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Conference Paper
R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, in Proc. SSVM, 2019.
M. Heiler and Schnörr, C., “Learning Sparse Image Codes by Convex Programming”, in Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05), Beijing, China, 2005, pp. 1667-1674.
S. Weber, Schüle, T., Schnörr, C., and Hornegger, J., “A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections”, in Bildverarbeitung für die Medizin 2003, 2003, pp. 41–45.
S. Weber, Schnörr, C., and Hornegger, J., “A Linear Programming Relaxation for Binary Tomography with Smoothness Priors”, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.
E. Bodnariuc, Petra, S., Schnörr, C., and Voorneveld, J., “A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry”, in Proc. GCPR, 2017.
F. Rathke, Desana, M., and Schnörr, C., “Locally Adaptive Probabilistic Models for Global Segmentation of Pathological OCT Scans”, in Proc. MICCAI, 2017.
F. Aström, Hühnerbein, R., Savarino, F., Recknagel, J., and Schnörr, C., “MAP Image Labeling Using Wasserstein Messages and Geometric Assignment”, in Proc. SSVM, 2017, vol. 10302.
J. H. Kappes and Schnörr, C., “MAP-Inference for Highly-Connected Graphs with DC-Programming”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 1–10.
M. Wulf, Stiehl, H. S., and Schnörr, C., “A model of spatiotemporal receptive fields in the primate retina”, in Proc. 1st Göttingen Conf. German Neurosci. Soc., 1999, vol. II.
M. Wulf, Stiehl, H. S., and Schnörr, C., “Modeling spatiotemporal receptive fields in the primate retina”, in Proc. Cognitive Neurosci. Conf., Bremen, Germany, 1999.
C. Schnörr and Peckar, W., “Motion-Based Identification of Deformable Templates”, in Proc. 6th Int. Conf. on Computer Analysis of Images and Patterns (CAIP '95), Prague, Czech Republic, 1995, vol. 970, pp. 122-129.
J. H. Kappes, Schmidt, S., and Schnörr, C., “MRF Inference by k-Fan Decomposition and Tight Lagrangian Relaxation”, in European Conference on Computer Vision (ECCV), 2010, vol. 6313, pp. 735–747.
M. Heiler and Schnörr, C., “Natural Statistics for Natural Image Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV 2003), Nice, France, 2003, pp. 1259-1266.
R. Sprengel and Schnörr, C., “Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme”, in Mustererkennung 1993, 15. DAGM-Symposium, 1993, pp. 134–141.
W. Peckar, Schnörr, C., Rohr, K., and Stiehl, H. S., “Non-Rigid Image Registration Using a Parameter-Free Elastic Model”, in 9th British Machine Vision Conference (BMVC`98), Southampton/UK, 1998, pp. 134–143.
F. Savarino, Hühnerbein, R., Aström, F., Recknagel, J., and Schnörr, C., “Numerical Integration of Riemannian Gradient Flows for Image Labeling”, in Proc. SSVM, 2017, vol. 10302.
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.
T. Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., “Parallel Variational Motion Estimation by Domain Decomposition and Cluster Computing”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 205-216.
J. Heers, Schnörr, C., and Stiehl, H. –S., “Parallele und global konvergente iterative Minimierung nichtlinearer Variationsansätze zur adaptiven Glättung und Segmentation von Bildern”, in Mustererkennung 1998, Heidelberg, 1998.
E. Bodnariuc, Petra, S., Poelma, C., and Schnörr, C., “Parametric Dictionary-Based Velocimetry for Echo PIV”, in Proc. CGPR, 2016.
A. Vlasenko and Schnörr, C., “Physically Consistent Variational Denoising of Image Fluid Flow Estimates”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 406–415.
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
C. Schellewald and Schnörr, C., “Probabilistic Subgraph Matching Based on Convex Relaxation”, in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), 2005, vol. 3757, pp. 171-186.
J. Weickert and Schnörr, C., “Räumlich–zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen”, in Mustererkennung 1999, 1999, pp. 317–324.
K. Wiehler, Grigat, R. –R., Heers, J., Schnörr, C., and Stiehl, H. –S., “Real–Time Adaptive Smoothing with a 1D Nonlinear Relaxation Network in Analogue VLSI Technology”, in Mustererkennung 1998, Heidelberg, 1998.
C. Schnörr, “Repräsentation von Bilddaten mit einem konvexen Variationsansatz”, in Mustererkennung 1996, Berlin, Heidelberg, 1996, pp. 21–28.
M. Heiler and Schnörr, C., “Reverse-Convex Programming for Sparse Image Codes”, in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'05), 2005, vol. 3757, pp. 600-616.
L. Kostrykin, Schnörr, C., and Rohr, K., “Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming”, in Proc. ISBI, 2018.
P. Markowsky, Reith, S., Zuber, T. E., König, R., Rohr, K., and Schnörr, C., “Segmentation of cell structure using model-based set covering with iterative reweighting”, in Proc. ISBI, 2017.
C. Schnörr, “Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.
S. Gerloff, Hagemann, A., Schnörr, C., Tieck, S., Stiehl, H. S., Dombrowski, R., Dreyer, M., and Wiesendanger, R., “Semi–Automated Analysis of SXM Images”, in Proc. 9th Int. Conf. on Scanning Tunneling Microscopy/Spectroscopy and Related Techniques (STM'97), Hamburg, Germany, 1997.
S. Petra, Schröder, A., Wieneke, B., and Schnörr, C., “On Sparsity Maximization in Tomographic Particle Image Reconstruction”, in Pattern Recognition – 30th DAGM Symposium, 2008, vol. 5096, pp. 294–303.
F. Lauer and Schnörr, C., “Spectral Clustering of Linear Subspaces for Motion Segmentation”, in Proc. IEEE Int. Conf. Computer Vision (ICCV'09), Kyoto, Japan, 2009.
S. Schmidt, Kappes, J. H., Bergtholdt, M., Pekar, V., Dries, S., Bystrov, D., and Schnörr, C., “Spine Detection and Labeling Using a Parts-Based Graphical Model”, in Proc. 20th International Conference on Information Processing in Medical Imaging (IPMI 2007), 2007, vol. 4584, pp. 122-133.
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling”, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.

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