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Conference Paper
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.
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.
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.PDF icon Article (891.47 KB)PDF icon Oral slides (9.17 MB)
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.
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.
L. Maier-Hein, Mersmann, S., Kondermann, D., Stock, C., Kenngott, H., Sanchez, A., Wagner, M., Preukschas, A., Wekerle, A. - L., Helfert, S., Bodenstedt, S., and Speidel, S., “Crowdsourcing for reference correspondence generation in endoscopic images”, in MICCAI, 2014.
A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.
T. Beier, Kröger, T., Kappes, J. H., Köthe, U., and Hamprecht, F. A., “Cut, Glue and Cut: A Fast, Approximate Solver for Multicut Partitioning”, in 2014 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2014, Columbus, OH, USA, June 23-28, 2014, 2014.PDF icon Technical Report (10.06 MB)
K. Honauer, Johannsen, O., Kondermann, D., and Goldlücke, B., “A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields”, in Computer Vision - ACCV 2016 : 13th Asian Conference on Computer Vision, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part III, Cham, 2016.
S. Wanner, Meister, S., and Goldlücke, B., “Datasets and Benchmarks for Densely Sampled 4D Light Fields”, in Vision, Modeling & Visualization, 2013, p. 225--226.
S. Nowozin, Rother, C., Bagon, S., Sharp, T., Yao, B., and Kohli, P., “Decision tree fields”, in Proceedings of the IEEE International Conference on Computer Vision, 2011, pp. 1668–1675.
F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.PDF icon Technical Report (1.29 MB)
F. Becker and Schnörr, C., “Decomposition of Quadratric Variational Problems”, in Pattern Recognition -- 30th DAGM Symposium, 2008, vol. 5096, p. 325--334.
W. Li, Hosseini Jafari, O., and Rother, C., “Deep Object Co-segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, vol. 11363 LNCS, pp. 638–653.
N. Ufer and Ommer, B., “Deep Semantic Feature Matching”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.PDF icon article (8.88 MB)
M. Bautista, Sanakoyeu, A., and Ommer, B., “Deep Unsupervised Similarity Learning using Partially Ordered Sets”, in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.PDF icon deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)
P. van Vliet, Hering, F., Jähne, B., and Jähne, B., “Delft Hydraulics Large Wind-Wave Flume”, in Air-Water Gas Transfer---Selected Papers from the Third International Symposium of Air--Water Gas Transfer in Heidelberg, 1995, p. 499--502.
X. Lou, Kaster, F. O., Lindner, M., Kausler, B. X., Köthe, U., Höckendorf, B., Wittbrodt, J., Jänicke, H., and Hamprecht, F. A., “DELTR: Digital Embryo Lineage Tree Reconstructor”, in Eighth IEEE International Symposium on Biomedical Imaging (ISBI). Proceedings, 2011, pp. 1557-1560.PDF icon Technical Report (1.44 MB)
F. Lenzen, Kim, K. I., Schäfer, H., Nair, R., Meister, S., Becker, F., and Garbe, C. S., “Denoising Strategies for Time-of-Flight Data”, in Time-of-Flight Imaging: Algorithms, Sensors and Applications, 2013, vol. 8200, pp. 24-25.
F. Lenzen, Schäfer, H., and Garbe, C. S., “Denoising Time-Of-Flight Data with Adaptive Total Variation”, in Proceedings ISVC, 2011, pp. 337-346.
H. Spies and Garbe, C. S., “Dense parameter fields from total least squares”, in Proceedings of the 24th DAGM Symposium on Pattern Recognition, 2002, vol. LNCS 2449, p. 379--386.
H. Spies, Jähne, B., and Barron, J. L., “Dense range flow from depth and intensity data”, in ICPR, 2000, p. 131--134.
S. Zheng, Cheng, M. Ming, Warrell, J., Sturgess, P., Vineet, V., Rother, C., and Torr, P. H. S., “Dense semantic image segmentation with objects and attributes”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3214–3221.
H. Spies, Kirchgeßner, N., Scharr, H., and Jähne, B., “Dense structure estimation via regularised optical flow”, in VMV 2000, 2000, p. 57--64.
H. Schäfer, Lenzen, F., and Garbe, C. S., “Depth and Intensity Based Edge Detection in Time-of-Flight Images”, in 3DV-Conference, 2013 International Conference on, 2013, pp. 111-118.PDF icon Technical Report (1.85 MB)
H. Schäfer, Lenzen, F., and Garbe, C. S., “Depth and Intensity Based Edge Detection in Time-of-Flight Images”, in 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2013 International Conference on, 2013, pp. 111-118.
B. Jähne and Geißler, P., “Depth from focus with one image”, in Proc. Conference on Computer Vision and Pattern Recognition (CVPR '94), Seattle, 20.-23. June 1994, 1994, p. 713--717.
M. Hornáček, Rhemann, C., Gelautz, M., and Rother, C., “Depth super resolution by rigid body self-similarity in 3D”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, pp. 1123–1130.
P. Geißler, Scholz, T., Jähne, B., Schmidt, C., Suhr, H., and Wehnert, G., “Depth-from-Focus Verfahren zur absoluten Größen- und Konzentrationsbestimmung kleiner Teilchen”, in Bildverarbeitung'95 - Forschen, Entwickeln, Anwenden, 1995, p. 365--380.
P. Geißler, Jähne, B., and Pöppl, S. J., “Depth-from-focus zur Bestimmung der Konzentration und Größe von Gasblasen”, in Proc. 15. DAGM-Symposium Mustererkennung, 1993, p. 560--567.
B. Jähne, “Der Standard EMVA 1288 zur Charakterisierung von Kameras und Bildsensoren: von 2D- zu 3D-Kameras”, in Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Beiträge der Oldenburger 3D-Tage 2013, 2013, p. 388--399.
E. J. Bock, Edson, J. B., Frew, N. M., Karachintsev, A., McGilles, W. R., Nelson, R. K., Hansen, K., Jähne, B., Hara, T., Uz, B. M., Jähne, B., Dieter, J., Klinke, J., and Haußecker, H., “Description of the science plan for the April 1995 CoOP experiment, `gas transfer in coastal waters', performed from the research vessel New Horizon”, in Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer, 1995, p. 801--810.
A. Bruhn, Jakob, T., Fischer, M., Kohlberger, T., Weickert, J., Brüning, U., and Schnörr, C., “Designing 3–D Nonlinear Diffusion Filters for High Performance Cluster Computing”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 290–297.

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