahren zur Untersuchung von Austauschvorg{\"a}ngen an der Luft-Wasser Phasengrenze},
	year = {1987},
	publisher = {Institut f{\"u}r Umweltphysik, Fakult{\"a}t f{\"u}r Physik und Astronomie, Univ.\ Heidelberg},
	url = {http://d-nb.info/881465941},
	author = {P. Libner}
}
