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Shotton, J, Winn, J, Rother, C and Criminisi, A (2006). TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3951 LNCS 1–15. http://research.microsoft.com/vision/cambridge/recognition/.

Shotton, J, Winn, J, Rother, C and Criminisi, A (2009). TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision. 81 2–23. http://jamie.shotton.org/work/code.html

Haußecker, H, Spies, H and Jähne, B (1998). Tensor-based image sequence processing techniques for the study of dynamical processes. Proc. Intern. Symp. On Real-time Imaging and Dynamic Analysis. International Society of Photogrammetry and Remote Sensing, ISPRS, Commision V. 704--711

Haußecker, H and Jähne, B (1997). A tensor approach for precise computation of dense displacement vector fields. Proceedings of the 19th DAGM Symposium on Pattern Recognition, Braunschweig. Springer. 199--208

Haußecker, H and Jähne, B (1996). A tensor approach for local structure analysis in multi-dimensional images. 3D Image Analysis and Synthesis. 171--178

Menze, B H, Ur, J A and Sherratt, A G (2005). Tell Spotting - Surveying Near Eastern Settlement Mounds from Space. Proceedings of the XXth CIPA International Symposium 2005, Torino, Italy. 217-223

Technical Report (2.53 MB)

Jähne, B, Massen, R, Nickolay, B and Scharfenberg, H (1995). Technische Bildverarbeitung - Maschinelles Sehen. Springer. http://d-nb.info/94569895X

Lefloch, D, Nair, R, Lenzen, F, Schäfer, H, Streeter, L and Cree, M J (2013). Technical Foundation and Calibration Methods for Time-of-Flight Cameras. Time-of-Flight Imaging: Algorithms, Sensors and Applications. Springer. 8200

Lefloch, D, Nair, R, Lenzen, F, Schäfer, H, Streeter, L, Cree, M J, Koch, R and Kolb, A (2013). Technical Foundation and Calibration Methods for Time-of-Flight Cameras. Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications. Springer. 8200 3-24

Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2020). Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. AISTATS 2020. https://gitlab.com/

PDF (2.58 MB)

Esser, P, Rombach, R and Ommer, B (2021). Taming Transformers for High-Resolution Image Synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://arxiv.org/abs/2012.09841

S

Mischler, W (2014). Systematic Measurements of Bubble Induced Gas Exchange for Trace Gases with Low Solubilities. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. Dissertation. http://www.ub.uni-heidelberg.de/archiv/17720

Hader, S (2003). System Concept for Image Sequence Classification in Laser Welding. Pattern Recognition. Springer. 2781 212-219

Großkinsky, (2019). Synaptic Cleft Prediction On Electron Microsope Images. Heidelberg University

Silvestri, F, Reinelt, G and Schnörr, C (2016). Symmetry-free SDP Relaxations for Affine Subspace Clustering. http://arxiv.org/abs/1607.07387

Neumann, J, Schnörr, C and Steidl, G (2004). SVM-based Feature Selection by Direct Objective Minimisation. Pattern Recognition, Proc. 26th DAGM Symposium. Springer. 3175 212-219

Nair, R, Ruhl, K, Lenzen, F, Meister, S, Schäfer, H, Garbe, C S, Eisemann, M, Magnor, M and Kondermann, D (2013). A Survey on Time-of-Flight Stereo Fusion. Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications. Springer. 8200 105-127

Nair, R, Ruhl, K, Lenzen, F, Meister, S, Schäfer, H, Garbe, C S, Eisemann, M, Magnor, M and Kondermann, D (2013). A Survey on Time-of-Flight Stereo Fusion. Time-of-Flight and Depth Imaging: Sensors, Algorithms, and Applications. Springer. 8200 105-127

Technical Report (6.05 MB)

Nair, R, Ruhl, K, Lenzen, F, Meister, S, Schäfer, H, Garbe, C S, Eisemann, M and Kondermann, D (2013). A Survey on Time-of-Flight Stereo Fusion. Time-of-Flight Imaging: Algorithms, Sensors and Applications. Springer. 8022 105-127

Hara, T, Uz, B M, Wei, H, Edson, J B, Frew, N M, McGilles, W R, McKenna, S P, Bock, E J, Haußecker, H and Schimpf, U (2002). Surface wave observations during CoOP experiments and their relations to air-sea gas transfer. Gas Transfer at Water Surfaces. American Geophysical Union. 127 45--49

Gutsche, M (2014). Surface Velocity Measurements At The Aeolotron By Means Of Active Thermography. Institut für Umweltphysik, Universität Heidelberg, Germany. http://www.ub.uni-heidelberg.de/archiv/17431

Gutsche, M (2014). Surface Velocity Measurements At The Aeolotron By Means Of Active Thermography. Institut für Umweltphysik, Universität Heidelberg, Germany

Bleyer, M, Rother, C and Kohli, P (2010). Surface stereo with soft segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1570–1577

Garbe, C S, Schimpf, U and Jähne, B (2004). A surface renewal model to analyze infrared image sequences of the ocean surface for the study of air-sea heat and gas exchange. J. Geophys. Res. 109 1-18

Garbe, C S, Schimpf, U and Jähne, B (2003). A surface renewal model to analyze infrared image sequences for the study of air-sea heat and gas exchange. Geophysical Research Abstracts. 11893

Wolf, C (1994). Surface Properties Of Breaking Water Waves. Institut für Umweltphysik, Fakultät für Physik und Astronomie, Univ.\ Heidelberg

Spies, H, Jähne, B and Barron, J L (2001). Surface expansion from range data sequences. Proceedings of the 23th DAGM Symposium on Pattern Recognition. Springer. 163--169

Singaraju, D, Rother, C and Rhemann, C (2009). Supplementary Material For New Appearance Models For Image Matting

Nowozin, S and Sharp, T (2011). Supplementary Material : Decision Tree Fields. Iccv

(2018). A Supplementary Material Cereals-Cost-Effective Region-Based Active Learning For Semantic Segmentation

Goldlücke, B, Aubry, M, Kolev, K and Cremers, D (2014). A super-resolution framework for high-accuracy multiview reconstruction. Int. J. Comp. Vision. 106 172--191

Censor, Y, Petra, S and Schnörr, C (2019). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. preprint: arXiv. https://arxiv.org/abs/1911.05498

Censor, Y, Petra, S and Schnörr, C (2020). Superiorization vs. Accelerated Convex Optimization: The Superiorized/Regularized Least Squares Case. J. Appl. Numer. Optimization (in press; arXiv:1911.05498). 2 15-62. http://jano.biemdas.com/archives/1060

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 (2020). Sum-Product Graphical Models. Machine Learning. 109 135–173

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