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Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. 78. PDF icon PDF (8.35 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)PDF icon deep_unsupervised_similarity_learning_cvpr_2017_paper.pdf (905.82 KB)
Bollweg, S, Haußmann, M, Kasieczka, G, Luchmann, M, Plehn, T and Thompson, J (2019). Deep-Learning Jets with Uncertainties and More . arXiv preprint arXiv:1904.10004
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
Lou, X, Kaster, F, Lindner, M, Kausler, B, 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. 1557-1560PDF icon Technical Report (1.44 MB)
Frank, M, Plaue, M and Hamprecht, F A (2009). Denoising of Continuous-Wave Time-Of-Flight Depth Images Using Confidence Measures. Optical Engineering. 48, 077003PDF icon Technical Report (2.5 MB)
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 25-45PDF icon Technical Report (961.62 KB)
Lenzen, F, Schäfer, H and Garbe, C S (2011). Denoising Time-Of-Flight Data with Adaptive Total Variation. Proceedings ISVC. Springer. 337-346
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
Spies, H, Jähne, B and Barron, J L (2000). Dense range flow from depth and intensity data. ICPR. 131--134
Spies, H, Kirchgeßner, N, Scharr, H and Jähne, B (2000). Dense structure estimation via regularised optical flow. VMV 2000. Aka GmbH, Berlin. 57--64
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. 111-118PDF icon Technical Report (1.85 MB)
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
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
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
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, 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, Jähne, B and Pöppl, S J (1993). Depth-from-focus zur Bestimmung der Konzentration und Größe von Gasblasen. Proc. 15. DAGM-Symposium Mustererkennung. Springer. 560--567
Geißler, (1998). Depth-from-Focus zur Messung der Größenverteilung durch Wellenbrechen erzeugter Blasenpopulationen. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg
Jähne, (2011). Der Ozean im Labor, Bildverarbeitung in den Umweltwissenschaften.
Jähne, (2011). Der Standard 1288 der European Machine Vision Association (EMVA 1288): Was macht die Qualität aus?.
Jähne, (2011). Der Standard EMVA 1288: Objektive Charakterisierung von Bildsensoren und digitalen Kameras.
Jähne, (2013). Der Standard EMVA 1288 zur Charakterisierung von Kameras und Bildsensoren: von 2D- zu 3D-Kameras. Photogrammetrie, Laserscanning, Optische 3D-Messtechnik, Beiträge der Oldenburger 3D-Tage 2013. Wichmann. 388--399.
Bock, E J, 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 (1995). Description of the science plan for the April 1995 CoOP experiment, `gas transfer in coastal waters', performed from the research vessel New Horizon. Air-Water Gas Transfer, Selected Papers, 3rd Intern. Symp. on Air-Water Gas Transfer. AEON. 801--810
Hilsenstein, V (2004). 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.
Klar, M (2005). 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.
Bruhn, A, Jakob, T, Fischer, M, Kohlberger, T, Weickert, J, Brüning, U and Schnörr, C (2002). Designing 3--D Nonlinear Diffusion Filters for High Performance Cluster Computing. Pattern Recognition, Proc.~24th DAGM Symposium. Springer. 2449 290--297
Schlecht, J, Carque, B and Ommer, B (2011). Detecting Gestures in Medieval Images. Proceedings of the International Conference on Image Processing. IEEE. 1309--1312PDF icon Technical Report (1.61 MB)
Decker, C and Hamprecht, F A (2014). Detecting individual body parts improves mouse behavior classification. Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). ProceedingsPDF icon Technical Report (1.48 MB)
Menze, B H, Ur, J A and Sherratt, A G (2006). Detection of ancient settlement mounds - Archaeological survey based on the SRTM terrain model. Photgrammetric Engineering & Remote Sensing. 3 321-327PDF icon Technical Report (643.89 KB)
Köhler, H - J, Haußecker, H and Jähne, B (1996). Detection of particle movements at soil interfaces due to changing hydraulic load conditions, localised by a digital image processing technique. Proc. Geofilters 1996, Montreal. Ecole Polytechnique Montreal
Sprengel, R, Schnörr, C and Neumann, B (1994). Detection of Visual Data Transitions in Nonlinear Parameter Space. Mustererkennung 1994. Technische Universität Wien. 5 315--323
Görlitz, L, Hamprecht, F A and Staudacher, M (2005). Detektion von Partikeln in Intensitätsbildern mit Hilfe eines morphologischen Skalenraumes. Robert-Bosch GmbH, University of Heidelberg
Schnörr, (1991). Determining Optical Flow for Irregular Domains by Minimizing Quadratic Functionals of a Certain Class. IJCV. 6 25--38