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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

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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

Ruhnau, P and Schnörr, C (2007). Optical Stokes Flow Estimation: An Imaging-Based Control Approach. Exp. in Fluids. 42 61–78

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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

Peckar, W, Schnörr, C, Rohr, K and Stiehl, H –S (1999). Parameter-Free Elastic Deformation Approach for 2D and 3D Registration Using Prescribed Displacements. J. Math. Imaging and Vision. 10 143–162

Bodnariuc, E, Petra, S, Poelma, C and Schnörr, C (2016). Parametric Dictionary-Based Velocimetry for Echo PIV. Proc. CGPR

Swoboda, P, Shekhovtsov, A, Kappes, J H, Schnörr, C and Savchynskyy, B (2016). Partial Optimality by Pruning for MAP-Inference with General Graphical Models. IEEE Trans. Patt. Anal. Mach. Intell. 38 1370–1382

Weickert, J and Schnörr, C (2000). PDE–Based Preprocessing of Medical Images. Künstliche Intelligenz. 3 5–10

Munder, S, Schnörr, C and Gavrila, D M (2008). Pedestrian Detection and Tracking Using a Mixture of View-Based Shape-Texture Models. IEEE Trans. Intell. Transp. Systems. 9 333-343

Schellewald, C, Roth, S and Schnörr, C (2002). Performance Evaluation Of A Convex Relaxation Approach To The Quadratic Assignment Of Relational Object Views. Dept. Math. and Comp. Science, University of Mannheim, Germany

Vlasenko, A and Schnörr, C (2010). Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates. IEEE Trans. Image Proc. 19 586-595

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

Weber, S, Schüle, T and Schnörr, C (2005). Prior Learning and Convex-Concave Regularization of Binary Tomography. Electr. Notes in Discr. Math. 20 313-327

Kappes, J, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2015). Probabilistic Correlation Clustering and Image Partitioning Using Perturbed Multicuts. Proc. SSVM. Springer

Rathke, F, Schmidt, S and Schnörr, C (2014). Probabilistic Intra-Retinal Layer Segmentation in 3-D OCT Images Using Global Shape Regularization. Medical Image Analysis. 18 781-794

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

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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

Lellmann, J and Schnörr, C (2011). Regularizers for Vector-Valued Data and Labeling Problems in Image Processing. Control Systems and Computers. 2 43–54

Schnörr, (1996). Repräsentation von Bilddaten mit einem konvexen Variationsansatz. Mustererkennung 1996. Springer-Verlag, Berlin, Heidelberg. 21–28

Schnörr, (1996). Representation Of Images By A Convex Variational Diffusion Approach. FB Informatik, Universität Hamburg

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

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Berger, J, Lenzen, F, Becker, F, Neufeld, A and Schnörr, C (2017). {Second-Order Recursive Filtering on the Rigid-Motion Lie Group SE(3) Based on Nonlinear Observations. J. Math. Imag. Vision. 58 102–129

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

Zisler, M, Zern, A, Petra, S and Schnörr, C (2019). Self-Assignment Flows for Unsupervised Data Labeling on Graphs. preprint: arXiv. https://arxiv.org/abs/1911.03472

Heiler, M, Keuchel, J and Schnörr, C (2005). Semidefinite Clustering for Image Segmentation with A-priori Knowledge. Pattern Recognition, Proc. 27th DAGM Symposium. Springer. 3663 309–317

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

Didden, E - M, Thorarinsdottir, T L, Lenkoski, A and Schnörr, C (2015). Shape from Texture using Locally Scaled Point Processes. Image Anal. Stereol. 34 161-170

Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160

Yuan, J, Schnörr, C and Steidl, G (2007). Simultaneous Optical Flow Estimation and Decomposition. SIAM J. Scientific Computing. 29 2283-2304

Lenzen, F, Lellmann, J, Becker, F and Schnörr, C (2014). Solving Quasi-Variational Inequalities for Image Restoration with Adaptive Constraint Sets. SIAM J. Imag. Sci. 7 2139–2174

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

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