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J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc.~SSVM, 2015.

Technical Report (1.1 MB)

J. Hendrik Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic correlation clustering and image partitioning using perturbed Multicuts”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9087, pp. 231–242.

E. Bodnariuc, Schiffner, M. F., Petra, S., and Schnörr, C., “Plane Wave Acoustic Superposition for Fast Ultrasound Imaging”, International Ultrasonics Symposium. 2016.

A. Vlasenko and Schnörr, C., “Physically Consistent Variational Denoising of Image Fluid Flow Estimates”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 406--415.

Technical Report (1.6 MB)

A. Vlasenko and Schnörr, C., “Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates”, IEEE Trans.~Image Proc., vol. 19, pp. 586-595, 2010.

Technical Report (2.65 MB)

A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, pp. 73–102, 2014.

A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, p. 73--102, 2014.

Technical Report (2.24 MB)

P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Persistency by Pruning for General Graphical Models”, in submitted to NIPS 2013., 2013.

F. A. Hamprecht, Jähne, B., and Schnörr, C., Eds., Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14, 2007, Proceedings, vol. 4713. Springer, 2007.

C. Schnörr and Jähne, B., “Pattern Recognition, 29th DAGM Symposium, Heidelberg, Germany, September 12-14”, vol. 4713. Springer, 2007.

F. A. Hamprecht, Schnörr, C., and Jähne, B., Eds., “Pattern Recognition – 29th DAGM Symposium”, LCNS, vol. 4713. Springer, 2007.

P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Scale Space and Variational Methods (SSVM 2013), 2013.

Technical Report (159.71 KB)

P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM, 2013, pp. 477-488.

P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality via Iterative Pruning for the Potts Model”, in Scale Space and Variational Methods (SSVM 2013), 2013.

P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General Graphical Models”, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.

Technical Report (703.34 KB)

P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General GraphicalModels”, CVPR. Proceedings. pp. 1170-1177, 2014.

P. Swoboda, Shekhovtsov, A., Kappes, J. Hendrik, Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, pp. 1370–1382, 2016.

P. Swoboda, Savchynskyy, B., Kappes, J. H., and Schnörr, C., “Partial Optimality by Pruning for MAP-inference with General Graphical Models”, in IEEE Conference on Computer Vision and Pattern Recognition 2014, 2014.

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F. Rathke, Schmidt, S., and Schnörr, C., “Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography”, in MICCAI, 2011, vol. 6893, pp. 370–377.

F. Rathke, Schmidt, S., and Schnörr, C., “Order preserving and shape prior constrained intra-retinal layer segmentation in optical coherence tomography”, Medical Image Computing and Computer-Assisted Intervention (MICCAI 2011), vol. 6893. Springer, pp. 370–377, 2011.

F. Rathke, Schmidt, S., and Schnörr, C., “Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography”, in MICCAI 2011, Proceedings, 2011, vol. 6893, pp. 370-377.

F. Rathke, Schmidt, S., and Schnörr, C., “Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography”, in MICCAI, 2011, vol. 6893, p. 370--377.

Technical Report (1.12 MB)

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for Variational Relaxations of Optimal Partition Problems”, 2010.

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47, pp. 239-257, 2012.

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, in Energy Min. Meth. Comp. Vis. Patt. Recogn., 2011, vol. 6819, p. 132--146.

Technical Report (1 MB)

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47, pp. 239-257, 2012.

Technical Report (616.16 KB)

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, IPA group, Heidelberg University, 2011.

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, in Energy Min. Meth. Comp. Vis. Patt. Recogn., 2011, vol. 6819, pp. 132–146.

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, Journal of Mathematical Imaging and Vision, vol. 47 (3), pp. 239-257, 2013.

J. Lellmann, Lenzen, F., and Schnörr, C., “Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem”, in Energy Min. Meth. Comp. Vis. Patt. Recogn., 2011, pp. 132-146.

P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp.~in Fluids, vol. 42, p. 61--78, 2007.

Technical Report (1.54 MB)

F. Becker, Petra, S., and Schnörr, C., “Optical Flow”, Handbook of Mathematical Methods in Imaging. Springer, 2014.

P. Ruhnau, Stahl, A., and Schnörr, C., “On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization”, in Proc.~DAGM 2006, 2006, vol. 375-388, pp. 375-388.

Technical Report (902.47 KB)

B. Schmitzer and Schnörr, C., “Object Segmentation by Shape Matching with Wasserstein Modes”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013), 2013, pp. 123-136.

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D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.

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