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Publications

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Journal Article
C. Schellewald, Roth, S., and Schnörr, C., “Evaluation of a convex relaxation to a quadratic assignment matching approach for relational object views”, Image Vision Comp., vol. 25, pp. 1301–1314, 2007.
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, Comp. Statistics & Data Analysis, vol. 140, pp. 41–58, 2019.
F. Rathke and Schnörr, C., “Fast Multivariate Log-Concave Density Estimation”, preprint: arXiv, 2018.
J. Weickert, Heers, J., Schnörr, C., Zuiderveld, K. –J., Scherzer, O., and Stiehl, H. –S., “Fast parallel algorithms for a broad class of nonlinear variational diffusion approaches”, Real–Time Imaging, vol. 7, pp. 31–45, 2001.
C. Schnörr, “On Functionals with Greyvalue-Controlled Smoothness Terms for Determining Optical Flow”, pami, vol. 15, pp. 1074–1079, 1993.
A. Nicola, Petra, S., Popa, C., and Schnörr, C., “A general extending and constraining procedure for linear iterative methods”, Int. J. Comp. Math., 2011.
F. Aström and Schnörr, C., “A Geometric Approach for Color Image Regularization”, Comp. Vision Image Understanding, vol. 165, pp. 43–59, 2017.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, vol. 36, p. 034004 (33pp), 2020.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, Inverse Problems, 2019.
A. Zeilmann, Savarino, F., Petra, S., and Schnörr, C., “Geometric Numerical Integration of the Assignment Flow”, preprint: arXiv, 2018.
B. Schmitzer and Schnörr, C., “Globally Optimal Joint Image Segmentation and Shape Matching based on Wasserstein Modes”, J. Math. Imag. Vision, vol. 52, pp. 436–458, 2015.
L. Kostrykin, Schnörr, C., and Rohr, K., “Globally Optimal Segmentation of Cell Nuclei in Fluoroscence Microscopy Images using Shape and Intensity Information”, Medical Image Analysis, 2019.
J. Heers, Schnörr, C., and Stiehl, H. S., “Globally–Convergent Iterative Numerical Schemes for Non–Linear Variational Image Smoothing and Segmentation on a Multi–Processor Machine”, IEEE Trans. Image Proc., vol. 10, pp. 852–864, 2001.
J. Kappes, Speth, M., Reinelt, G., and Schnörr, C., “Higher-order Segmentation via Multicuts”, Comp. Vision Image Understanding, vol. 143, pp. 104–119, 2016.
R. Hühnerbein, Savarino, F., Aström, F., and Schnörr, C., “Image Labeling Based on Graphical Models Using Wasserstein Messages and Geometric Assignment”, SIAM J. Imaging Science, vol. 11, pp. 1317–1362, 2018.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “Image Labeling by Assignment”, J. Math. Imag. Vision, vol. 58, pp. 211–238, 2017.
Y. Censor, Gibali, A., Lenzen, F., and Schnörr, C., “The Implicit Convex Feasibility Problem and Its Application to Adaptive Image Denoising”, J. Comp. Math., vol. 34, pp. 608-623, 2016.
R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, preprint: arXiv, 2019.
M. Heiler and Schnörr, C., “Learning Sparse Representations by Non-Negative Matrix Factorization and Sequential Cone Programming”, J. Mach. Learning Res., vol. 7, pp. 1385–1407, 2006.
W. Peckar, Schnörr, C., Rohr, K., Stiehl, H. –S., and Spetzger, U., “Linear and Incremental Estimation of Elastic Deformations in Medical Registration Using Prescribed Displacements”, Machine Graphics & Vision, vol. 7, pp. 807–829, 1998.
S. Weber, Schüle, T., Schnörr, C., and Hornegger, J., “A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections”, Methods of Information in Medicine, vol. 43, pp. 320–326, 2004.
M. Welk, Weickert, J., Becker, F., Schnörr, C., Feddern, C., and Burgeth, B., “Median and related local filters for tensor-valued images”, Signal Processing, vol. 87, pp. 291-308, 2007.
J. H. Kappes, Swoboda, P., Savchynskyy, B., Hazan, T., and Schnörr, C., “Multicuts and Perturb & MAP for Probabilistic Graph Clustering”, J. Math. Imag. Vision, vol. 56, pp. 221–237, 2016.
M. Heiler and Schnörr, C., “Natural Image Statistics for Natural Image Segmentation”, Int. J. Comp. Vision, vol. 63, pp. 5–19, 2005.
C. Schnörr and Sprengel, R., “A Nonlinear Regularization Approach to Early Vision”, Biol. Cybernetics, vol. 72, pp. 141–149, 1994.
P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp. in Fluids, vol. 42, pp. 61–78, 2007.
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.
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.
J. Weickert and Schnörr, C., “PDE–Based Preprocessing of Medical Images”, Künstliche Intelligenz, vol. 3, pp. 5–10, 2000.
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.
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.
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.
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. 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.
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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