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L. Cerrone, “Deep End-to-End Learning of a Diffusion Process for Seeded Image Segmentation”, Heidelberg University, 2018.

M. Kandemir and Hamprecht, F. A., “The Deep Feed-Forward Gaussian Process: An Effective Generalization to Covariance Priors”, NIPS. Proceedings, vol. 44. pp. 145-159, 2015.

Supplementary Material (223.39 KB)

Technical Report (2.58 MB)

A. Ruiz, “Deep k-segments: a generalization of k-means”, Heidelberg University, 2021.

P. Schmidt, “Deep Learning for Bioimage Analysis”, University of Heidelberg, 2016.

L. Balles, “Deep Learning for Diabetic Retinopathy Diagnostics”, University of Heidelberg, 2016.

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

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.

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)

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.

article (8.88 MB)

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

PDF (8.35 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)

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)

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.

Technical Report (1.44 MB)

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)

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)

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.

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, “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.

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, 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.

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

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

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

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

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