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C. Schellewald and Schnörr, C., “Subgraph Matching with Semidefinite Programming”, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.
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.
C. Schnörr, “A Study of a Convex Variational Diffusion Approach for Image Segmentation and Feature Extraction”, J. of Math. Imag. and Vision, vol. 8, pp. 271–292, 1998.
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.
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. 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. Lenzen, Lellmann, J., Becker, F., and Schnörr, C., “Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets”, SIAM J. Imag. Sci., vol. 7, pp. 2139–2174, 2014.
J. Yuan, Schnörr, C., and Steidl, G., “Simultaneous Optical Flow Estimation and Decomposition”, SIAM J. Scientific Computing, vol. 29, pp. 2283-2304, 2007.
C. Schnörr, “Signal and Image Approximation with Level-Set Constraints”, Computing, vol. 81, pp. 137-160, 2007.
E. - M. Didden, Thorarinsdottir, T. L., Lenkoski, A., and Schnörr, C., “Shape from Texture using Locally Scaled Point Processes”, Image Anal. Stereol., vol. 34, pp. 161-170, 2015.
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.
M. Heiler, Keuchel, J., and Schnörr, C., “Semidefinite Clustering for Image Segmentation with A-priori Knowledge”, Pattern Recognition, Proc. 27th DAGM Symposium, vol. 3663. Springer, pp. 309–317, 2005.
M. Zisler, Zern, A., Petra, S., and Schnörr, C., “Self-Assignment Flows for Unsupervised Data Labeling on Graphs”, preprint: arXiv, 2019.
C. Schnörr, “Segmentation of Visual Motion by Minimizing Convex Non-Quadratic Functionals”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.
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.
L. Kostrykin, Schnörr, C., and Rohr, K., “Segmentation of Cell Nuclei Using Intensity-Based Model Fitting and Sequential Convex Programming”, in Proc. ISBI, 2018.
J. Berger, Lenzen, F., Becker, F., Neufeld, A., and Schnörr, C., “{Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations”, J. Math. Imag. Vision, vol. 58, pp. 102–129, 2017.
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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.
C. Schnörr, “Representation of Images by a Convex Variational Diffusion Approach”, FB Informatik, Universität Hamburg, FBI-HH-M-256/96, 1996.
C. Schnörr, “Repräsentation von Bilddaten mit einem konvexen Variationsansatz”, in Mustererkennung 1996, Berlin, Heidelberg, 1996, pp. 21–28.
J. Lellmann and Schnörr, C., “Regularizers for Vector-Valued Data and Labeling Problems in Image Processing”, Control Systems and Computers, vol. 2, pp. 43–54, 2011.
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.
J. Weickert and Schnörr, C., “Räumlich–zeitliche Berechnung des optischen Flusses mit nichtlinearen flussabhängigen Glattheitstermen”, in Mustererkennung 1999, 1999, pp. 317–324.
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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.
F. Rathke, Schmidt, S., and Schnörr, C., “Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization”, Medical Image Analysis, vol. 18, pp. 781-794, 2014.
J. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts”, in Proc. SSVM, 2015.
S. Weber, Schüle, T., and Schnörr, C., “Prior Learning and Convex-Concave Regularization of Binary Tomography”, Electr. Notes in Discr. Math., vol. 20, pp. 313-327, 2005.
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.
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.
C. Schellewald, Roth, S., and Schnörr, C., “Performance Evaluation of a Convex Relaxation Approach to the Quadratic Assignment of Relational Object Views”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 02/2002, 2002.
S. Munder, Schnörr, C., and Gavrila, D. M., “Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models”, IEEE Trans. Intell. Transp. Systems, vol. 9, pp. 333-343, 2008.
J. Weickert and Schnörr, C., “PDE–Based Preprocessing of Medical Images”, Künstliche Intelligenz, vol. 3, pp. 5–10, 2000.
P. Swoboda, Shekhovtsov, A., Kappes, J. H., Schnörr, C., and Savchynskyy, B., “Partial Optimality by Pruning for MAP-Inference with General Graphical Models”, IEEE Trans. Patt. Anal. Mach. Intell., vol. 38, pp. 1370–1382, 2016.
E. Bodnariuc, Petra, S., Poelma, C., and Schnörr, C., “Parametric Dictionary-Based Velocimetry for Echo PIV”, in Proc. CGPR, 2016.
W. Peckar, Schnörr, C., Rohr, K., and Stiehl, H. –S., “Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements”, J. Math. Imaging and Vision, vol. 10, pp. 143–162, 1999.

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