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Conference Paper
E. Bodnariuc, Petra, S., Schnörr, C., and Voorneveld, J., “A Local Spatio-Temporal Approach to Plane Wave Ultrasound Particle Image Velocimetry”, in Proc. GCPR, 2017.
S. Weber, Schnörr, C., and Hornegger, J., “A Linear Programming Relaxation for Binary Tomography with Smoothness Priors”, in Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'03), Palermo, Italy, 2003.
S. Weber, Schüle, T., Schnörr, C., and Hornegger, J., “A Linear Programming Approach to Limited Angle 3D Reconstruction from DSA Projections”, in Bildverarbeitung für die Medizin 2003, 2003, pp. 41–45.
M. Heiler and Schnörr, C., “Learning Sparse Image Codes by Convex Programming”, in Proc. Tenth IEEE Int. Conf. Computer Vision (ICCV'05), Beijing, China, 2005, pp. 1667-1674.
R. Hühnerbein, Savarino, F., Petra, S., and Schnörr, C., “Learning Adaptive Regularization for Image Labeling Using Geometric Assignment”, in Proc. SSVM, 2019.
J. Heers, Schnörr, C., and Stiehl, H. –S., “Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation”, in Proc. IEEE Int. Conf. Image Proc., Chicago, 1998.
M. Zisler, Aström, F., Petra, S., and Schnörr, C., “Image Reconstruction by Multilabel Propagation”, in Proc. SSVM, 2017, vol. 10302.
C. Schellewald, Keuchel, J., and Schnörr, C., “Image labeling and grouping by minimizing linear functionals over cones”, in Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'01), INRIA, Sophia Antipolis, France, 2001, vol. 2134, pp. 267–282.
J. Keuchel, Heiler, M., and Schnörr, C., “Hierarchical Image Segmentation based on Semidefinite Programming”, in Pattern Recognition, Proc. 26th DAGM Symposium, 2004, vol. 3175, pp. 120-128.
R. Karim, Bergtholdt, M., Kappes, J. H., and Schnörr, C., “Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification”, in Pattern Recognition – 29th DAGM Symposium, 2007, vol. 4713, pp. 395-404.
M. Zisler, Savarino, F., Petra, S., and Schnörr, C., “Gradient Flows on a Riemannian Submanifold for Discrete Tomography”, in Proc. GCPR, 2017.
C. Schnörr, Stiehl, H. - S., and Grigat, R. - R., “On Globally Asymptotically Stable Continuous-Time CNNs for Adaptive Smoothing of Multidimensional Signals”, in Proc. 4th IEEE Int. Workshop on Cellular Neural Networks and their Applications, Seville, Spain, 1996.
A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in EMMCVPR, 2018, vol. 10746, pp. 533–547.
A. Zern, Rohr, K., and Schnörr, C., “Geometric Image Labeling with Global Convex Labeling Constraints”, in Proc. EMMCVPR, 2017.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “A Geometric Approach to Image Labeling”, in Proc. ECCV, 2016.
J. Neumann, Schnörr, C., and Steidl, G., “Feasible Adaption Criteria for Hybrid Wavelet – Large Margin Classifiers”, in Proc. Computer Analysis of Images and Patterns (CAIP'03), 2003, vol. 2756, pp. 588–595.
J. Lellmann, Breitenreicher, D., and Schnörr, C., “Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision”, in European Conference on Computer Vision (ECCV), 2010, vol. 6312, pp. 494–505.
C. Schellewald, Roth, S., and Schnörr, C., “Evaluation of Convex Optimization Techniques for the Weighted Graph–Matching Problem in Computer Vision”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 361–368.
S. Petra, Popa, C., and Schnörr, C., “Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods”, in Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08), Constanta, Romania, 2008.
S. Petra, Popa, C., and Schnörr, C., “Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods”, in Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08), Bucharest, Romania, 2008.
C. Schnörr and Neumann, B., “Ein Ansatz zur effizienten und eindeutigen Rekonstruktion stückweise glatter Funktionen”, in Mustererkennung 1992, 14. DAGM-Symposium, Dresden, 1992, pp. 411–416.
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 Parallel Variational Optical Flow Computation”, in Pattern Recognition, Proc. 25th DAGM Symposium, 2003, vol. 2781, pp. 196–203.
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.
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. 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.
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”, 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. 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.
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.

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