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Conference Paper
Hühnerbein, R, Savarino, F, Petra, S and Schnörr, C (2019). Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. Proc. SSVM. Springer
Heiler, M and Schnörr, C (2005). Learning Sparse Image Codes by Convex Programming. Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05). Beijing, China. 1667-1674
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). Palermo, Italy
Bodnariuc, E, Petra, S, Schnörr, C and Voorneveld, J (2017). A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry. Proc. GCPR
Rathke, F, Desana, M and Schnörr, C (2017). Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans. Proc. MICCAI
Aström, F, Hühnerbein, R, Savarino, F, Recknagel, J and Schnörr, C (2017). MAP Image Labeling Using Wasserstein Messages and Geometric Assignment. Proc. SSVM. Springer. 10302
Kappes, J H and Schnörr, C (2008). MAP-Inference for Highly-Connected Graphs with DC-Programming. Pattern Recognition – 30th DAGM Symposium. Springer Verlag. 5096 1–10
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. Hanse–Wissenschaftskolleg, Bremen, Germany
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, Prague, Czech Republic. 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 735–747
Heiler, M and Schnörr, C (2003). Natural Statistics for Natural Image Segmentation. Proc. IEEE Int. Conf. Computer Vision (ICCV 2003). Nice, France. 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
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). Southampton/UK. 134–143
Savarino, F, Hühnerbein, R, Aström, F, Recknagel, J and Schnörr, C (2017). Numerical Integration of Riemannian Gradient Flows for Image Labeling. Proc. SSVM. Springer. 10302
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. 375-388 375-388
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, Heidelberg
Bodnariuc, E, Petra, S, Poelma, C and Schnörr, C (2016). Parametric Dictionary-Based Velocimetry for Echo PIV. Proc. CGPR
Vlasenko, A and Schnörr, C (2008). Physically Consistent Variational Denoising of Image Fluid Flow Estimates. Pattern Recognition – 30th DAGM Symposium. Springer Verlag. 5096 406–415
Kappes, J, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc. SSVM. Springer
Schellewald, C and Schnörr, C (2005). Probabilistic Subgraph Matching Based on Convex Relaxation. Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 171-186
Weickert, J and Schnörr, C (1999). Räumlich–zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen. Mustererkennung 1999. Springer. 317–324
Wiehler, K, Grigat, R –R, Heers, J, Schnörr, C and Stiehl, H –S (1998). Real–Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology. Mustererkennung 1998. Springer, Heidelberg
Schnörr, (1996). Repräsentation von Bilddaten mit einem konvexen Variationsansatz. Mustererkennung 1996. Springer-Verlag, Berlin, Heidelberg. 21–28
Heiler, M and Schnörr, C (2005). Reverse-Convex Programming for Sparse Image Codes. Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05). Springer. 3757 600-616
Kostrykin, L, Schnörr, C and Rohr, K (2018). Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming. Proc. ISBI
Markowsky, P, Reith, S, Zuber, T E, König, R, Rohr, K and Schnörr, C (2017). Segmentation of cell structure using model-based set covering with iterative reweighting. Proc. ISBI
Schnörr, (1994). Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals. 12th Int. Conf. on Pattern Recognition. Jerusalem, Israel
Gerloff, S, Hagemann, A, Schnörr, C, Tieck, S, Stiehl, H S, Dombrowski, R, Dreyer, M and Wiesendanger, R (1997). Semi–Automated Analysis of SXM Images. Proc. 9th Int. Conf. on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97). Hamburg, Germany
Petra, S, Schröder, A, Wieneke, B and Schnörr, C (2008). On Sparsity Maximization in Tomographic Particle Image Reconstruction. Pattern Recognition – 30th DAGM Symposium. Springer Verlag. 5096 294–303
Lauer, F and Schnörr, C (2009). Spectral Clustering of Linear Subspaces for Motion Segmentation. Proc. IEEE Int. Conf. Computer Vision (ICCV'09). Kyoto, Japan
Schmidt, S, Kappes, J H, Bergtholdt, M, Pekar, V, Dries, S, Bystrov, D and Schnörr, C (2007). Spine Detection and Labeling Using a Parts-Based Graphical Model. Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007). Springer. 4584 122-133
Savchynskyy, B, Kappes, J H, Schmidt, S and Schnörr, C (2011). A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)

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