@proceedings {7079,
	title = {Assignment Flows and Nonlocal PDEs on Graphs},
	year = {2021},
	author = {Gonzalez-Alvarado, D and Zeilmann, A and Schn{\"o}rr, C}
}
@article {Zern:2020aa,
	title = {Assignment Flows for Data Labeling on Graphs: Convergence and Stability},
	journal = {preprint: arXiv},
	year = {2020},
	url = {https://arxiv.org/abs/2002.11571},
	author = {Zern, A. and Zeilmann, A. and Schn{\"o}rr, C.}
}
@article {Zeilmann:2020aa,
	title = {Geometric Numerical Integration of the Assignment Flow},
	journal = {Inverse Problems},
	volume = {36},
	number = {3},
	year = {2020},
	pages = {034004 (33pp)},
	author = {Zeilmann, A. and Savarino, F. and Petra, S. and Schn{\"o}rr, C.}
}
@article {Zern:2020ab,
	title = {Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment},
	journal = {Journal of Mathematical Imaging and Vision},
	year = {2020},
	url = {https://doi.org/10.1007/s10851-019-00935-7},
	author = {Zern, A. and M. Zisler and Petra, S. and Schn{\"o}rr, C.}
}
@proceedings {6302,
	title = {End-to-End Learned Random Walker for Seeded Image Segmentation},
	year = {2019},
	pages = {12559-12568},
	doi = { 10.1109/CVPR.2019.01284},
	author = {Cerrone, L and Zeilmann, A and Fred A. Hamprecht}
}
@article {Zeilmann:2019aa,
	title = {Geometric Numerical Integration of the Assignment Flow},
	journal = {Inverse Problems},
	year = {2019},
	url = {https://doi.org/10.1088/1361-6420/ab2772},
	author = {Zeilmann, A. and Savarino, F. and Petra, S. and Schn{\"o}rr, C.}
}
@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}
}
@article {Zisler:2019ab,
	title = {Self-Assignment Flows for Unsupervised Data Labeling on Graphs},
	journal = {preprint: arXiv},
	year = {2019},
	url = {https://arxiv.org/abs/1911.03472},
	author = {M. Zisler and Zern, A. and Petra, S. and Schn{\"o}rr, C.}
}
@article {Zern:2019aa,
	title = {Unsupervised Assignment Flow: Label Learning on Feature Manifolds by Spatially Regularized Geometric Assignment},
	journal = {preprint: arXiv},
	year = {2019},
	url = {https://arxiv.org/abs/1904.10863},
	author = {Zern, A. and M. Zisler and Petra, S. and Schn{\"o}rr, C.}
}
@conference {Zisler:2019aa,
	title = {Unsupervised Labeling by Geometric and Spatially Regularized Self-Assignment},
	booktitle = {Proc. SSVM},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {M. Zisler and Zern, A. and Petra, S. and Schn{\"o}rr, C.}
}
@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}
}
@conference {Zern:2018aa,
	title = {Geometric Image Labeling with Global Convex Labeling Constraints},
	booktitle = {EMMCVPR},
	series = {LNCS},
	volume = {10746},
	year = {2018},
	pages = {533{\textendash}547},
	author = {Zern, A. and Rohr, K. and Schn{\"o}rr, C.}
}
@article {Zeilmann:2018aa,
	title = {Geometric Numerical Integration of the Assignment Flow},
	journal = {preprint: arXiv},
	year = {2018},
	url = {https://arxiv.org/abs/1810.06970},
	author = {Zeilmann, A. and Savarino, F. and Petra, S. and Schn{\"o}rr, C.}
}
@conference {Zern:2018ac,
	title = {Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment},
	booktitle = {GCPR},
	year = {2018},
	author = {Zern, A. and M. Zisler and Astr{\"o}m, F. and Petra, S. and Schn{\"o}rr, C.}
}
@proceedings {6311,
	title = {Unsupervised Label Learning on Manifolds by Spatially Regularized Geometric Assignment},
	year = {2018},
	pages = {698-713},
	doi = {10.1007/978-3-030-12939-2_48},
	author = {Zern, A and M. Zisler and Freddie Astr{\"o}m and Petra, S and Schn{\"o}rr, C}
}
@conference {Zern2017,
	title = {Geometric Image Labeling with Global Convex Labeling Constraints},
	booktitle = {Proc. EMMCVPR},
	year = {2017},
	note = {to appear},
	author = {Zern, A. and Rohr, K. and Schn{\"o}rr, C.}
}
@conference {Zisler2017a,
	title = {Gradient Flows on a Riemannian Submanifold for Discrete Tomography},
	booktitle = {Proc. GCPR},
	year = {2017},
	author = {M. Zisler and Savarino, F. and Petra, S. and Schn{\"o}rr, C.}
}
@conference {Zisler2017,
	title = {Image Reconstruction by Multilabel Propagation},
	booktitle = {Proc. SSVM},
	series = {LNCS},
	volume = {10302},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	author = {M. Zisler and Astr{\"o}m, F. and Petra, S. and Schn{\"o}rr, C.}
}
@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}
}
@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.}
}
@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}
}
@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 {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 {funke_15_learning,
	title = {Learning to Segment: Training Hierarchical Segmentation under a Topological Loss},
	booktitle = {MICCAI. Proceedings, Part III},
	volume = {9351},
	year = {2015},
	pages = {268-275},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-24574-4},
	author = {Funke, J. and Fred A. Hamprecht and Zhang, C.},
	editor = {Frangi, A. et al.}
}
@conference {Zheng2015,
	title = {Object proposals estimation in depth image using compact 3D shape manifolds},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9358},
