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Rother, C (2003). Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane. http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=8&ved=0CDUQFjAD&url=http%3A%2F%2Fwww.nada.kth.se%2Futbildning%2Fforsk.utb%2Favhandlingar%2Fdokt%2Frother.pdf&ei=AyX_VPKmIomeNqeOgpgL&usg=AFQjCNHCmc75P5EHYWLtBUaHtUAs4yOnJw&bvm=bv.
Rother, C, Kolmogorov, V and Blake, A (2004). "GrabCut" - Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics. 23 309–314
Rother, C and Kolmogorov, V (2011). Interactive foreground extraction using graph cut. Advances in Markov \ldots. 1–20. http://research.microsoft.com/pubs/147408/rotheretalmrfbook-grabcut.pdf
Royer, L A, Richmond, D L, Rother, C, Andres, B and Kainmueller, D (2016). Convexity shape constraints for image segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 402–410. http://arxiv.org/abs/1509.02122
Rubio, J C and Ommer, B (2015). Regularizing Max-Margin Exemplars by Reconstruction and Generative Models. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. 4213--4221PDF icon Technical Report (2.8 MB)
Rubio, J C, Eigenstetter, A and Ommer, B (2015). Generative Regularization with Latent Topics for Discriminative Object Recognition. Pattern Recognition. Elsevier. 48 3871--3880PDF icon Technical Report (5.49 MB)
Rudigier, E (2000). Entwicklung Eines Automatisierten Bildverarbeitungssystems Zur Auswertung Unregelmäßiger Bildpunkte Auf Dna-Chips. University of Heidelberg
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2005). Variational Optical Flow Estimation for Particle Image Velocimetry. Experiments in Fluids. 38 21--32PDF icon Technical Report (1.21 MB)
Ruhnau, P and Schnörr, C (2007). Optical Stokes Flow Estimation: An Imaging-Based Control Approach. Exp.~in Fluids. 42 61--78PDF icon Technical Report (1.54 MB)
Ruhnau, P, Stahl, A and Schnörr, C (2007). Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization. Measurement Science and Technology. 18 755-763PDF icon Technical Report (842.06 KB)
Ruhnau, P, Stahl, A and Schnörr, C (2006). On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization. Proc.~DAGM 2006. Springer. 375-388 375-388PDF icon Technical Report (902.47 KB)
Ruhnau, P, Gütter, C, Putze, T and Schnörr, C (2005). A variational approach for particle tracking velocimetry. Meas. Science and Techn. 16 1449-1458
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2004). Variational Optical Flow Estimation for Particle Image Velocimetry. Proc. Lasermethoden in der Strömungsmeßtechnik. Deutsche Gesellschaft für Laser-Anemometrie GALA e.V., Karlsruhe
Ruhnau, P, Kohlberger, T, Nobach, H and Schnörr, C (2005). Variational Optical Flow Estimation for Particle Image Velocimetry. Experiments in Fluids. 38 21–32
Ruhnau, P and Schnörr, C (2007). Optical Stokes Flow Estimation: An Imaging-Based Control Approach. Exp. in Fluids. 42 61–78
Ruhnau, P, Stahl, A and Schnörr, C (2007). Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization. Measurement Science and Technology. 18 755-763
Ruhnau, P, Stahl, A and Schnörr, C (2006). On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization. Proc. DAGM 2006. Springer. 375-388 375-388
Ruiz, A (2021). Deep K-Segments: A Generalization Of K-Means. Heidelberg University
S
Sanakoyeu, A, Ma, P, Tschernezki, V and Ommer, B (2021). Improving Deep Metric Learning by Divide and Conquer. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). https://arxiv.org/abs/2109.04003
Sanakoyeu, A, Bautista, M and Ommer, B (2018). Deep Unsupervised Learning of Visual Similarities. Pattern Recognition. 78. https://authors.elsevier.com/a/1WXUt77nKSb25 PDF icon PDF (8.35 MB)
Sanakoyeu, A, Kotovenko, D, Lang, S and Ommer, B (2018). A Style-Aware Content Loss for Real-time HD Style Transfer. Proceedings of the European Conference on Computer Vision (ECCV) (Oral)
Sanakoyeu, A, Tschernezki, V, Büchler, U and Ommer, B (2019). Divide and Conquer the Embedding Space for Metric Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://github.com/CompVis/metric-learning-divide-and-conquer
E Sanmartin, F, Damrich, S and Hamprecht, F A (2019). Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning. Advances in Neural Information Processing Systems
Saracoglu, K (2001). Bildanalyse von M-FISH. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg. http://www.ub.uni-heidelberg.de/archiv/1805/
Sauer, P (2008). Pattern Recognition On Statistically Textured Surfaces. University of Heidelberg
Saussen, B (2007). Retention Time Domain Registration Of Liquid Chromatography/Mass Spectrometry Data. University of Heidelberg
Savarino, F and Schnörr, C (2019). Continuous-Domain Assignment Flows. preprint: arXiv. https://arxiv.org/abs/1910.07287
Savarino, F and Schnörr, C (2019). A Variational Perspective on the Assignment Flow. Proc. SSVM. Springer
Savarino, F, Hühnerbein, R, Aström, F, Recknagel, J and Schnörr, C (2017). Numerical Integration of Riemannian Gradient Flows for Image Labeling. Proc. SSVM. Springer. 10302
Savchynskyy, B (2019). Discrete Graphical Models — An Optimization Perspective. Foundations and Trends® in Computer Graphics and Vision. Now Publishers. 11 160–429
Savchynskyy, B, Kappes, J H, Schmidt, S and Schnörr, C (2011). A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)
Savchynskyy, B, Kappes, J Hendrik, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS
Savchynskyy, B, Kappes, J H, Schmidt, S and Schnörr, C (2011). A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling. IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), accepted as oral presentation. 1817 - 1823
Savchynskyy, B, Kappes, J H, Swoboda, P and Schnörr, C (2013). Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation. NIPS. Proceedings. 1950-1958
Savchynskyy, B and Schmidt, S (2013). Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study. Workshop on Inference for Probabilistic Graphical Models at ICCV. Proceedings

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