Publications

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Author Title [ Type(Desc)] Year
Conference Paper
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)
Kaster, F O, Weber, M - A and Hamprecht, F A (2011). Comparative Validation of Graphical Models for Learning Tumor Segmentations from Noisy Manual Annotations. LNCS. Springer, Heidelberg. LNCS 6533 74-85PDF icon Technical Report (544.56 KB)
Kolmogorov, V and Rother, C (2006). Comparison of energy minimization algorithms for highly connected graphs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3952 LNCS 1–15
Kolmogorov, V and Rother, C (2006). Comparison of energy minimization algorithms for highly connected graphs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3952 LNCS 1–15
Snoeij, P, van Halsema, D, Oost, W A, Calkoen, C J, Vogelzang, J and Jähne, B (1991). Comparison of microwave backscatter measurements and small-scale surface wave measurements made from the Dutch ocean research tower 'Noordwijk'. Proceedings IGARSS '91. 3 1289--1292
Haja, A, Abraham, S and Jähne, B (2008). A Comparison of Region Detectors for Tracking. Pattern Recognition, Proceedings 30th DAGM Symposium, Munich, Germany, June 2008. 5096 112--121
van Halsema, D, Calkoen, C J, Oost, W A, Snoeij, P, Vogelzang, J and Jähne, B (1992). Comparisons of backscattering calculations with measurements made in a large wind/wave flume. Proc. IGARSS'92. 2 1451--1453
van Halsema, D, Calkoen, C J, Oost, W A, Snoeij, P and Jähne, B (1989). Comparisons of X-band Radar Backscatter Measurements with Area extended wave slop measurements made in a large Wind/Wave Tank. Proc. IGARSS'89. 5 2997--3001
Ommer, B and Buhmann, J M (2007). Compositional Object Recognition, Segmentation, and Tracking in Video. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 4679 318--333PDF icon Technical Report (2.78 MB)
Ommer, B and Buhmann, J M (2003). A Compositionality Architecture for Perceptual Feature Grouping. Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. Springer. 2683 275--290PDF icon Technical Report (2.89 MB)
Hering, F, Haußecker, H, Dieter, J, Netzsch, T and Jähne, B (1997). A comprehensive study of algorithms for multidimensional flow field diagnostics. Proc. Optical 3D Measurement Techniques IV, Zurich, Sept. 29 - Oct. 2, 1997. Wichmann. 436--443
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
Keränen, S V E, DePace, A, Hendriks, C L Luengo, Fowlkes, C, Arbelaez, P, Ommer, B, Brox, T, Henriquez, C, Wunderlich, Z, Eckenrode, K, Fischer, B, Hammonds, A and Celniker, S E (2009). Computational Analysis of Quantitative Changes in Gene Expression and Embryo Morphology between Species. Evolution-The Molecular Landscape
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
Kluger, F, Brachmann, E, Ackermann, H, Rother, C, Yang, M Ying and Rosenhahn, B (2020). CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. CVPR 2020. http://arxiv.org/abs/2001.02643PDF icon PDF (9.95 MB)
Schiegg, M, Hanslovsky, P, Kausler, B X, Hufnagel, L and Hamprecht, F A (2013). Conservation Tracking. ICCV 2013. Proceedings. 2928--2935PDF icon Technical Report (5.22 MB)
Kotovenko, D, Sanakoyeu, A, Lang, S and Ommer, B (2019). Content and Style Disentanglement for Artistic Style Transfer. Proceedings of the Intl. Conf. on Computer Vision (ICCV)
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)
Lauer, F, Bloch, G and Vidal, R (2009). A Continuous Optimization Framework for Hybrid System Identification. submitted to Automatica
Schlecht, J and Ommer, B (2011). Contour-based Object Detection. BMVC. 1--9PDF icon Technical Report (2.62 MB)
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)
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)
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)
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, 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
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)
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
Royer, L A, Richmond, D L, Rother, C, Andres, B and Kainmueller, D (2016). Convexity shape constraints for image segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 402–410. http://arxiv.org/abs/1509.02122
Rother, C, Kolmogorov, V, Minka, T and Blake, A (2006). Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1 994–1000. http://research.microsoft.com/vision/cambridge/
Vicente, S, Kolmogorov, V and Rother, C (2010). Cosegmentation revisited: Models and optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6312 LNCS 465–479

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