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Conference Paper
C. Schnörr, Niemann, H., and Kopecz, J., “Architekturkonzepte zur Bildauswertung”, in Grundlagen und Anwendungen der Künstlichen Intelligenz, 17. Fachtagung für Künstliche Intelligenz, Berlin, 1993, pp. 268–274.
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.
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.
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.
C. Schnörr, “Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale”, in Mustererkennung 1994, 1994, vol. 5, pp. 178–185.
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.
J. Heikkonen, Koikkalainen, P., and Schnörr, C., “Building Trajectories via Selforganization from Spatiotemporal Features”, in 12th Int. Conf. on Pattern Recognition, Jerusalem, Israel, 1994.
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.
R. Dalitz, Petra, S., and Schnörr, C., “Compressed Motion Sensing”, in Proc. SSVM, 2017, vol. 10302.
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.
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.
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.
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.
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.
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.
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.
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.
F. Aström and Schnörr, C., “Double-Opponent Vectorial Total Variation”, in Proc. ECCV, 2016.
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.
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.
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. 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.
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.
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.
F. Aström, Petra, S., Schmitzer, B., and Schnörr, C., “A Geometric Approach to Image Labeling”, in Proc. ECCV, 2016.
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.
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.
M. Zisler, Savarino, F., Petra, S., and Schnörr, C., “Gradient Flows on a Riemannian Submanifold for Discrete Tomography”, in Proc. GCPR, 2017.
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.
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.
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.
M. Zisler, Aström, F., Petra, S., and Schnörr, C., “Image Reconstruction by Multilabel Propagation”, in Proc. SSVM, 2017, vol. 10302.
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.

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