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M. Heiler, Cremers, D., and Schnörr, C., “Efficient Feature Subset Selection for Support Vector Machines”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 21/2001, 2001.

J. Neumann, Schnörr, C., and Steidl, G., “Effectively Finding the Optimal Wavelet for Hybrid Wavelet - Large Margin Signal Classification”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 5, 2003.

D

K. Wiehler, Grigat, R. –R., Heers, J., Schnörr, C., and Stiehl, H. S., “Dynamic Circular Cellular Networks for Adaptive Smoothing of Multi–Dimensional Signals”, in Proc. 5th IEEE Int. Workshop on Cellular Neural Networks and their Applications, London, 1998.

F. Aström and Schnörr, C., “Double-Opponent Vectorial Total Variation”, in Proc. ECCV, 2016.

T. Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., “Domain decomposition for variational optical flow computation”, IEEE Trans. Image Proc., vol. 14, pp. 1125-1137, 2005.

T. Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., “Domain Decomposition for Variational Optical Flow Computation”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 07/2003, 2003.

T. Kohlberger, Schnörr, C., Bruhn, A., and Weickert, J., “Domain Decomposition for Parallel Variational Optical Flow Computation”, in Pattern Recognition, Proc. 25th DAGM Symposium, 2003, vol. 2781, pp. 196–203.

T. Schüle, Schnörr, C., Weber, S., and Hornegger, J., “Discrete Tomography By Convex-Concave Regularization and D.C. Programming”, Discr. Appl. Math., vol. 151, pp. 229-243, 2005.

T. Schüle, Schnörr, C., Weber, S., and Hornegger, J., “Discrete Tomography By Convex-Concave Regularization and D.C. Programming”, Dept. Math. and Comp. Science, University of Mannheim, Germany, 15, 2003.

J. Yuan, Ruhnau, P., Mémin, E., and Schnörr, C., “Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation”, in Scale-Space 2005, 2005, vol. 3459, pp. 267–278.

C. Schnörr, “Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class”, ijcv, vol. 6, pp. 25–38, 1991.

R. Sprengel, Schnörr, C., and Neumann, B., “Detection of Visual Data Transitions in Nonlinear Parameter Space”, in Mustererkennung 1994, 1994, vol. 5, pp. 315–323.

C

S. Petra, Schnörr, C., and Schröder, A., “Critical Parameter Values and Reconstruction Properties of Discrete Tomography: Application to Experimental Fluid Dynamics”. 2012.

C. Schnörr, “Convex Variational Segmentation of Multi-Channel Images”, in Proc. 12th Int. Conf. on Analysis and Optimization of Systems: Images, Wavelets and PDE's, Paris, 1996, vol. 219.

J. Yuan, Schnörr, C., Kohlberger, T., and Ruhnau, P., “Convex Set-Based Estimation of Image Flows”, in ICPR 2004 – 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, vol. 1, pp. 124-127.

J. Keuchel, Schellewald, C., Cremers, D., and Schnörr, C., “Convex Relaxations for Binary Image Partitioning and Perceptual Grouping”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 353–360.

F. Savarino and Schnörr, C., “Continuous-Domain Assignment Flows”, preprint: arXiv, 2019.

J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, CoRR, vol. abs/1102.5448, 2011.

J. Lellmann and Schnörr, C., “Continuous Multiclass Labeling Approaches and Algorithms”, Univ. of Heidelberg, 2010.

M. Wulf, Stiehl, H. S., and Schnörr, C., “On the computational rôle of the primate retina”, in Proc. 2nd ICSC Symposium on Neural Computation (NC 2000), Berlin, Germany, 2000.

C. Schnörr, “Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization”, ijcv, vol. 8, pp. 153–165, 1992.

C. Schnörr, “Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization”, in Proc. British Machine Vision Conference, Oxford/UK, 1990, pp. 109–114.

R. Dalitz, Petra, S., and Schnörr, C., “Compressed Motion Sensing”, in Proc. SSVM, 2017, vol. 10302.

J. Neumann, Schnörr, C., and Steidl, G., “Combined SVM-based Feature Selection and Classification”, Machine Learning, vol. 61, pp. 129-150, 2005.

J. Heers, Schnörr, C., and Stiehl, H. S., “A class of parallel algorithms for nonlinear variational image segmentation”, in Proc. Noblesse Workshop on Non–Linear Model Based Image Analysis (NMBIA'98), Glasgow, Scotland, 1998.

B

J. Heikkonen, Koikkalainen, P., and Schnörr, C., “Building Trajectories via Selforganization from Spatiotemporal Features”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.

S. Weber, Schüle, T., Hornegger, J., and Schnörr, C., “Binary Tomography by Iterating Linear Programs from Noisy Projections”, in Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'04), 2004, vol. 3322, pp. 38–51.

S. Weber, Schnörr, C., Schüle, T., and Hornegger, J., “Binary Tomography by Iterating Linear Programs”, Geometric Properties from Incomplete Data. Springer, 2005.

J. Keuchel, Schnörr, C., Schellewald, C., and Cremers, D., “Binary Partitioning, Perceptual Grouping, and Restoration with Semidefinite Programming”, vol. 25, pp. 1364–1379, 2003.

C. Schnörr, “Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale”, in Mustererkennung 1994, 1994, vol. 5, pp. 178–185.

J. Giebel, Gavrila, D. M., and Schnörr, C., “A Bayesian Framework for Multi-cue 3D Object Tracking”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 241-252.

A

D. Bister, Rohr, K., and Schnörr, C., “Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge”, in Mustererkennung 1990, 12. DAGM-Symposium, Oberkochen-Aalen, 1990, vol. 254, pp. 44–51.

F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “The Assignment Manifold: A Smooth Model for Image Labeling”, in Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award), 2016.

A. Zern, Zeilmann, A., and Schnörr, C., “Assignment Flows for Data Labeling on Graphs: Convergence and Stability”, preprint: arXiv, 2020.

C. Schnörr, “Assignment Flows”, Handbook of Variational Methods for Nonlinear Geometric Data. Springer, p. 235—260, 2020.

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