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M. Klar, “Design of an endoscopic 3D Particle-Tracking Velocimetry system and its application in flow measurements within a gravel layer”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2005.

H. Schilling, Diebold, M., Gutsche, M., and Jähne, B., “On the design of a fractal calibration pattern for improved camera calibration”, tm - Technisches Messen, vol. 84, pp. 440–451, 2017.

V. Hilsenstein, “Design and Implementation of a Passive Stereo-Infrared Imaging System for the Surface Reconstruction of Water Waves”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2004.

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.

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.

B. Jähne, “Der Standard EMVA 1288: Objektive Charakterisierung von Bildsensoren und digitalen Kameras”. 2011.

B. Jähne, “Der Standard 1288 der European Machine Vision Association (EMVA 1288): Was macht die Qualität aus?”. 2011.

B. Jähne, “Der Ozean im Labor, Bildverarbeitung in den Umweltwissenschaften”. 2011.

P. Geißler, “Depth-from-Focus zur Messung der Größenverteilung durch Wellenbrechen erzeugter Blasenpopulationen”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1998.

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.

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, Scholz, T., Jähne, B., Haußecker, H., and Geißler, P., “Depth-from-focus for the measurement of size distributions of small particles”, Handbook of Computer Vision and Applications, vol. 3: Systems and Applications. Academic Press, pp. 623-646, 1999.

P. Geißler, “Depth-from-focus Bildanalyseverfahren zur Messung der Konzentration und Größe von Blasen und Mikroorganismen”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 1993.

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.

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.

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.

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.

Technical Report (1.85 MB)

H. Spies, Kirchgeßner, N., Scharr, H., and Jähne, B., “Dense structure estimation via regularised optical flow”, in VMV 2000, 2000, p. 57--64.

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, Jähne, B., and Barron, J. L., “Dense range flow from depth and intensity data”, in ICPR, 2000, p. 131--134.

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.

F. Lenzen, Schäfer, H., and Garbe, C. S., “Denoising Time-Of-Flight Data with Adaptive Total Variation”, in Proceedings ISVC, 2011, pp. 337-346.

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, Kim, K. In, Schäfer, H., Nair, R., Meister, S., Becker, F., and Garbe, C. S., “Denoising Strategies for Time-of-Flight Data”, Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications, vol. 8200. Springer, pp. 25-45, 2013.

Technical Report (961.62 KB)

M. Frank, Plaue, M., and Hamprecht, F. A., “Denoising of Continuous-Wave Time-Of-Flight Depth Images Using Confidence Measures”, Optical Engineering, vol. 48, 077003, 2009.

Technical Report (2.5 MB)

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.

Technical Report (1.44 MB)

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.

S. Bollweg, Haußmann, M., Kasieczka, G., Luchmann, M., Plehn, T., and Thompson, J., “Deep-Learning Jets with Uncertainties and More”, SciPost Phys, vol. 8, no. 1, 2020.

Technical Report (1.65 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.

deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)

A. Sanakoyeu, Bautista, M., and Ommer, B., “Deep Unsupervised Learning of Visual Similarities”, Pattern Recognition, vol. 78, 2018.

PDF (8.35 MB)

N. Ufer and Ommer, B., “Deep Semantic Feature Matching”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

article (8.88 MB)

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.

J. Kleesiek, Urban, G., Hubert, A., Schwarz, D., Maier-Hein, K., Bendszus, M., and Biller, A., “Deep MRI brain extraction: A 3D convolutional neural network for skull stripping.”, NeuroImage, vol. 129, pp. 460-469, 2016.

Technical Report (1.14 MB)

T. Dencker, Klinkisch, P., Maul, S. M., and Ommer, B., “Deep learning of cuneiform sign detection with weak supervision using transliteration alignment”, PLoS ONE, vol. 15, no. 12, 2020.

A. Bailoni, “Deep Learning for Graph-Based Image Instance Segmentation”. Heidelberg University, 2021.

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