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C. Rother, “Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane”. 2003.
C. Rother, Kolmogorov, V., and Blake, A., “"GrabCut" - Interactive foreground extraction using iterated graph cuts”, in ACM Transactions on Graphics, 2004, vol. 23, pp. 309–314.
C. Rother and Kolmogorov, V., “Interactive foreground extraction using graph cut”, Advances in Markov \ldots, pp. 1–20, 2011.
L. A. Royer, Richmond, D. L., Rother, C., Andres, B., and Kainmueller, D., “Convexity shape constraints for image segmentation”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 402–410.
J. C. Rubio and Ommer, B., “Regularizing Max-Margin Exemplars by Reconstruction and Generative Models”, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, p. 4213--4221.PDF icon Technical Report (2.8 MB)
J. C. Rubio, Eigenstetter, A., and Ommer, B., “Generative Regularization with Latent Topics for Discriminative Object Recognition”, Pattern Recognition, vol. 48, p. 3871--3880, 2015.PDF icon Technical Report (5.49 MB)
E. Rudigier, “Entwicklung eines automatisierten Bildverarbeitungssystems zur Auswertung unregelmäßiger Bildpunkte auf DNA-Chips”, University of Heidelberg, 2000.
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Experiments in Fluids, vol. 38, p. 21--32, 2005.PDF icon Technical Report (1.21 MB)
P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp.~in Fluids, vol. 42, p. 61--78, 2007.PDF icon Technical Report (1.54 MB)
P. Ruhnau, Stahl, A., and Schnörr, C., “Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization”, Measurement Science and Technology, vol. 18, pp. 755-763, 2007.PDF icon Technical Report (842.06 KB)
P. Ruhnau, Stahl, A., and Schnörr, C., “On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization”, in Proc.~DAGM 2006, 2006, vol. 375-388, pp. 375-388.PDF icon Technical Report (902.47 KB)
P. Ruhnau, Gütter, C., Putze, T., and Schnörr, C., “A variational approach for particle tracking velocimetry”, Meas. Science and Techn., vol. 16, pp. 1449-1458, 2005.
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Proc. Lasermethoden in der Strömungsmeßtechnik. Deutsche Gesellschaft für Laser-Anemometrie GALA e.V., Karlsruhe, 2004.
P. Ruhnau, Kohlberger, T., Nobach, H., and Schnörr, C., “Variational Optical Flow Estimation for Particle Image Velocimetry”, Experiments in Fluids, vol. 38, pp. 21–32, 2005.
P. Ruhnau and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp. in Fluids, vol. 42, pp. 61–78, 2007.
P. Ruhnau, Stahl, A., and Schnörr, C., “Variational Estimation of Experimental Fluid Flows with Physics-Based Spatio-Temporal Regularization”, Measurement Science and Technology, vol. 18, pp. 755-763, 2007.
P. Ruhnau, Stahl, A., and Schnörr, C., “On-Line Variational Estimation of Dynamical Fluid Flows with Physics-Based Spatio-Temporal Regularization”, in Proc. DAGM 2006, 2006, vol. 375-388, pp. 375-388.
A. Ruiz, “Deep k-segments: a generalization of k-means”, Heidelberg University, 2021.
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A. Sanakoyeu, Ma, P., Tschernezki, V., and Ommer, B., “Improving Deep Metric Learning by Divide and Conquer”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021.
A. Sanakoyeu, Bautista, M., and Ommer, B., “Deep Unsupervised Learning of Visual Similarities”, Pattern Recognition, vol. 78, 2018.PDF icon PDF (8.35 MB)
A. Sanakoyeu, Kotovenko, D., Lang, S., and Ommer, B., “A Style-Aware Content Loss for Real-time HD Style Transfer”, in Proceedings of the European Conference on Computer Vision (ECCV) (Oral), 2018.
A. Sanakoyeu, Tschernezki, V., Büchler, U., and Ommer, B., “Divide and Conquer the Embedding Space for Metric Learning”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2019.
F. E Sanmartin, Damrich, S., and Hamprecht, F. A., “Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning”, in Advances in Neural Information Processing Systems, 2019.
K. Saracoglu, “Bildanalyse von M-FISH”. IWR, Fakultät für Physik und Astronomie, Univ.\ Heidelberg, 2001.
P. Sauer, “Pattern Recognition on Statistically Textured Surfaces”, University of Heidelberg, 2008.
B. Saussen, “Retention Time Domain Registration of Liquid Chromatography/Mass Spectrometry Data”, University of Heidelberg, 2007.
F. Savarino and Schnörr, C., “Continuous-Domain Assignment Flows”, preprint: arXiv, 2019.
F. Savarino and Schnörr, C., “A Variational Perspective on the Assignment Flow”, in Proc. SSVM, 2019.
F. Savarino, Hühnerbein, R., Aström, F., Recknagel, J., and Schnörr, C., “Numerical Integration of Riemannian Gradient Flows for Image Labeling”, in Proc. SSVM, 2017, vol. 10302.
B. Savchynskyy, “Discrete Graphical Models — An Optimization Perspective”, Foundations and Trends® in Computer Graphics and Vision, vol. 11, pp. 160–429, 2019.
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “A Study of Nesterov's Scheme for Lagrangian Decomposition and MAP Labeling”, in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
B. Savchynskyy, Kappes, J. Hendrik, Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS, 2013.
B. Savchynskyy, Kappes, J. H., Schmidt, S., and Schnörr, C., “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, pp. 1817 - 1823, 2011.
B. Savchynskyy, Kappes, J. H., Swoboda, P., and Schnörr, C., “Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation”, in NIPS. Proceedings, 2013, pp. 1950-1958.
B. Savchynskyy and Schmidt, S., “Getting Feasible Variable Estimates From Infeasible Ones: MRF Local Polytope Study”, in Workshop on Inference for Probabilistic Graphical Models at ICCV. Proceedings, 2013.

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