	year = {2015},
	pages = {196{\textendash}208},
	abstract = {Man-made objects, such as chairs, often have very large shape variations, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any collection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based filtering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previous state-of-the-art approaches, on four different shape classes, and show a clear improvement.},
	isbn = {9783319249469},
	issn = {16113349},
	doi = {10.1007/978-3-319-24947-6_16},
	author = {Zheng, Shuai and Prisacariu, Victor Adrian and Averkiou, Melinos and Cheng, Ming Ming and Mitra, Niloy J and Shotton, Jamie and Torr, Philip H.S. and Carsten Rother}
}
@article {Mitra2015,
	title = {Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut},
	volume = {34},
	number = {7},
	year = {2015},
	abstract = {Figure-ground segmentation from bounding box input, provided either automatically or manually, has been ex-tremely popular in the last decade and influenced various applications. A lot of research has focused on high-quality segmentation, using complex formulations which often lead to slow techniques, and often hamper practi-cal usage. In this paper we demonstrate a very fast segmentation technique which still achieves very high quality results. We propose to replace the time consuming iterative refinement of global colour models in traditional GrabCut formulation by a densely connected CRF. To motivate this decision, we show that a dense CRF implicitly models unnormalized global colour models for foreground and background. Such relationship provides insightful analysis to bridge between dense CRF and GrabCut functional. We extensively evaluate our algorithm using two famous benchmarks. Our experimental results demonstrated that the proposed algorithm achieves an order of magnitude (10{\texttimes}) speed-up with respect to the closest competitor, and at the same time achieves a considerably higher accuracy.},
	keywords = {I46 [IMAGE PROCESSING AND COMPUTER VISION], partitioning, Segmentation{\textemdash}Region growing},
	url = {http://mftp.mmcheng.net/Papers/DenseCut.pdf},
	author = {Mitra, N J and Stam, J and Xu, K and Cheng, Ming-Ming and Prisacariu, Victor Adrian and Zheng, Shuai and Torr, Philip H S and Carsten Rother}
}
@conference {Mund2015,
	title = {Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons},
	booktitle = {Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems},
	year = {2015},
	pages = {85{\textendash}94},
	abstract = {Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.},
	keywords = {Airport surveillance, apron control, Apron management service, FOD, Foreign object debris, laser scanning, LiDAR, Object detection, Point cloud},
	isbn = {9781450335621},
	doi = {10.1145/2899361.2899370},
	author = {Mund, Johannes and Zouhar, Alexander and Meyer, Lothar and Fricke, Hartmut and Carsten Rother}
}
@conference {Mund2015a,
	title = {Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons},
	booktitle = {Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems},
	year = {2015},
	pages = {85{\textendash}94},
	abstract = {Both the current system of airport ground control and the continuous implementation efforts of A-SMGCS and Remote Tower concepts require complete and independent surveillance coverage in real-time. We believe that 3D point clouds generated by an actively scanning LiDAR system available at TU Dresden may satisfy these high standards. Nonetheless, the utilization of LiDAR sensing for airport ground surveillance purposes is extremely challenging due to the unique requirement profile in this domain. This is also the reason why existing solutions in other domains such as autonomous driving and robotics are not directly applicable for airport ground surveillance. In a first step, we developed point cloud object detection and segmentation techniques to present that new data comprehensively to the airport apron controller. In this paper, we focused on the timely detection of dislocated objects (foreign object debris, forgotten equipment etc.) as a serious cause to hazardous situations on airport movement areas. The results are promising for various reference targets. However, the detection of very small objects (e.g. socket wrench) requires more elaborate algorithms to take full advantage of the current LiDAR technology. In the future we will assess the strength of LiDAR-based surveillance in terms of the number of hazardous situations that could be avoided or safely managed by the apron controller.},
	keywords = {Airport surveillance, apron control, Apron management service, FOD, Foreign object debris, laser scanning, LiDAR, Object detection, Point cloud},
	isbn = {9781450335621},
	doi = {10.1145/2899361.2899370},
	author = {Mund, Johannes and Zouhar, Alexander and Meyer, Lothar and Fricke, Hartmut and Carsten Rother}
}
@conference {Zouhar2015,
