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Cremers, D, Kohlberger, T and Schnörr, C (2003). Shape Statistics in Kernel Space for Variational Image Segmentation. Pattern Recognition. 36 1929–1943

Cremers, D and Schnörr, C (2002). Motion Competition: Variational Integration of Motion Segmentation and Shape Regularization. Pattern Recognition, Proc. 24th DAGM Symposium. Springer, Zürich, Switzerland. 2449 472–480

Cremers, D and Schnörr, C (2003). Statistical Shape Knowledge in Variational Motion Segmentation. Image and Vision Comp. 21 77-86

Cremers, D, Schnörr, C and Weickert, J (2001). Diffusion–Snakes: Combining Statistical Shape Knowledge and Image Information in a Variational Framework. IEEE First Workshop on Variational and Level Set Methods in Computer Vision. IEEE Comp. Soc., Vancouver, Canada. 237–244

Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Learning Translation Invariant Shape Knowledge for Steering Diffusion-Snakes. 3rd Workshop on Dynamic Perception. Akad. Verlagsges., Berlin, Germany. 9 117–122

Cremers, D, Schnörr, C, Weickert, J and Schellewald, C (2000). Diffusion Snakes Using Statistical Shape Knowledge. Proc. Algebraic Frames for the Perception-Action Cycle. Springer, Kiel. 1888 164–174

Cremers, D, Sochen, N and Schnörr, C (2003). Towards Recognition-Based Variational Segmentation Using Shape Priors and Dynamic Labeling. Scale Space Methods in Computer Vision. Springer. 2695 388–400

Cremers, D, Sochen, N and Schnörr, C (2004). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. Computer Vision – ECCV 2004. Springer. 3024 74-86

Cremers, D, Sochen, N and Schnörr, C (2006). Multiphase Dynamic Labeling for Variational Recognition-Driven Image Segmentation. ijcv. 66 67-81

Cremers, D, Tischhäuser, F, Weickert, J and Schnörr, C (2002). Diffusion Snakes: Introducing Statistical Shape Knowledge into the Mumford–Shah functional. Int. J. Computer Vision. 50 295–313

Criminisi, A, Blake, A, Rother, C, Shotton, J and Torr, P H S (2007). Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming. International Journal of Computer Vision. Kluwer Academic Publishers. 71 89–110

Criminisi, A, Shotton, J, Blake, A and Torr, P (2004). Efficient dense stereo and novel-view synthesis for gaze manipulation in one-to-one teleconferencing. http://www.research.microsoft.comi http://jamie.shotton.org/work/publications/TechRep2003-59.pdf

D

Dalitz, R, Petra, S and Schnörr, C (2017). Compressed Motion Sensing. Proc. SSVM. Springer. 10302

Damrich, S and Hamprecht, F H (2021). UMAP does not reproduce high-dimensional similarities due to negative sampling. arXiv preprint

Damrich, S and Hamprecht, F A (2021). On UMAP's True Loss Function. NeurIPS. Proceedings. 34

Technical Report (1.87 MB)

Damrich, S (2022). Discovering Structure without Labels. Heidelberg University

Daume, D (2010). Fusion Von Midwave-Infrared- Und Longwave-Infrared-Wärmebildgeräten Zur Klassifizierung Von Flugobjekten. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

Dauwe, S (1997). Infrarotuntersuchungen Zur Bestimmung Des Wasser- Und Wärmehaushalts Eines Blattes. University of Heidelberg

(2013). Time-of-Flight Imaging: Algorithms, Sensors and Applications (Dagstuhl Seminar 12431). Dagstuhl Reports. Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik. 2 79--104. http://drops.dagstuhl.de/opus/volltexte/2013/3904

Decker, C (2014). Automated Animal Behavior Classification. University of Heidelberg

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). Proceedings

Technical Report (1.48 MB)

Degreif, K (2006). Untersuchungen zum Gasaustausch - Entwicklung und Applikation eines zeitlich aufgelösten Massenbilanzverfahrens. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/6120

Degreif, K and Jähne, B (2006). Gas exchange measurements: transition of the boundary conditions from a flat to a rough water surface. Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006. Deutsche Physikalische Gesellschaft. http://www.dpg-verhandlungen.de/2006/heidelberg/up.html

Degreif, K and Jähne, B (2004). Gas exchange experiments using time resolved UV-spectroscopy. Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Munich, 22.-26.03.2004. Deutsche Physikalische Gesellschaft. http://www.dpg-verhandlungen.de/2004/up_15.html

Degreif, K, Kuss, J and Jähne, B (2006). Gas exchange measurements: the chemically enhanced gas transfer of carbon dioxide at the water surface. Verhandlungen der Deutschen Physikalischen Gesellschaft, Spring Conference, Heidelberg, 15.-17.03.2006. Deutsche Physikalische Gesellschaft. http://www.dpg-verhandlungen.de/2006/heidelberg/up.html

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

Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections. Fundamenta Informaticae. 135 73--102

Technical Report (2.24 MB)

Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography. Discrete Geometry for Computer Imagery (DGCI) 2014. Springer. 262--274

Technical Report (894.83 KB)

Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). Phase Transitions and Cosparse Tomographic Recovery of Compound Solid Bodies from Few Projections. Fundamenta Informaticae. 135 73–102

Denitiu, A, Petra, S, Schnörr, C and Schnörr, C (2014). An Entropic Perturbation Approach to TV-Minimization for Limited-Data Tomography. Discrete Geometry for Computer Imagery (DGCI) 2014. Springer. 262–274

Desana, M and Schnörr, C (2020). Sum-Product Graphical Models. Machine Learning. 109 135–173

Desana, M and Schnörr, C (2019). Sum-Product Graphical Models. Machine Learning. https://doi.org/10.1007/s10994-019-05813-2

Desana, M and Schnörr, C (2016). Expectation Maximization for Sum-Product Networks as Exponential Family Mixture Models. http://arxiv.org/abs/1604.07243

Detert, M, Jirka, G H, Jehle, M, Klar, M, Jähne, B, Köhler, H - J and Wenka, T (2004). Pressure fluctuations within subsurface gravel bed caused by turbulent open-channel flow. Proc. of River Flow 2004. A. A. Balkema Publishers. 695-701

Didden, E - M, Thorarinsdottir, T L, Lenkoski, A and Schnörr, C (2015). Shape from Texture using Locally Scaled Point Processes. Image Anal. Stereol. 34 161-170

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