Publications

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Journal Article
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
Yuan, J, Schnörr, C and Steidl, G (2007). Simultaneous Optical Flow Estimation and Decomposition. SIAM J. Scientific Computing. 29 2283-2304
Schnörr, (2007). Signal and Image Approximation with Level-Set Constraints. Computing. 81 137-160
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
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
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
Lellmann, J and Schnörr, C (2011). Regularizers for Vector-Valued Data and Labeling Problems in Image Processing. Control Systems and Computers. 2 43–54
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
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
Vlasenko, A and Schnörr, C (2010). Physically Consistent and Efficient Variational Denoising of Image Fluid Flow Estimates. IEEE Trans. Image Proc. 19 586-595
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
Weickert, J and Schnörr, C (2000). PDE–Based Preprocessing of Medical Images. Künstliche Intelligenz. 3 5–10
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
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
Ruhnau, P and Schnörr, C (2007). Optical Stokes Flow Estimation: An Imaging-Based Control Approach. Exp. in Fluids. 42 61–78
Schnörr, C and Sprengel, R (1994). A Nonlinear Regularization Approach to Early Vision. Biol. Cybernetics. 72 141–149
Heiler, M and Schnörr, C (2005). Natural Image Statistics for Natural Image Segmentation. Int. J. Comp. Vision. 63 5–19
Kappes, J H, Swoboda, P, Savchynskyy, B, Hazan, T and Schnörr, C (2016). Multicuts and Perturb & MAP for Probabilistic Graph Clustering. J. Math. Imag. Vision. 56 221–237
Welk, M, Weickert, J, Becker, F, Schnörr, C, Feddern, C and Burgeth, B (2007). Median and related local filters for tensor-valued images. Signal Processing. 87 291-308
Weber, S, Schüle, T, Schnörr, C and Hornegger, J (2004). A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections. Methods of Information in Medicine. 43 320–326
Peckar, W, Schnörr, C, Rohr, K, Stiehl, H –S and Spetzger, U (1998). Linear and Incremental Estimation of Elastic Deformations in Medical Registration Using Prescribed Displacements. Machine Graphics & Vision. 7 807–829
Heiler, M and Schnörr, C (2006). Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming. J. Mach. Learning Res. 7 1385–1407. http://www.cvgpr.uni-mannheim.de/Publications
Hühnerbein, R, Savarino, F, Petra, S and Schnörr, C (2019). Learning Adaptive Regularization for Image Labeling Using Geometric Assignment. preprint: arXiv. https://arxiv.org/abs/1910.09976
Censor, Y, Gibali, A, Lenzen, F and Schnörr, C (2016). The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising. J. Comp. Math. 34 608-623
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2017). Image Labeling by Assignment. J. Math. Imag. Vision. 58 211–238. Papers/Astroem2017.pdf
Hühnerbein, R, Savarino, F, Aström, F and Schnörr, C (2018). Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment. SIAM J. Imaging Science. 11 1317–1362. https://epubs.siam.org/doi/abs/10.1137/17M1150669
Kappes, J, Speth, M, Reinelt, G and Schnörr, C (2016). Higher-order Segmentation via Multicuts. Comp. Vision Image Understanding. 143 104–119
Heers, J, Schnörr, C and Stiehl, H S (2001). Globally–Convergent Iterative Numerical Schemes for Non–Linear Variational Image Smoothing and Segmentation on a Multi–Processor Machine. IEEE Trans. Image Proc. 10 852–864
Kostrykin, L, Schnörr, C and Rohr, K (2019). Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information. Medical Image Analysis. https://doi.org/10.1016/j.media.2019.101536
Schmitzer, B and Schnörr, C (2015). Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes. J. Math. Imag. Vision. 52 436–458. http://link.springer.com/article/10.1007/s10851-014-0546-8
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2018). Geometric Numerical Integration of the Assignment Flow. preprint: arXiv. https://arxiv.org/abs/1810.06970
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2020). Geometric Numerical Integration of the Assignment Flow. Inverse Problems. 36 034004 (33pp)
Zeilmann, A, Savarino, F, Petra, S and Schnörr, C (2019). Geometric Numerical Integration of the Assignment Flow. Inverse Problems. https://doi.org/10.1088/1361-6420/ab2772
Aström, F and Schnörr, C (2017). A Geometric Approach for Color Image Regularization. Comp. Vision Image Understanding. 165 43–59. https://doi.org/10.1016/j.cviu.2017.10.013
Nicola, A, Petra, S, Popa, C and Schnörr, C (2011). A general extending and constraining procedure for linear iterative methods. Int. J. Comp. Math. http://dx.doi.org/10.1080/00207160.2011.634002

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