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A

B. Jähne, “Air-Sea Gas Exchange”, Encyclopedia of Ocean Sciences, vol. 6. Academic Press, pp. 1–13, 2019.

O. Hosseini Jafari, Groth, O., Kirillov, A., Yang, M. Ying, and Rother, C., “Analyzing modular CNN architectures for joint depth prediction and semantic segmentation”, in Proceedings - IEEE International Conference on Robotics and Automation, 2017, pp. 4620–4627.

B

P. Yarlagadda and Ommer, B., “Beyond the Sum of Parts: Voting with Groups of Dependent Entities”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 37, p. 1134--1147, 2015.

C

S. Karthik Mustikovela, Yang, M. Ying, and Rother, C., “Can ground truth label propagation from video help semantic segmentation?”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9915 LNCS, pp. 804–820.

C. Zhang, Yarkony, J., and Hamprecht, F. A., “Cell detection and segmentation using correlation clustering”, in MICCAI. Proceedings, 2014, pp. 9-16.

Technical Report (8.06 MB)

F. Kluger, Brachmann, E., Ackermann, H., Rother, C., Yang, M. Ying, and Rosenhahn, B., “CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, in CVPR 2020, 2020.

PDF (9.95 MB)

J. Yuan, Schnörr, C., and Steidl, G., “Convex Hodge Decomposition and Regularization of Image Flows”, J.~Math.~Imag.~Vision, vol. 33, pp. 169-177, 2009.

Technical Report (1003.75 KB)

J. Yuan, Steidl, G., and Schnörr, C., “Convex Hodge Decomposition of Image Flows”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 416--425.

Technical Report (290.72 KB)

J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, IWR, University of Heidelberg, 2008.

Technical Report (2.6 MB)

J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.

Technical Report (1.75 MB)

J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., Schnörr, C., Mórken, K., and Lysaker, M., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.

J. Yuan, Schnörr, C., Kohlberger, T., and Ruhnau, P., “Convex Set-Based Estimation of Image Flows”, in ICPR 2004 – 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, vol. 1, pp. 124-127.

D

B. Jähne, “Data acquisition by imaging detectors”, Handbook of Experimental Fluid Mechanics. Springer, p. 1419--1436, 2007.

B. Jähne, Klar, M., and Jehle, M., “Data analysis”, Handbook of Experimental Fluid Mechanics. Springer, p. 1437--1491, 2007.

S. Nowozin, Rother, C., Bagon, S., Sharp, T., Yao, B., and Kohli, P., “Decision tree fields”, in Proceedings of the IEEE International Conference on Computer Vision, 2011, pp. 1668–1675.

J. Yuan, Schnörr, C., and Mémin, E., “Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation”, J.~Math.~Imag.~Vision, vol. 28, pp. 67-80, 2007.

Technical Report (752.44 KB)

J. Yuan, Ruhnau, P., Mémin, E., and Schnörr, C., “Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation”, in Scale-Space 2005, 2005, vol. 3459, pp. 267–278.

F

P. Yarlagadda and Ommer, B., “From Meaningful Contours to Discriminative Object Shape”, in Proceedings of the European Conference on Computer Vision, 2012, vol. 7572, p. 766--779.

Technical Report (4.58 MB)

H

J. Yarkony, Zhang, C., and Fowlkes, C. C., “Hierarchical Planar Correlation Clustering for Cell Segmentation”, in EMMCVPR. Proceedings, 2014, vol. 8932, pp. 492-504.

Technical Report (548.12 KB)

I

L. Ding and Yilmaz, A., “Interactive image segmentation using probabilistic hypergraphs”, Pattern Recognition, vol. 43, pp. 1863–1873, 2010.

L

A. Krull, Brachmann, E., Michel, F., Yang, M. Ying, Gumhold, S., and Rother, C., “Learning analysis-by-synthesis for 6d pose estimation in RGB-D images”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 954–962.

P. Yarlagadda, Eigenstetter, A., and Ommer, B., “Learning Discriminative Chamfer Regularization”, in BMVC, 2012, p. 1--11.

M

D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., “Mapping auto-context decision forests to deep convnets for semantic segmentation”, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.

A. Eigenstetter, Yarlagadda, P., and Ommer, B., “Max-Margin Regularization for Reducing Accidentalness in Chamfer Matching”, in Proceedins of the Aian Conference on Computer Vision, 2012, p. 152--163.

Technical Report (7.31 MB)

P

J. Yarkony, Beier, T., Baldi, P., and Hamprecht, F. A., “Parallel Multicut Segmentation via Dual Decomposition”, in 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, 2014.

F. Michel, Krull, A., Brachmann, E., Yang, M. Ying, Gumhold, S., and Rother, C., “Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression”, 2015, pp. 181.1–181.11.

R

O. Hosseini Jafari and Yang, M. Ying, “Real-time RGB-D based template matching pedestrian detection”, in Proceedings - IEEE International Conference on Robotics and Automation, 2016, vol. 2016-June, pp. 5520–5527.

P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Recognition and Analysis of Objects in Medieval Images”, in Proceedins of the Aian Conference on Computer Vision, Workshop on e-Heritage, 2010, p. 296--305.

Technical Report (2.76 MB)

S

M. Hullin, Klein, R., Schultz, T., Yao, A., Li, W., Hosseini Jafari, O., and Rother, C., “Semantic-Aware Image Smoothing”, Vision, Modeling, and Visualization, 2017.

J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J.~Scientific Computing, vol. 29, pp. 2283-2304, 2007.

Technical Report (1.16 MB)

J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J. Scientific Computing, vol. 29, pp. 2283-2304, 2007.

J. Yuan, Schnörr, C., Steidl, G., and Becker, F., “A Study of Non-Smooth Convex Flow Decomposition”, in Proc. Variational, Geometric and Level Set Methods in Computer Vision, 2005, vol. 3752, pp. 1–12.

T

P. Yarlagadda, Monroy, A., Carque, B., and Ommer, B., “Top-down Analysis of Low-level Object Relatedness Leading to Semantic Understanding of Medieval Image Collections”, in Conference on Computer Vision and Image Analysis of Art II, 2011, vol. 7869, p. 61--69.

Technical Report (11.06 MB)

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