Publications

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Conference Paper
Becker, F and Schnörr, C (2008). Decomposition of Quadratric Variational Problems. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 325--334PDF icon Technical Report (1.29 MB)
Bruhn, A, Jakob, T, Fischer, M, Kohlberger, T, Weickert, J, Brüning, U and Schnörr, C (2002). Designing 3--D Nonlinear Diffusion Filters for High Performance Cluster Computing. Pattern Recognition, Proc.~24th DAGM Symposium. Springer. 2449 290--297
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
Fornland, P and Schnörr, C (1997). Determining the Dominant Plane from Uncalibrated Stereo Vision by a Robust and Convergent Iterative Approach without Correspondence. Proc.~Int.~Conf.~Comp.~Vision and Patt.~Recog.~(CVPR'97)
Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Diffusion Snakes Using Statistical Shape Knowledge. Proc.~Algebraic Frames for the Perception-Action Cycle. Springer. 1888 164--174
Cremers, D, Schnörr, C and Weickert, J (2001). Diffusion--Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework. IEEE First Workshop on Variational and Level Set Methods in Computer Vision. IEEE Comp.~Soc. 237--244
Bruhn, A, Weickert, J, Kohlberger, T and Schnörr, C (2005). Discontinuity-Preserving Computation of Variational Optic Flow in Real-Time. Scale-Space 2005. Springer. 3459 279--290
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
Stahl, A, Ruhnau, P and Schnörr, C (2006). A Distributed Parameter Approach to Dynamic Image Motion. ECCV 2006, International Workshop on The Representation and Use of Prior Knowledge in Vision. LNCS, SpringerPDF icon Technical Report (1.24 MB)
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
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
Savchynskyy, B, Schmidt, S, Kappes, J H and Schnörr, C (2012). Efficient MRF Energy Minimization via Adaptive Diminishing Smoothing. UAI 2012PDF icon Technical Report (529 KB)
Schnörr, C and Neumann, B (1992). Ein Ansatz zur effizienten und eindeutigen Rekonstruktion stückweise glatter Funktionen. Mustererkennung 1992, 14. DAGM-Symposium. Springer-Verlag. 411--416
Andres, B, Kappes, J H, Köthe, U, Schnörr, C and Hamprecht, F A (2010). An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. Pattern Recognition, Proc.~32th DAGM Symposium. 353-362
Andres, B, Kappes, J H, Köthe, U, Schnörr, C and Hamprecht, F A (2010). An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. Pattern Recognition, Proc.~32th DAGM SymposiumPDF icon Technical Report (218.43 KB)
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)
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
Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography. Discrete Geometry for Computer Imagery (DGCI) 2014. Springer. 262--274PDF icon Technical Report (894.83 KB)
Neufeld, A, Berger, J, Becker, F, Lenzen, F and Schnörr, C (2015). Estimating Vehicle Ego-Motion and Piecewise Planar Scene Structure from Optical Flow in a Continuous Framework. 37th German Conference on Pattern Recognition. Springer, Aachen
Schmidt, S, Savchynskyy, B, Kappes, J H and Schnörr, C (2011). Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. EMMCVPR 2011. Springer. 6819 89-103
Schmidt, S, Savchynskyy, B, Kappes, J H and Schnörr, C (2011). Evaluation of a First-Order Primal-Dual Algorithm for MRF Energy Minimization. EMMCVPR. Springer. 6819 89-103PDF icon Technical Report (684.13 KB)
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. 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--505PDF icon Technical Report (1.94 MB)
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
Garbe, C S, Schnörr, C and Jähne, B (2007). Fluid flow estimation through integration of physical flow configurations. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 92--101
Savchynskyy, B, Kappes, J H, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS. Proceedings. 1950-1958
Savchynskyy, B, Kappes, J H, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPSPDF icon Technical Report (499.17 KB)
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
Kappes, J H, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. SpringerPDF icon Technical Report (7.47 MB)
Kappes, J H, Speth, M, Andres, B, Reinelt, G and Schnörr, C (2011). Globally Optimal Image Partitioning by Multicuts. EMMCVPR. Springer. 31-44PDF icon Technical Report (7.3 MB)
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 395-404PDF icon Technical Report (491.56 KB)
Schmitzer, B and Schnörr, C (2013). A Hierarchical Approach to Optimal Transport. Scale Space and Variational Methods (SSVM 2013). 452-464
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. 2134 267--282
Breitenreicher, D and Schnörr, C (2009). Intrinsic Second-Order Geometric Optimization for Robust Point Set Registration Without Correspondence. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2009). Springer. 5681 274-287. http://www.springerlink.com/content/1470n7577713069q/PDF icon Technical Report (752.29 KB)

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