Publications

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Conference Paper
Keuchel, J, Schellewald, C, Cremers, D and Schnörr, C (2001). Convex Relaxations for Binary Image Partitioning and Perceptual Grouping. Mustererkennung 2001. Springer. 2191 353--360
Silvestri, F, Reinelt, G and Schnörr, C (2015). A Convex Relaxation Approach to the Affine Subspace Clustering Problem. Proc.~GCPRPDF icon Technical Report (878.63 KB)
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan. 646-653
Lellmann, J, Becker, F and Schnörr, C (2009). Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers. IEEE International Conference on Computer Vision (ICCV). 646 -- 653PDF icon Technical Report (930.18 KB)
Lellmann, J, Kappes, J H, Yuan, J, Becker, F, Schnörr, C, Mórken, K and Lysaker, M (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162
Lellmann, J, Kappes, J H, Yuan, J, Becker, F and Schnörr, C (2009). Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation. Scale Space and Variational Methods in Computer Vision (SSVM 2009). Springer. 5567 150-162PDF icon Technical Report (1.75 MB)
Yuan, J, Steidl, G and Schnörr, C (2008). Convex Hodge Decomposition of Image Flows. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 416--425PDF icon Technical Report (290.72 KB)
Heiler, M and Schnörr, C (2006). Controlling Sparseness in Non-negative Tensor Factorization. Computer Vision -- ECCV 2006. Springer. 3951 56-67PDF icon Technical Report (568.86 KB)
Fundana, K, Heyden, A, Gosch, C and Schnörr, C (2008). Continuous Graph Cuts for Prior-Based Object Segmentation. 19th Int.~Conf.~Patt.~Recog.~(ICPR). 1--4PDF icon Technical Report (414.89 KB)
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)
Schnörr, (1990). Computation of Discontinuous Optical Flow by Domain Decomposition and Shape Optimization. Proc. British Machine Vision Conference. 109--114
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Sungwoong, K, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. CVPR 2013. ProceedingsPDF icon Technical Report (1.35 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem. CVPRPDF icon Technical Report (1.35 MB)
Bruhn, A, Weickert, J and Schnörr, C (2002). Combining the Advantages of Local and Global Optic Flow Methods. Pattern Recognition, Proc.~24th DAGM Symposium. Springer. 2449 454--462
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)
Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPRPDF icon Technical Report (430.63 KB)
Kappes, J H, Savchynskyy, B and Schnörr, C (2012). A Bundle Approach To Efficient MAP-Inference by Lagrangian Relaxation. CVPR. Proceedings. 1688-1695
Heikkonen, J, Koikkalainen, P and Schnörr, C (1994). Building Trajectories via Selforganization from Spatiotemporal Features. 12th Int. Conf. on Pattern Recognition
Petra, S, Schnörr, C, Becker, F and Lenzen, F (2013). B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision SSVM. 110-124
Petra, S, Schnörr, C, Becker, F and Lenzen, F (2013). B-SMART: Bregman-Based First-Order Algorithms for Non-Negative Compressed Sensing Problems. Proceedings of the 4th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM) 2013. Springer. 7893 110-124PDF icon Technical Report (1.15 MB)
Weber, S, Schüle, T, Schnörr, C and Kuba, A (2006). Binary Tomography with Deblurring. Combinatorial Image Analysis. Springer. 4040 375-388PDF icon Technical Report (803.63 KB)
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
Schnörr, (1994). Bewegungssegmentation von Bildfolgen durch die Minimierung konvexer nicht-quadratischer Funktionale. Mustererkennung 1994. Technische Universität Wien. 5 178--185
Weber, S, Nagy, A, Schüle, T, Schnörr, C and Kuba, A (2006). A Benchmark Evaluation of Large-Scale Optimization Approaches to Binary Tomography. Discrete Geometry for Computer Imagery (DGCI 2006). Springer. 4245 146-156PDF icon Technical Report (301.1 KB)
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
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. 254 44--51
Schnörr, C, Niemann, H and Kopecz, J (1993). Architekturkonzepte zur Bildauswertung. Grundlagen und Anwendungen der Künstlichen Intelligenz, 17. Fachtagung für Künstliche Intelligenz. Springer-Verlag. 268--274
Bodnariuc, E, Gurung, A, Petra, S and Schnörr, C (2015). Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV. Proc.~EMMCVPR. Springer. 8932 378--391PDF icon Technical Report (951.37 KB)
Bodnariuc, E, Gurung, A, Petra, S and Schnörr, C (2015). Adaptive Dictionary-Based Spatio-temporal Flow Estimation for Echo PIV. EMMCVPR
Kondermann, C, Kondermann, D, Jähne, B, Garbe, C S, Schnörr, C and Jähne, B (2007). An adaptive confidence measure for optical flows based on linear subspace projections. Proceedings of the 29th DAGM Symposium on Pattern Recognition. Springer. 4713 132--141
Biesdorf, A, Wörz, S, von Tengg-Kobligk, H, Rohr, K and Schnörr, C (2015). 3D Segmentation of Vessels by Incremental Implicit Polynomial Fitting and Convex Optimization. Proc.~ISBIPDF icon Technical Report (611.33 KB)

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