Publications

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Conference Paper
Heers, J, Schnörr, C and Stiehl, H S (1998). Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation. Proc.~IEEE Int.~Conf.~Image Proc
Berger, J and Schnörr, C (2016). Joint Recursive Monocular Filtering of Camera Motion and Disparity Map. 38th German Conference on Pattern Recognition. Springer, Hannover. https://arxiv.org/abs/1606.02092PDF icon Technical Report (2.34 MB)
Bergtholdt, M, Kappes, J H and Schnörr, C (2006). Learning of Graphical Models and Efficient Inference for Object Class Recognition. Proc.~DAGM 2006. Springer. 375-388 375-388
Heiler, M and Schnörr, C (2005). Learning Sparse Image Codes by Convex Programming. Proc.~Tenth IEEE Int.~Conf.~Computer Vision (ICCV'05). 1667-1674
Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes. 3rd Workshop on Dynamic Perception. Akad.~Verlagsges. 9 117--122
Weber, S, Schüle, T, Schnörr, C and Hornegger, J (2003). A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections. Bildverarbeitung für die Medizin 2003. Springer Verlag. 41--45
Weber, S, Schnörr, C and Hornegger, J (2003). A Linear Programming Relaxation for Binary Tomography with Smoothness Priors. Proc.~Int.~Workshop on Combinatorial Image Analysis (IWCIA'03)
Kappes, J H and Schnörr, C (2008). MAP-Inference for Highly-Connected Graphs with DC-Programming. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 1--10PDF icon Technical Report (1.91 MB)
Kappes, J H, Beier, T and Schnörr, C (2014). MAP-Inference on Large Scale Higher-Order Discrete Graphical Models by Fusion Moves. Computer Vision - {ECCV} 2014 Workshops - Zurich, Switzerland, September 6-7 and 12, 2014, Proceedings, Part {II}. http://dx.doi.org/10.1007/978-3-319-16181-5_37PDF icon Technical Report (557.49 KB)
Welk, M, Becker, F, Schnörr, C and Weickert, J (2005). Matrix-Valued Filters as Convex Programs. Scale-Space 2005. Springer. 3459 204--216
Wulf, M, Stiehl, H S and Schnörr, C (1999). A model of spatiotemporal receptive fields in the primate retina. Proc.~1st Göttingen Conf.~German Neurosci.~Soc.. II
Wulf, M, Stiehl, H S and Schnörr, C (1999). Modeling spatiotemporal receptive fields in the primate retina. Proc.~Cognitive Neurosci.~Conf
Cremers, D and Schnörr, C (2002). Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization. Pattern Recognition, Proc.~24th DAGM Symposium. Springer. 2449 472--480
Schnörr, C and Peckar, W (1995). Motion-Based Identification of Deformable Templates. Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP '95). Springer Verlag. 970 122-129
Kappes, J H, Schmidt, S and Schnörr, C (2010). MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. European Conference on Computer Vision (ECCV). Springer Berlin / Heidelberg. 6313 735--747PDF icon Technical Report (1.49 MB)
Kappes, J H, Schmidt, S and Schnörr, C (2010). MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation. European Conference on Computer Vision (ECCV). Springer. 6313 735--747
Cremers, D, Sochen, N and Schnörr, C (2004). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. Computer Vision -- ECCV 2004. Springer. 3024 74-86
Heiler, M and Schnörr, C (2003). Natural Statistics for Natural Image Segmentation. Proc.~IEEE Int.~Conf.~Computer Vision (ICCV 2003). 1259-1266
Sprengel, R and Schnörr, C (1993). Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme. Mustererkennung 1993, 15. DAGM-Symposium. Springer Verlag. 134--141
Cremers, D, Kohlberger, T and Schnörr, C (2002). Nonlinear Shape Statistics in Mumford-Shah Based Segmentation. Computer Vision -- ECCV 2002). Springer Verlag. 2351 93--108PDF icon Technical Report (636.58 KB)
Cremers, D, Kohlberger, T and Schnörr, C (2001). Nonlinear Shape Statistics via Kernel Spaces. Mustererkennung 2001. Springer. 2191 269--276PDF icon Technical Report (324.55 KB)
Peckar, W, Schnörr, C, Rohr, K and Stiehl, H S (1998). Non-Rigid Image Registration Using a Parameter-Free Elastic Model. 9th British Machine Vision Conference (BMVC`98). 134--143
Schmitzer, B and Schnörr, C (2013). Object Segmentation by Shape Matching with Wasserstein Modes. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013). 123-136
Ruhnau, P, Stahl, A and Schnörr, C (2006). On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization. Proc.~DAGM 2006. Springer. 375-388 375-388PDF icon Technical Report (902.47 KB)
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Energy Min. Meth. Comp. Vis. Patt. Recogn. Springer. 132-146
Lellmann, J, Lenzen, F and Schnörr, C (2011). Optimality Bounds for a Variational Relaxation of the Image Partitioning Problem. Energy Min. Meth. Comp. Vis. Patt. Recogn. Springer. 6819 132--146PDF icon Technical Report (1 MB)
Rathke, F, Schmidt, S and Schnörr, C (2011). Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography. MICCAI. Springer. 6893 370--377PDF icon Technical Report (1.12 MB)
Rathke, F, Schmidt, S and Schnörr, C (2011). Order Preserving and Shape Prior Constrained Intra-Retinal Layer Segmentation in Optical Coherence Tomography. MICCAI 2011, Proceedings. Springer. 6893 370-377
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2004). Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing. Computer Vision -- ECCV 2004. Springer. 3024 205-216
Heers, J, Schnörr, C and Stiehl, H S (1998). Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern. Mustererkennung 1998. Springer
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2014). Partial Optimality by Pruning for MAP-inference with General Graphical Models. IEEE Conference on Computer Vision and Pattern Recognition 2014PDF icon Technical Report (703.34 KB)
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM. 477-488
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Partial Optimality via Iterative Pruning for the Potts Model. Scale Space and Variational Methods (SSVM 2013)PDF icon Technical Report (159.71 KB)
Swoboda, P, Savchynskyy, B, Kappes, J H and Schnörr, C (2013). Persistency by Pruning for General Graphical Models. submitted to NIPS 2013
Vlasenko, A and Schnörr, C (2008). Physically Consistent Variational Denoising of Image Fluid Flow Estimates. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 406--415PDF icon Technical Report (1.6 MB)

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