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Conference Paper

D. Schlesinger, Jug, F., Myers, G., Rother, C., and Kainmueller, D., “Crowd sourcing image segmentation with iaSTAPLE”, in Proceedings - International Symposium on Biomedical Imaging, 2017, pp. 401–405.

J. Fehr, Reisert, M., and Burkhardt, H., “Cross-Correlation and Rotation Estimation of Local 3D Vector FieldPatches”, in Proceedings of the ISVC 2009, Part I, 2009, vol. 5875, pp. 287-296.

N. Sayed, Brattoli, B., and Ommer, B., “Cross and Learn: Cross-Modal Self-Supervision”, in German Conference on Pattern Recognition (GCPR) (Oral), Stuttgart, Germany, 2018.

Article (891.47 KB)

Oral slides (9.17 MB)

B. Jähne, Waas, S., and Klinke, J., “A critical theoretical review of optical techniques for short ocean wave measurements”, in Optics of the Air-Sea Interface: Theory and Measurements, 1992, vol. 1749, p. 204--215.

B. Güssefeld, Honauer, K., and Kondermann, D., “Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets”, in Advances in Visual Computing - 12th International Symposium, {ISVC} 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part {I}, 2016.

S. Vicente, Kolmogorov, V., and Rother, C., “Cosegmentation revisited: Models and optimization”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6312 LNCS, pp. 465–479.

C. Rother, Kolmogorov, V., Minka, T., and Blake, A., “Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 1, pp. 994–1000.

L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D., “Convexity shape constraints for image segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.

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.

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.

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.

F. Silvestri, Reinelt, G., and Schnörr, C., “A Convex Relaxation Approach to the Affine Subspace Clustering Problem”, in Proc.~GCPR, 2015.

Technical Report (878.63 KB)

J. Lellmann, Becker, F., and Schnörr, C., “Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers”, in IEEE International Conference on Computer Vision (ICCV), 2009, p. 646 -- 653.

Technical Report (930.18 KB)

J. Lellmann, Becker, F., and Schnörr, C., “Convex Optimization for Multi-Class Image Labeling with a Novel Family of Total Variation Based Regularizers”, in Proceedings of the IEEE Conference on Computer Vision (ICCV 09) Kyoto, Japan, 2009, pp. 646-653.

J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., Schnörr, C., Mórken, K., and Lysaker, M., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.

J. Lellmann, Kappes, J. H., Yuan, J., Becker, F., and Schnörr, C., “Convex Multi-Class Image Labeling by Simplex-Constrained Total Variation”, in Scale Space and Variational Methods in Computer Vision (SSVM 2009), 2009, vol. 5567, pp. 150-162.

Technical Report (1.75 MB)

J. Yuan, Steidl, G., and Schnörr, C., “Convex Hodge Decomposition of Image Flows”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 416--425.

Technical Report (290.72 KB)

M. Heiler and Schnörr, C., “Controlling Sparseness in Non-negative Tensor Factorization”, in Computer Vision -- ECCV 2006, 2006, vol. 3951, pp. 56-67.

Technical Report (568.86 KB)

J. Schlecht and Ommer, B., “Contour-based Object Detection”, in BMVC, 2011, p. 1--9.

Technical Report (2.62 MB)

F. Lauer, Bloch, G., and Vidal, R., “A Continuous Optimization Framework for Hybrid System Identification”, in submitted to Automatica, 2009.

K. Fundana, Heyden, A., Gosch, C., and Schnörr, C., “Continuous Graph Cuts for Prior-Based Object Segmentation”, in 19th Int.~Conf.~Patt.~Recog.~(ICPR), 2008, p. 1--4.

Technical Report (414.89 KB)

D. Kotovenko, Sanakoyeu, A., Lang, S., and Ommer, B., “Content and Style Disentanglement for Artistic Style Transfer”, in Proceedings of the Intl. Conf. on Computer Vision (ICCV), 2019.

M. Schiegg, Hanslovsky, P., Kausler, B. X., Hufnagel, L., and Hamprecht, F. A., “Conservation Tracking”, in ICCV 2013. Proceedings, 2013, p. 2928--2935.

Technical Report (5.22 MB)

F. Kluger, Brachmann, E., Ackermann, H., Rother, C., Yang, M. Ying, and Rosenhahn, B., “CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, in CVPR 2020, 2020.

PDF (9.95 MB)

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.

S. V. E. Keränen, 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., “Computational Analysis of Quantitative Changes in Gene Expression and Embryo Morphology between Species”, in Evolution-The Molecular Landscape, 2009.

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.

R. Dalitz, Petra, S., and Schnörr, C., “Compressed Motion Sensing”, in Proc. SSVM, 2017, vol. 10302.

F. Hering, Haußecker, H., Dieter, J., Netzsch, T., and Jähne, B., “A comprehensive study of algorithms for multidimensional flow field diagnostics”, in Proc. Optical 3D Measurement Techniques IV, Zurich, Sept. 29 - Oct. 2, 1997, 1997, p. 436--443.

B. Ommer and Buhmann, J. M., “A Compositionality Architecture for Perceptual Feature Grouping”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2003, vol. 2683, p. 275--290.

Technical Report (2.89 MB)

B. Ommer and Buhmann, J. M., “Compositional Object Recognition, Segmentation, and Tracking in Video”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2007, vol. 4679, p. 318--333.

Technical Report (2.78 MB)

D. van Halsema, Calkoen, C. J., Oost, W. A., Snoeij, P., and Jähne, B., “Comparisons of X-band Radar Backscatter Measurements with Area extended wave slop measurements made in a large Wind/Wave Tank”, in Proc. IGARSS'89, 1989, vol. 5, p. 2997--3001.

D. van Halsema, Calkoen, C. J., Oost, W. A., Snoeij, P., Vogelzang, J., and Jähne, B., “Comparisons of backscattering calculations with measurements made in a large wind/wave flume”, in Proc. IGARSS'92, 1992, vol. 2, p. 1451--1453.

A. Haja, Abraham, S., and Jähne, B., “A Comparison of Region Detectors for Tracking”, in Pattern Recognition, Proceedings 30th DAGM Symposium, Munich, Germany, June 2008, 2008, vol. 5096, p. 112--121.

P. Snoeij, van Halsema, D., Oost, W. A., Calkoen, C. J., Vogelzang, J., and Jähne, B., “Comparison of microwave backscatter measurements and small-scale surface wave measurements made from the Dutch ocean research tower 'Noordwijk'”, in Proceedings IGARSS '91, 1991, vol. 3, p. 1289--1292.

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