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Conference Paper
Schnörr, C, Niemann, H and Kopecz, J (1993). Architekturkonzepte zur Bildauswertung. Grundlagen und Anwendungen der Künstlichen Intelligenz, 17. Fachtagung für Künstliche Intelligenz. Springer-Verlag, Berlin. 268–274
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2016). The Assignment Manifold: A Smooth Model for Image Labeling. Proc. 2nd Int. Workshop on Differential Geometry in Computer Vision and Machine Learning (DIFF-CVML'16; oral presentation; Grenander best paper award)
Bister, D, Rohr, K and Schnörr, C (1990). Automatische Bestimmung der Trajektorien von sich bewegenden Objekten aus einer Grauwertbildfolge. Mustererkennung 1990, 12. DAGM-Symposium. Springer-Verlag, Oberkochen-Aalen. 254 44–51
Giebel, J, Gavrila, D M and Schnörr, C (2004). A Bayesian Framework for Multi-cue 3D Object Tracking. Computer Vision – ECCV 2004. Springer. 3024 241-252
Schnörr, (1994). Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale. Mustererkennung 1994. Technische Universität Wien. 5 178–185
Weber, S, Schüle, T, Hornegger, J and Schnörr, C (2004). Binary Tomography by Iterating Linear Programs from Noisy Projections. Combinatorial Image Analysis, Proc. Int. Workshop on Combinatorial Image Analysis (IWCIA'04). Springer Verlag. 3322 38–51
Heikkonen, J, Koikkalainen, P and Schnörr, C (1994). Building Trajectories via Selforganization from Spatiotemporal Features. 12th Int. Conf. on Pattern Recognition. Jerusalem, Israel
Heers, J, Schnörr, C and Stiehl, H S (1998). A class of parallel algorithms for nonlinear variational image segmentation. Proc. Noblesse Workshop on Non–Linear Model Based Image Analysis (NMBIA'98). Glasgow, Scotland
Dalitz, R, Petra, S and Schnörr, C (2017). Compressed Motion Sensing. Proc. SSVM. Springer. 10302
Schnörr, (1990). Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization. Proc. British Machine Vision Conference. Oxford/UK. 109–114
Wulf, M, Stiehl, H S and Schnörr, C (2000). On the computational rôle of the primate retina. Proc. 2nd ICSC Symposium on Neural Computation (NC 2000). Berlin, Germany
Keuchel, J, Schellewald, C, Cremers, D and Schnörr, C (2001). Convex Relaxations for Binary Image Partitioning and Perceptual Grouping. Mustererkennung 2001. Springer, Munich, Germany. 2191 353–360
Yuan, J, Schnörr, C, Kohlberger, T and Ruhnau, P (2004). Convex Set-Based Estimation of Image Flows. ICPR 2004 – 17th Int. Conf. on Pattern Recognition. IEEE, Cambridge, UK. 1 124-127
Schnörr, (1996). Convex Variational Segmentation of Multi-Channel Images. Proc. 12th Int. Conf. on Analysis and Optimization of Systems: Images, Wavelets and PDE's. Springer-Verlag, Paris. 219
Sprengel, R, Schnörr, C and Neumann, B (1994). Detection of Visual Data Transitions in Nonlinear Parameter Space. Mustererkennung 1994. Technische Universität Wien. 5 315–323
Yuan, J, Ruhnau, P, Mémin, E and Schnörr, C (2005). Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation. Scale-Space 2005. Springer. 3459 267–278
Kohlberger, T, Schnörr, C, Bruhn, A and Weickert, J (2003). Domain Decomposition for Parallel Variational Optical Flow Computation. Pattern Recognition, Proc. 25th DAGM Symposium. Springer. 2781 196–203
Aström, F and Schnörr, C (2016). Double-Opponent Vectorial Total Variation. Proc. ECCV
Wiehler, K, Grigat, R –R, Heers, J, Schnörr, C and Stiehl, H S (1998). Dynamic Circular Cellular Networks for Adaptive Smoothing of Multi–Dimensional Signals. Proc. 5th IEEE Int. Workshop on Cellular Neural Networks and their Applications. London
Schnörr, C and Neumann, B (1992). Ein Ansatz zur effizienten und eindeutigen Rekonstruktion stückweise glatter Funktionen. Mustererkennung 1992, 14. DAGM-Symposium. Springer-Verlag, Dresden. 411–416
Petra, S, Popa, C and Schnörr, C (2008). Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods. Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08). Ed Acad Romane, Bucuresti, Constanta, Romania
Petra, S, Popa, C and Schnörr, C (2008). Enhancing Sparsity by Constraining Strategies: Constrained SIRT versus Spectral Projected Gradient Methods. Proc. 7th Workshop on Modelling of Environmental and Life Sciences Problems (WMM 08). Bucharest, Romania
Schellewald, C, Roth, S and Schnörr, C (2001). Evaluation of Convex Optimization Techniques for the Weighted Graph–Matching Problem in Computer Vision. Mustererkennung 2001. Springer, Munich, Germany. 2191 361–368
Lellmann, J, Breitenreicher, D and Schnörr, C (2010). Fast and Exact Primal-Dual Iterations for Variational Problems in Computer Vision. European Conference on Computer Vision (ECCV). Springer Berlin / Heidelberg. 6312 494–505
Neumann, J, Schnörr, C and Steidl, G (2003). Feasible Adaption Criteria for Hybrid Wavelet – Large Margin Classifiers. Proc. Computer Analysis of Images and Patterns (CAIP'03). Springer. 2756 588–595
Aström, F, Petra, S, Schmitzer, B and Schnörr, C (2016). A Geometric Approach to Image Labeling. Proc. ECCV
Zern, A, Rohr, K and Schnörr, C (2018). Geometric Image Labeling with Global Convex Labeling Constraints. EMMCVPR. 10746 533–547
Zern, A, Rohr, K and Schnörr, C (2017). Geometric Image Labeling with Global Convex Labeling Constraints. Proc. EMMCVPR
Schnörr, C, Stiehl, H - S and Grigat, R - R (1996). On Globally Asymptotically Stable Continuous-Time CNNs for Adaptive Smoothing of Multidimensional Signals. Proc. 4th IEEE Int. Workshop on Cellular Neural Networks and their Applications. Seville, Spain
Zisler, M, Savarino, F, Petra, S and Schnörr, C (2017). Gradient Flows on a Riemannian Submanifold for Discrete Tomography. Proc. GCPR
Karim, R, Bergtholdt, M, Kappes, J H and Schnörr, C (2007). Greedy-Based Design of Sparse Two-Stage SVMs for Fast Classification. Pattern Recognition – 29th DAGM Symposium. Springer. 4713 395-404
Keuchel, J, Heiler, M and Schnörr, C (2004). Hierarchical Image Segmentation based on Semidefinite Programming. Pattern Recognition, Proc. 26th DAGM Symposium. Springer. 3175 120-128
Schellewald, C, Keuchel, J and Schnörr, C (2001). Image labeling and grouping by minimizing linear functionals over cones. Proc. Third Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR'01). Springer, INRIA, Sophia Antipolis, France. 2134 267–282
Zisler, M, Aström, F, Petra, S and Schnörr, C (2017). Image Reconstruction by Multilabel Propagation. Proc. SSVM. Springer. 10302
Heers, J, Schnörr, C and Stiehl, H –S (1998). Investigation of Parallel and Globally Convergent Iterative Schemes for Nonlinear Variational Image Smoothing and Segmentation. Proc. IEEE Int. Conf. Image Proc. Chicago

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