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D. Cremers, Kohlberger, T., and Schnörr, C., “Shape Statistics in Kernel Space for Variational Image Segmentation”, Pattern Recognition, vol. 36, pp. 1929–1943, 2003.
D. Cremers and Schnörr, C., “Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization”, in Pattern Recognition, Proc. 24th DAGM Symposium, Zürich, Switzerland, 2002, vol. 2449, pp. 472–480.
D. Cremers and Schnörr, C., “Statistical Shape Knowledge in Variational Motion Segmentation”, Image and Vision Comp., vol. 21, pp. 77-86, 2003.
D. Cremers, Schnörr, C., and Weickert, J., “Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework”, in IEEE First Workshop on Variational and Level Set Methods in Computer Vision, Vancouver, Canada, 2001, pp. 237–244.
D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes”, in 3rd Workshop on Dynamic Perception, Berlin, Germany, 2000, vol. 9, pp. 117–122.
D. Cremers, Schnörr, C., Weickert, J., and Schellewald, C., “Diffusion Snakes Using Statistical Shape Knowledge”, in Proc. Algebraic Frames for the Perception-Action Cycle, Kiel, 2000, vol. 1888, pp. 164–174.
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, pp. 388–400.
D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, in Computer Vision – ECCV 2004, 2004, vol. 3024, pp. 74-86.
D. Cremers, Sochen, N., and Schnörr, C., “Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation”, ijcv, vol. 66, pp. 67-81, 2006.
D. Cremers, Tischhäuser, F., Weickert, J., and Schnörr, C., “Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional”, Int. J. Computer Vision, vol. 50, pp. 295–313, 2002.
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
R. Dalitz, Petra, S., and Schnörr, C., “Compressed Motion Sensing”, in Proc. SSVM, 2017, vol. 10302.
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.
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. Dauwe, “Infrarotuntersuchungen zur Bestimmung des Wasser- und Wärmehaushalts eines Blattes”, University of Heidelberg, 1997.
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.
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)
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.
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.
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)
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.
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.
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.
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.

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