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Geißler, P, Scholz, T, Jähne, B, Schmidt, C, Suhr, H and Wehnert, G (1995). Depth-from-Focus Verfahren zur absoluten Größen- und Konzentrationsbestimmung kleiner Teilchen. Bildverarbeitung'95 - Forschen, Entwickeln, Anwenden. Technische Akademie Esslingen. 365--380

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

Geißler, (1993). Depth-From-Focus Bildanalyseverfahren Zur Messung Der Konzentration Und Größe Von Blasen Und Mikroorganismen. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

Hornáček, M, Rhemann, C, Gelautz, M and Rother, C (2013). Depth super resolution by rigid body self-similarity in 3D. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1123–1130

Jähne, B and Geißler, P (1994). Depth from focus with one image. Proc. Conference on Computer Vision and Pattern Recognition (CVPR '94), Seattle, 20.-23. June 1994. 713--717

Schäfer, H, Lenzen, F and Garbe, C S (2013). Depth and Intensity Based Edge Detection in Time-of-Flight Images. 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), 2013 International Conference on. IEEE. 111-118

Schäfer, H, Lenzen, F and Garbe, C S (2013). Depth and Intensity Based Edge Detection in Time-of-Flight Images. 3DV-Conference, 2013 International Conference on. 111-118

Technical Report (1.85 MB)

Spies, H, Kirchgeßner, N, Scharr, H and Jähne, B (2000). Dense structure estimation via regularised optical flow. VMV 2000. Aka GmbH, Berlin. 57--64

Zheng, S, Cheng, M Ming, Warrell, J, Sturgess, P, Vineet, V, Rother, C and Torr, P H S (2014). Dense semantic image segmentation with objects and attributes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3214–3221. http://www.robots.ox.ac.uk/˜tvg/http://tu-dresden.de/inf/cvld

Spies, H, Jähne, B and Barron, J L (2000). Dense range flow from depth and intensity data. ICPR. 131--134

Spies, H and Garbe, C S (2002). Dense parameter fields from total least squares. Proceedings of the 24th DAGM Symposium on Pattern Recognition. Springer. LNCS 2449 379--386

Lenzen, F, Schäfer, H and Garbe, C S (2011). Denoising Time-Of-Flight Data with Adaptive Total Variation. Proceedings ISVC. Springer. 337-346

Lenzen, F, Kim, K I, Schäfer, H, Nair, R, Meister, S, Becker, F and Garbe, C S (2013). Denoising Strategies for Time-of-Flight Data. Time-of-Flight Imaging: Algorithms, Sensors and Applications. Springer. 8200 24-25

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

Technical Report (961.62 KB)

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

Technical Report (2.5 MB)

Lou, X, Kaster, F O, Lindner, M, Kausler, B X, Köthe, U, Höckendorf, B, Wittbrodt, J, Jänicke, H and Hamprecht, F A (2011). DELTR: Digital Embryo Lineage Tree Reconstructor. Eighth IEEE International Symposium on Biomedical Imaging (ISBI). Proceedings. 1557-1560

Technical Report (1.44 MB)

van Vliet, P, Hering, F, Jähne, B and Jähne, B (1995). Delft Hydraulics Large Wind-Wave Flume. Air-Water Gas Transfer---Selected Papers from the Third International Symposium of Air--Water Gas Transfer in Heidelberg. AEON. 499--502

Bollweg, S, Haußmann, M, Kasieczka, G, Luchmann, M, Plehn, T and Thompson, J (2020). Deep-Learning Jets with Uncertainties and More. SciPost Phys. 8. https://scipost.org/10.21468/SciPostPhys.8.1.006

Technical Report (1.65 MB)

Bautista, M, Sanakoyeu, A and Ommer, B (2017). Deep Unsupervised Similarity Learning using Partially Ordered Sets. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)

Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. 78. https://authors.elsevier.com/a/1WXUt77nKSb25

PDF (8.35 MB)

Li, W, Hosseini Jafari, O and Rother, C (2019). Deep Object Co-segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11363 LNCS 638–653

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

Technical Report (1.14 MB)

Dencker, T, Klinkisch, P, Maul, S M and Ommer, B (2020). Deep learning of cuneiform sign detection with weak supervision using transliteration alignment. PLoS ONE. 15. https://hci.iwr.uni-heidelberg.de/compvis/projects/cuneiform

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

Balles, L (2016). Deep Learning For Diabetic Retinopathy Diagnostics. University of Heidelberg

Schmidt, P (2016). Deep Learning For Bioimage Analysis. University of Heidelberg

Ruiz, A (2021). Deep K-Segments: A Generalization Of K-Means. Heidelberg University

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

Supplementary Material (223.39 KB)

Technical Report (2.58 MB)

Cerrone, L (2018). Deep End-To-End Learning Of A Diffusion Process For Seeded Image Segmentation. Heidelberg University

Haußmann, M, Hamprecht, F A and Kandemir, M (2019). Deep Active Learning with Adaptive Acquisition. IJCAI. Proceedings. 2470-2476

Technical Report (137.6 KB)

Becker, F and Schnörr, C (2008). Decomposition of Quadratric Variational Problems. Pattern Recognition -- 30th DAGM Symposium. Springer Verlag. 5096 325--334

Technical Report (1.29 MB)

Becker, F and Schnörr, C (2008). Decomposition of Quadratric Variational Problems. Pattern Recognition -- 30th DAGM Symposium. 5096 325--334

Lu, G -hung, Tsai, W -ting and Jähne, B (2019). Decomposing infrared images of wind waves for quantitative separation into characteristic flow processes. IEEE Transactions on Geoscience and Remote Sensing. 57 8304–8316

Nowozin, S, Rother, C, Bagon, S, Sharp, T, Yao, B and Kohli, P (2011). Decision tree fields. Proceedings of the IEEE International Conference on Computer Vision. 1668–1675

Rennekamp, F (1998). Datenbank Gestützte Verwaltung Kalibrierter Bildsequenzen Zur Qualitätsbewertung Von Algorithmen. Fakultät für Physik und Astronomie Universität Heidelberg

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