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Conference Paper

E. Brachmann and Rother, C., “Neural-guided RANSAC: Learning where to sample model hypotheses”, in Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 4321–4330.

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D. Singaraju, Rother, C., and Rhemann, C., “New appearance models for natural image matting”, 2010, pp. 659–666.

D. Singaraju, Rother, C., and Rhemann, C., “New appearance models for natural image matting”, in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 659–666.

B. Voss, Heinlein, A., Jähne, B., and Garbe, C. S., “A new approach for 3C3D measurements of aqueous boundary layer flows relative to the wind-wave undulated interface”, in 6th Int. Symp. Gas Transfer at Water Surfaces, Kyoto, May 17--21, 2010, 2010.

C. Rother, “A new approach to vanishing point detection in architectural environments”, in Image and Vision Computing, 2002, vol. 20, pp. 647–655.

T. Scholz, Jähne, B., Suhr, H., Wehnert, G., Geißler, P., and Schneider, K., “A new depth from focus technique for in situ determination of cell concentration in bioreactors”, in Proc. 16. DAGM-Symposium Mustererkennung, 1994, p. 145--150.

B. Jähne, “New experimental results on the parameters influencing air-sea gas exchange”, in Air-Water Mass Transfer, selected papers from the 2nd International Symposium on Gas Transfer at Water Surfaces, September 11--14, 1990, Minneapolis, Minnesota, 1991, p. 582--592.

J. Klinke and Jähne, B., “A new instrument for the optical measurement of the fine structure of the water surface in the field”, in IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves, 1995, p. 388.

G. Balschbach, Menzel, M., and Jähne, B., “A new instrument to measure steep wind-waves”, in IAPSO Proceedings, XXI General Assembly, Honolulu, Hawai, August 1995, PS-10 Spatial Structure of Short Ocean Waves, 1995, p. 387.

B. Jähne, Wais, T., and Barabas, M., “A new optical bubble measuring device; a simple model for bubble contribution to gas exchange”, in Gas transfer at water surfaces, 1984, p. 237--246.

K. E. Richter and Jähne, B., “New schemes for fast measurements of air-sea gas exchange in the Aeolotron lab”, in Poster abstracts SOLAS Open Science Conference, Barcelona, 16--19 Sep. 2009, 2009.

B. Jähne, “New trends in image processing hard- and software”, in Proceedings Image Analysis for Pulp and Paper Research and Production, 1993, p. 1--12.

R. Sprengel and Schnörr, C., “Nichtlineare Diffusion zur Integration visueller Daten - Anwendung auf Kernspintomogramme”, in Mustererkennung 1993, 15. DAGM-Symposium, 1993, pp. 134–141.

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision – ECCV 2002), 2002, vol. 2351, pp. 93–108.

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.

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D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, 2001, vol. 2191, p. 269--276.

Technical Report (324.55 KB)

D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.

C. Sigg, Fischer, B., Ommer, B., Roth, V., and Buhmann, J. M., “Nonnegative CCA for Audiovisual Source Separation”, in International Workshop on Machine Learning for Signal Processing, 2007, p. 253--258.

Technical Report (1.27 MB)

J. Jancsary, Nowozin, S., and Rother, C., “Non-parametric crfs for image labeling”, in NIPS Workshop Modern Nonparametric Methods in Machine Learning, 2012, pp. 1–5.

P. Márquez-Neila, Kohli, P., Rother, C., and Baumela, L., “Non-parametric higher-order random fields for image segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8694 LNCS, pp. 269–284.

W. Peckar, Schnörr, C., Rohr, K., and Stiehl, H. S., “Non-Rigid Image Registration Using a Parameter-Free Elastic Model”, in 9th British Machine Vision Conference (BMVC`98), Southampton/UK, 1998, pp. 134–143.

P. Esser, Rombach, R., and Ommer, B., “A Note on Data Biases in Generative Models”, in NeurIPS 2020 Workshop on Machine Learning for Creativity and Design, 2020.

U. Schimpf, Garbe, C. S., and Jähne, B., “Novel insights into heat transfer across the aqueous boundary layer by infrared imagery and its application to air-sea exchange processes”, in Proceedings of Eurotherm 71 on Visualization, Imaging and Data Analysis In Convective Heat and Mass Transfer, 2002.

M. Jehle and Jähne, B., “A novel method for spatio-temporal analysis of flows within the water-side viscous boundary layer”, in 12th Intern. Symp. on Flow Visualization, Göttingen, 10--14. September 2006, 2006.

B. Voss and Garbe, C. S., “Novel strategy for water sided interfacial 3D3Cflow-visualization using a single camera”, in 14th International Symposium on Flow Visualization, 2010, pp. D1-018.

F. Hering, Balschbach, G., Jähne, B., and Waldhäusl, P., “A novel system for the combined measurement of wave- and flow-fields beneath wind induced water waves”, in Proc. 18th Int. Congr. for Photogrammetry and Remote Sensing, 1996, vol. 31, p. 231--236.

F. Savarino, Hühnerbein, R., Aström, F., Recknagel, J., and Schnörr, C., “Numerical Integration of Riemannian Gradient Flows for Image Labeling”, in Proc. SSVM, 2017, vol. 10302.

H. Scharr, Körkel, S., and Jähne, B., “Numerische Isotropieoptimierung von FIR-Filtern mittels Querglättung”, in Proceedings of the 19th DAGM Symposium on Pattern Recognition, Braunschweig, 1997, p. 199--208.

T. Scheuermann, Pfundt, G., Eyerer, P., and Jähne, B., “Oberflächenkonturvermessung mikroskopischer Objekte durch Projektion statistischer Rauschmuster”, in Proc. 17. DAGM-Symposium Mustererkennung, Bielefeld, 13.-15. September 1995, 1995, p. 319--326.

B. Ommer and Buhmann, J. M., “Object Categorization by Compositional Graphical Models”, in Proceedings of the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, 2005, vol. 3757, p. 235--250.

Technical Report (2.07 MB)

S. Vicente, Rother, C., and Kolmogorov, V., “Object cosegmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2217–2224.

S. Zheng, Prisacariu, V. Adrian, Averkiou, M., Cheng, M. Ming, Mitra, N. J., Shotton, J., Torr, P. H. S., and Rother, C., “Object proposals estimation in depth image using compact 3D shape manifolds”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 196–208.

B. Schmitzer and Schnörr, C., “Object Segmentation by Shape Matching with Wasserstein Modes”, in Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2013), 2013, pp. 123-136.

M. Bleyer, Rother, C., Kohli, P., Scharstein, D., and Sinha, S., “Object stereo Joint stereo matching and object segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 3081–3088.

F. O. Kaster, Kassemeyer, S., Merkel, B., Nix, O., and Hamprecht, F. A., “An object-oriented library for systematic training and comparison of classifiers for computer-assisted tumor diagnosis from MRSI measurements”, in Bildverarbeitung für die Medizin 2010 -- Algorithmen, Systeme, Anwendungen, 2010, pp. 97-101.

Technical Report (1.12 MB)

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