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A
Jähne, (2019). Air-Sea Gas Exchange. Encyclopedia of Ocean Sciences. Academic Press. 6 1–13
Hosseini Jafari, O, Groth, O, Kirillov, A, Yang, M Ying and Rother, C (2017). Analyzing modular CNN architectures for joint depth prediction and semantic segmentation. Proceedings - IEEE International Conference on Robotics and Automation. 4620–4627. http://arxiv.org/abs/1702.08009 http://dx.doi.org/10.1109/ICRA.2017.7989537
B
Yarlagadda, P and Ommer, B (2015). Beyond the Sum of Parts: Voting with Groups of Dependent Entities. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. 37 1134--1147. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6926849
C
Mustikovela, S Karthik, Yang, M Ying and Rother, C (2016). Can ground truth label propagation from video help semantic segmentation?. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9915 LNCS 804–820
Zhang, C, Yarkony, J and Hamprecht, F A (2014). Cell detection and segmentation using correlation clustering. MICCAI. Proceedings. Springer. 9-16PDF icon Technical Report (8.06 MB)
Kluger, F, Brachmann, E, Ackermann, H, Rother, C, Yang, M Ying and Rosenhahn, B (2020). CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. CVPR 2020. http://arxiv.org/abs/2001.02643PDF icon PDF (9.95 MB)
Yuan, J, Schnörr, C and Steidl, G (2009). Convex Hodge Decomposition and Regularization of Image Flows. J.~Math.~Imag.~Vision. 33 169-177PDF icon Technical Report (1003.75 KB)
Yuan, J, Steidl, G and Schnörr, C (2008). Convex Hodge Decomposition of Image Flows. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 416--425PDF icon Technical Report (290.72 KB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2008). Convex Multi-Class Image Labeling By Simplex-Constrained Total Variation. IWR, University of Heidelberg. http://www.ub.uni-heidelberg.de/archiv/8759/PDF icon Technical Report (2.6 MB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162PDF icon Technical Report (1.75 MB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F, Schnörr, C, Mórken, K and Lysaker, M (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162
Yuan, J, Schnörr, C, Kohlberger, T and Ruhnau, P (2004). Convex Set-Based Estimation of Image Flows. ICPR 2004 – 17th Int. Conf. on Pattern Recognition. IEEE, Cambridge, UK. 1 124-127
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Jähne, (2007). Data acquisition by imaging detectors. Handbook of Experimental Fluid Mechanics. Springer. 1419--1436
Jähne, B, Klar, M and Jehle, M (2007). Data analysis. Handbook of Experimental Fluid Mechanics. Springer. 1437--1491
Nowozin, S, Rother, C, Bagon, S, Sharp, T, Yao, B and Kohli, P (2011). Decision tree fields. Proceedings of the IEEE International Conference on Computer Vision. 1668–1675
Yuan, J, Schnörr, C and Mémin, E (2007). Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. J.~Math.~Imag.~Vision. 28 67-80PDF icon Technical Report (752.44 KB)
Yuan, J, Ruhnau, P, Mémin, E and Schnörr, C (2005). Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. Scale-Space 2005. Springer. 3459 267–278
F
Yarlagadda, P and Ommer, B (2012). From Meaningful Contours to Discriminative Object Shape. Proceedings of the European Conference on Computer Vision. Springer. 7572 766--779PDF icon Technical Report (4.58 MB)
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Yarkony, J, Zhang, C and Fowlkes, C C (2014). Hierarchical Planar Correlation Clustering for Cell Segmentation. EMMCVPR. Proceedings. Springer. 8932 492-504PDF icon Technical Report (548.12 KB)
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Ding, L and Yilmaz, A (2010). Interactive image segmentation using probabilistic hypergraphs. Pattern Recognition. 43 1863–1873. http://www.research.microsoft.com/vision/cambridge
L
Krull, A, Brachmann, E, Michel, F, Yang, M Ying, Gumhold, S and Rother, C (2015). Learning analysis-by-synthesis for 6d pose estimation in RGB-D images. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter 954–962
Yarlagadda, P, Eigenstetter, A and Ommer, B (2012). Learning Discriminative Chamfer Regularization. BMVC. Springer. 1--11. http://www.bmva.org/bmvc/2012/BMVC/paper020/paper020.pdf
M
Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12
Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12. http://arxiv.org/abs/1507.07583
Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12. https://github.com/BVLC/caffe/wiki/Model-Zoo\#fcn
Eigenstetter, A, Yarlagadda, P and Ommer, B (2012). Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching. Proceedins of the Aian Conference on Computer Vision. Springer. 152--163PDF icon Technical Report (7.31 MB)
P
Yarkony, J, Beier, T, Baldi, P and Hamprecht, F A (2014). Parallel Multicut Segmentation via Dual Decomposition. New Frontiers in Mining Complex Patterns - Third International Workshop, {NFMCP} 2014, Held in Conjunction with {ECML-PKDD} 2014, Nancy, France, September 19, 2014, Revised Selected Papers. http://dx.doi.org/10.1007/978-3-319-17876-9_4
Michel, F, Krull, A, Brachmann, E, Yang, M Ying, Gumhold, S and Rother, C (2015). Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression. 181.1–181.11
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Hosseini Jafari, O and Yang, M Ying (2016). Real-time RGB-D based template matching pedestrian detection. Proceedings - IEEE International Conference on Robotics and Automation. 2016-June 5520–5527
Yarlagadda, P, Monroy, A, Carque, B and Ommer, B (2010). Recognition and Analysis of Objects in Medieval Images. Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage. Springer. 296--305PDF icon Technical Report (2.76 MB)
S
Hullin, M, Klein, R, Schultz, T, Yao, A, Li, W, Hosseini Jafari, O and Rother, C (2017). Semantic-Aware Image Smoothing. Vision, Modeling, and Visualization. https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf
Yuan, J, Schnörr, C and Steidl, G (2007). Simultaneous Optical Flow Estimation and Decomposition. SIAM J.~Scientific Computing. 29 2283-2304PDF icon Technical Report (1.16 MB)
Yuan, J, Schnörr, C and Steidl, G (2007). Simultaneous Optical Flow Estimation and Decomposition. SIAM J. Scientific Computing. 29 2283-2304
Yuan, J, Schnörr, C, Steidl, G and Becker, F (2005). A Study of Non-Smooth Convex Flow Decomposition. Proc. Variational, Geometric and Level Set Methods in Computer Vision. Springer. 3752 1–12
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Yarlagadda, P, Monroy, A, Carque, B and Ommer, B (2011). Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections. Conference on Computer Vision and Image Analysis of Art II. 7869 61--69PDF icon Technical Report (11.06 MB)

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