HCI Logo
Heidelberg University Logo
IWR Logo

Publications

Export 1963 results:
[ Author(Asc)] Title Type Year
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
M. Diebold, Gatto, A., and Jähne, B., “Heterogeneous Light Fields”, in 2016 {IEEE} Conference on Computer Vision and Pattern Recognition, {CVPR} 2016, Las Vegas, NV, USA, June 27-30, 2016, 2016.
M. Diebold, Blum, O., Gutsche, M., Wanner, S., Garbe, C. S., Baker, H., and Jähne, B., “Light-field camera design for high-accuracy depth estimation”, Videometrics, Range Imaging, and Applications XIII. 2015.
M. Diebold and Goldlücke, B., “Epipolar Plane Image Refocusing for Improved Depth Estimation and Occlusion Handling.”, in VMV, 2013.
E. - M. Didden, Thorarinsdottir, T. L., Lenkoski, A., and Schnörr, C., “Shape from Texture using Locally Scaled Point Processes”, Image Anal. Stereol., vol. 34, pp. 161-170, 2015.
M. Detert, Jirka, G. H., Jehle, M., Klar, M., Jähne, B., Köhler, H. - J., and Wenka, T., “Pressure fluctuations within subsurface gravel bed caused by turbulent open-channel flow”, in Proc. of River Flow 2004, 2004, pp. 695-701.
M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, vol. 109, pp. 135–173, 2020.
M. Desana and Schnörr, C., “Sum-Product Graphical Models”, Machine Learning, 2019.
M. Desana and Schnörr, C., “Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models”. 2016.
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, pp. 73–102, 2014.
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography”, in Discrete Geometry for Computer Imagery (DGCI) 2014, 2014, pp. 262–274.
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections”, Fundamenta Informaticae, vol. 135, p. 73--102, 2014.PDF icon Technical Report (2.24 MB)
A. Denitiu, Petra, S., Schnörr, C., and Schnörr, C., “An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography”, in Discrete Geometry for Computer Imagery (DGCI) 2014, 2014, p. 262--274.PDF icon Technical Report (894.83 KB)
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.
K. Degreif, “Untersuchungen zum Gasaustausch - Entwicklung und Applikation eines zeitlich aufgelösten Massenbilanzverfahrens”. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2006.
K. Degreif and Jähne, B., “Gas exchange measurements: transition of the boundary conditions from a flat to a rough water surface”, in Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006, 2006.
K. Degreif and Jähne, B., “Gas exchange experiments using time resolved UV-spectroscopy”, in Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Munich, 22.-26.03.2004, 2004.
K. Degreif, Kuss, J., and Jähne, B., “Gas exchange measurements: the chemically enhanced gas transfer of carbon dioxide at the water surface”, in Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006, 2006.
C. Decker, “Automated Animal Behavior Classification”, University of Heidelberg, 2014.
C. Decker and Hamprecht, F. A., “Detecting individual body parts improves mouse behavior classification”, in Workshop on visual observation and analysis of Vertebrate And Insect Behavior (VAIB), 22nd International Conference on Pattern Recognition (ICPR). Proceedings, 2014.PDF icon Technical Report (1.48 MB)
J. Davis, Jähne, B., Kolb, A., Raskar, R., Theobalt, C., Davis, J., Jähne, B., Raskar, R., Theobalt, C., and Kolb, A., Eds., “Time-of-Flight Imaging: Algorithms, Sensors and Applications (Dagstuhl Seminar 12431)”, Dagstuhl Reports, vol. 2, p. 79--104, 2013.
S. Dauwe, “Infrarotuntersuchungen zur Bestimmung des Wasser- und Wärmehaushalts eines Blattes”, University of Heidelberg, 1997.
D. Daume, “Fusion von Midwave-infrared- und Longwave-infrared-Wärmebildgeräten zur Klassifizierung von Flugobjekten”, Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2010.
S. Damrich and Hamprecht, F. H., “UMAP does not reproduce high-dimensional similarities due to negative sampling”. arXiv preprint, 2021.
S. Damrich and Hamprecht, F. A., “On UMAP's True Loss Function”, NeurIPS. Proceedings, vol. 34. 2021.PDF icon Technical Report (1.87 MB)
S. Damrich, “Discovering Structure without Labels”, Heidelberg University. 2022.
R. Dalitz, Petra, S., and Schnörr, C., “Compressed Motion Sensing”, in Proc. SSVM, 2017, vol. 10302.
C
A. Criminisi, Blake, A., Rother, C., Shotton, J., and Torr, P. H. S., “Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming”, International Journal of Computer Vision, vol. 71, pp. 89–110, 2007.
A. Criminisi, Shotton, J., Blake, A., and Torr, P., “Efficient dense stereo and novel-view synthesis for gaze manipulation in one-to-one teleconferencing”, 2004.
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision -- ECCV 2002), 2002, vol. 2351, p. 93--108.PDF icon Technical Report (636.58 KB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, 2001, vol. 2191, p. 269--276.PDF icon Technical Report (324.55 KB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, p. 1929--1943, 2003.PDF icon Technical Report (1.67 MB)
D. Cremers, Sochen, N., and Schnörr, C., “Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling”, in Scale Space Methods in Computer Vision, 2003, vol. 2695, p. 388--400.PDF icon Technical Report (451.82 KB)
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics in Mumford-Shah Based Segmentation”, in Computer Vision – ECCV 2002), 2002, vol. 2351, pp. 93–108.
D. Cremers, Kohlberger, T., and Schnörr, C., “Nonlinear Shape Statistics via Kernel Spaces”, in Mustererkennung 2001, Munich, Germany, 2001, vol. 2191, pp. 269–276.
D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.

Pages