	title = {Semantic 3-D labeling of ear implants using a global parametric transition prior},
	booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {9350},
	year = {2015},
	pages = {177{\textendash}184},
	abstract = {In this work we consider the problem of sematic part-labeling of 3-D meshesof ear implants. This is a challenging problem and automatic solutions are of high practical relevance, since they help to automate the design of hearing aids. The contribution of this work is a new framework which outperforms existing approaches for this task. To achieve the boost in performance we introduce the new concept of a global parametric transition prior. To our knowledge, this is the first time that such a generic prior is used for 3-D mesh processing, and it may be found useful for a large class of 3-D meshes. To foster more research on the important topic of ear implant labeling, we collected a large data set of 3-D meshes, with associated ground truth labels, which we will make publicly available.},
	isbn = {9783319245706},
	issn = {16113349},
	doi = {10.1007/978-3-319-24571-3_22},
	author = {Zouhar, Alexander and Carsten Rother and Fuchs, Siegfried}
}
@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}
}
@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}
}
@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}
}
@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 {zhang_14_cell,
	title = {Cell detection and segmentation using correlation clustering},
	booktitle = {MICCAI. Proceedings},
	number = {8673},
	year = {2014},
	note = {1},
	pages = {9-16},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-10404-1_2},
	author = {Zhang, C. and Julian Yarkony 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 {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 {yarkony_14_hierarchical,
	title = {Hierarchical Planar Correlation Clustering for Cell Segmentation},
	booktitle = {EMMCVPR. Proceedings},
	volume = {8932},
	year = {2014},
	note = {1},
	pages = {492-504},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-14612-6_36},
	author = {Julian Yarkony and Zhang, C. and Fowlkes, C. C.}
}
@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 {He2014,
	title = {Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows},
	journal = {Life.Kunzhou.Net},
	volume = {1},
	number = {212},
	year = {2014},
	pages = {1{\textendash}8},
	abstract = {The control polygon of a Bezier curve is well-defined and meaningful{\textendash}-there is a sequence of weights under which the limiting position of the curve is the control polygon. For a Bezier surface patch, there are many possible polyhedral control structures, and none are canonical. We propose a not necessarily polyhedral control structure for surface patches, regular control surfaces, which are certain C\^0 spline surfaces. While not unique, regular control surfaces are exactly the possible limiting positions of a Bezier patch when the weights are allowed to vary, but the control points are fixed.},
	isbn = {0103660054},
	issn = {07300301},
	doi = {10.1145/XXXXXXX.YYYYYYY},
	url = {http://doi.acm.org/10.1145/XXXXXXX.YYYYYYY http://life.kunzhou.net/2013/SPHsurfacetension.pdf},
	author = {He, Xiaowei and Wang, H and Zhang, Fengjun and Wang, Guoping and Zhou, Kun}
}
@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 {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}
}
@article {fiaschi_13_keeping,
	title = {Keeping Count: Leveraging Temporal Context to Count Heavily Overlapping Objects},
	journal = {ISBI 2013.Proceedings},
	year = {2013},
	note = {1},
	pages = {656-659},
	doi = {10.1109/ISBI.2013.6556560},
	author = {Fiaschi, L. and Karl-Heinz Grosser and Afonso, B. and Zlatic, M. and Fred A. Hamprecht}
}
@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.}
}
@article {horvat_12_one,
	title = {One plus one makes three (for social networks)},
	journal = {PLoS ONE},
	volume = {4,7},
	year = {2012},
	doi = {10.1371/journal.pone.0034740},
	author = {Horvát, E.-Á. and Hanselmann, M. and Fred A. Hamprecht and K. A. Zweig}
}
@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 {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.}
}
@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}
}
@article {horvat_12_you,
	title = {You Are Who Knows You: Predicting Links Between Non-Members of Facebook},
	journal = {European Conference on Complex Systems. Proceedings},
	volume = {3},
	year = {2012},
	note = {1},
	pages = {309-315},
	doi = {10.1007/978-3-319-00395-5_41},
	author = {Horvát, E.-Á. and Hanselmann, M. and Fred A. Hamprecht and K. A. Zweig}
}
@conference {schmidt2011a,
	title = {High frame rate for 3D time-of-flight cameras by dynamic sensor calibration},
	booktitle = {Proceedings IEEE International Conference on Computational Photography (ICCP)},
	year = {2011},
	pages = {1--8},
