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A. Ruiz, “Deep k-segments: a generalization of k-means”, Heidelberg University, 2021.
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.
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 and Schnörr, C., “Optical Stokes Flow Estimation: An Imaging-Based Control Approach”, Exp. in Fluids, vol. 42, pp. 61–78, 2007.
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, 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, 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, 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, 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 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, 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)
E. Rudigier, “Entwicklung eines automatisierten Bildverarbeitungssystems zur Auswertung unregelmäßiger Bildpunkte auf DNA-Chips”, University of Heidelberg, 2000.
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)
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)
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.
C. Rother and Carlsson, S., “Linear multi view reconstruction and camera recovery”, in Proceedings of the IEEE International Conference on Computer Vision, 2001, vol. 1, pp. 42–49.
C. Rother, Kolmogorov, V., Minka, T., and Blake, A., “Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, vol. 1, pp. 994–1000.
C. Rother, Carlsson, S., and Tell, D., “Projective factorization of planes and cameras in multiple views”, in Proceedings - International Conference on Pattern Recognition, 2002, vol. 16, pp. 737–740.
C. Rother, “Linear Multi-View Reconstruction for Translating Cameras”, Nada.Kth.Se, 2003.
C. Rother and Carlsson, S., “Linear multi view reconstruction with missing data”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2351, pp. 209–324.
C. Rother and Carlsson, S., “Linear multi view reconstruction and camera recovery using a reference plane”, International Journal of Computer Vision, vol. 49, pp. 117–141, 2002.
C. Rother, “A new approach to vanishing point detection in architectural environments”, in Image and Vision Computing, 2002, vol. 20, pp. 647–655.
C. Rother, Kolmogorov, V., Lempitsky, V., and Szummer, M., “Optimizing Binary MRFs via Extended Roof Duality Technical Report MSR-TR-2007-46”, Computing, 2007.
C. Rother, Kumar, S., Kolmogorov, V., and Blake, A., “Digital tapestry [automatic image synthesis]”, in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, 2005, vol. 1, pp. 589–596.
C. Rother and Kolmogorov, V., “Interactive foreground extraction using graph cut”, Advances in Markov \ldots, pp. 1–20, 2011.
C. Rother, Kolmogorov, V., Lempitsky, V., and Szummer, M., “Optimizing binary MRFs via extended roof duality”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
C. Rother, “Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane”, in Proceedings of the IEEE International Conference on Computer Vision, 2003, vol. 2, pp. 1210–1217.
C. Rother, “Sparse Higher Order Functions of Discrete Variables–-Representation and Optimization”, research.microsoft.com, vol. 45, 2011.
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, Kohli, P., Feng, W., and Jia, J., “Minimizing sparse higher order energy functions of discrete variables”, 2010, pp. 1382–1389.
N. Roth, “Visualization of Near-Surface Flow Patterns for Air-Water Gas Transfer”, Institut für Umweltphysik, Universität Heidelberg, Germany, 2018.
K. Roth, Milbich, T., Ommer, B., Cohen, J. Paul, and Ghassemi, M., “S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning”, Proceedings of International Conference on Machine Learning (ICML). 2021.
K. Roth, Milbich, T., Sinha, S., Gupta, P., Ommer, B., and Cohen, J. Paul, “Revisiting Training Strategies and Generalization Performance in Deep Metric Learning”, International Conference on Machine Learning (ICML). 2020.
V. Roth and Ommer, B., “Exploiting Low-level Image Segmentation for Object Recognition”, in Pattern Recognition, Symposium of the DAGM, 2006, vol. 4174, p. 11--20.PDF icon Technical Report (473.84 KB)

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