Publications

Search

Show only items where

Author

Type

Term

Year

Keyword

Export 1965 results:

A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

D

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. http://www.ub.uni-heidelberg.de/archiv/5961

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

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. http://www.ub.uni-heidelberg.de/archiv/4601

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

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. http://www.ub.uni-heidelberg.de/archiv/17699

Jähne, (2011). Der Standard EMVA 1288: Objektive Charakterisierung von Bildsensoren und digitalen Kameras. www.elektroniknet.de

Jähne, (2011). Der Standard 1288 der European Machine Vision Association (EMVA 1288): Was macht die Qualität aus?. http://www.qe-online.de/home

Jähne, (2011). Der Ozean im Labor, Bildverarbeitung in den Umweltwissenschaften. www.laborundmore.de/

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

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, 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)

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

article (8.88 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

Pages