	abstract = {3D Time-of-Flight cameras are able to deliver robust depth maps of dynamic scenes. The frame rate, however, is limited because today{\textquoteright}s systems utilizing two-tap sensors need to acquire the required raw images in multiple instances in order to compute one depth map. These multiple raw images allow canceling out systematic errors introduced by asymmetries in the two taps, which otherwise would distort the reconstructed depth map. This work presents a method to implicitly calibrate these asymmetries of multi-tap 3D Time-of-Flight sensors. The calibration data are gathered from arbitrary live acquisitions possibly in real-time. The proposed correction of raw data supersedes the commonly used averaging technique. Thus it is possible to compute multiple depth maps from a single set of raw images. This increases the frame rate by at least a factor of two. The method is verified using real camera data and is evaluated quantitatively.},
	doi = {10.1109/ICCPHOT.2011.5753121},
	author = {Schmidt, Mirko and Klaus Zimmermann and Bernd J{\"a}hne}
}
@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}
}
@conference {lou_11_structured,
	title = {Structured Learning for Cell Tracking},
	booktitle = {NIPS 2011. Proceedings},
	year = {2011},
	note = {1},
	pages = {1296-1304},
	author = {Lou, X. and Fred A. Hamprecht},
	editor = {Shawe-Taylor, J. and Zemel, R.S. and Pereira, F.C.N. and Weinberger, K.Q. and Bartlett, P.}
}
@conference {schmidt2010,
	title = {Exact modelling of time-of-flight cameras for optimal depth maps},
	booktitle = {International Conference on Computational Photography (ICCP) 2010},
	year = {2010},
	note = {Poster},
	author = {Schmidt, Mirko and Michael Erz and Klaus Zimmermann and Bernd J{\"a}hne}
}
@conference {Gulshan2010,
	title = {Geodesic star convexity for interactive image segmentation},
	booktitle = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
	year = {2010},
	pages = {3129{\textendash}3136},
	abstract = {In this paper we introduce a new shape constraint for interactive image segmentation. It is an extension of Veksler{\textquoteright}s [25] star-convexity prior, in two ways: from a single star to multiple stars and from Euclidean rays to Geodesic paths. Global minima of the energy function are obtained subject to these new constraints. We also introduce Geodesic Forests, which exploit the structure of shortest paths in implementing the extended constraints. The star-convexity prior is used here in an interactive setting and this is demonstrated in a practical system. The system is evaluated by means of a "robot user" to measure the amount of interaction required in a precise way. We also introduce a new and harder dataset which augments the existing Grabcut dataset [1] with images and ground truth taken from the PASCAL VOC segmentation challenge [7]. {\textcopyright}2010 IEEE.},
	isbn = {9781424469840},
	issn = {10636919},
	doi = {10.1109/CVPR.2010.5540073},
	author = {Gulshan, Varun and Carsten Rother and Criminisi, Antonio and Blake, Andrew and Zisserman, Andrew}
}
@conference {atif2010,
	title = {A space-variant (3D) image simulation tool for computational cameras},
	booktitle = {International Conference on Computational Photography (ICCP) 2010},
	year = {2010},
	note = {Poster},
	author = {Atif, Muhammad and Klaus Zimmermann and Bernd J{\"a}hne}
}
@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 {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}
}
@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 {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}
}
@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.}
}
@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.}
}
@conference {zhang_05_voc,
	title = {Report about VOCs Dataset{\textquoteright}s Analysis based on Random Forests},
	booktitle = {Proceedings of the HPC-Asia05},
	year = {2005},
	pages = {603-607},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	author = {Zhang, H. and Fred A. Hamprecht and Amann, A.}
}
@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.}
}
@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}
}
@article {zhang2004,
	title = {Studying dynamical processes of air-sea exchanges with air-water interface image techniques},
	journal = {Recent Research Developments in Fluid Dynamics},
	volume = {5},
	year = {2004},
	pages = {57--87},
	url = {www-pord.ucsd.edu/~xzhang/publication/fd1.pdf},
	author = {Zhang, X. and Christoph S. Garbe}
}
@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}
}
@article {Weickert-et-al-01a,
	title = {Fast parallel algorithms for a broad class of nonlinear variational diffusion approaches},
	journal = {Real{\textendash}Time Imaging},
	volume = {7},
	number = {1},
	year = {2001},
	pages = {31{\textendash}45},
	author = {Weickert, J. and Heers, J. and Schn{\"o}rr, C. and Zuiderveld, K.{\textendash}J. and Scherzer, O. and Stiehl, H.{\textendash}S.}
}
@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 {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.}
}
@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}
}
