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Ruiz, A (2021). Deep K-Segments: A Generalization Of K-Means. Heidelberg University
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
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 and Schnörr, C (2007). Optical Stokes Flow Estimation: An Imaging-Based Control Approach. Exp. in Fluids. 42 61–78
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, 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, Gütter, C, Putze, T and Schnörr, C (2005). A variational approach for particle tracking velocimetry. Meas. Science and Techn. 16 1449-1458
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, 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 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, 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)
Rudigier, E (2000). Entwicklung Eines Automatisierten Bildverarbeitungssystems Zur Auswertung Unregelmäßiger Bildpunkte Auf Dna-Chips. University of Heidelberg
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)
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)
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
Rother, C and Carlsson, S (2001). Linear multi view reconstruction and camera recovery. Proceedings of the IEEE International Conference on Computer Vision. 1 42–49
Rother, C, Kolmogorov, V, Minka, T and Blake, A (2006). Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1 994–1000. http://research.microsoft.com/vision/cambridge/
Rother, C, Carlsson, S and Tell, D (2002). Projective factorization of planes and cameras in multiple views. Proceedings - International Conference on Pattern Recognition. 16 737–740
Rother, C (2003). Linear Multi-View Reconstruction for Translating Cameras. Nada.Kth.Se. http://www.nada.kth.se/ carstenr/papers/paper_ssab03.pdf
Rother, C and Carlsson, S (2002). Linear multi view reconstruction with missing data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2351 209–324
Rother, C and Carlsson, S (2002). Linear multi view reconstruction and camera recovery using a reference plane. International Journal of Computer Vision. 49 117–141
Rother, C (2002). A new approach to vanishing point detection in architectural environments. Image and Vision Computing. 20 647–655
Rother, C, Kolmogorov, V, Lempitsky, V and Szummer, M (2007). Optimizing Binary MRFs via Extended Roof Duality Technical Report MSR-TR-2007-46. Computing. http://research.microsoft.com/vision/cambridge/
Rother, C, Kumar, S, Kolmogorov, V and Blake, A (2005). Digital tapestry [automatic image synthesis]. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. 1 589–596. http://research.microsoft.com/ http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1467321%5Cnhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467321
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
Rother, C, Kolmogorov, V, Lempitsky, V and Szummer, M (2007). Optimizing binary MRFs via extended roof duality. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Rother, C (2003). Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane. Proceedings of the IEEE International Conference on Computer Vision. 2 1210–1217. http://www.nada.kth.se/carstenr
Rother, C (2011). Sparse Higher Order Functions of Discrete Variables–-Representation and Optimization. research.microsoft.com. 45. http://research.microsoft.com/pubs/147370/RotherKohli-SparseHigherOrder.pdf
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, Kohli, P, Feng, W and Jia, J (2010). Minimizing sparse higher order energy functions of discrete variables. 1382–1389
Roth, N (2018). Visualization Of Near-Surface Flow Patterns For Air-Water Gas Transfer. Institut für Umweltphysik, Universität Heidelberg, Germany
Roth, K, Milbich, T, Ommer, B, Cohen, J Paul and Ghassemi, M (2021). S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning. Proceedings of International Conference on Machine Learning (ICML). https://arxiv.org/abs/2009.08348
Roth, K, Milbich, T, Sinha, S, Gupta, P, Ommer, B and Cohen, J Paul (2020). Revisiting Training Strategies and Generalization Performance in Deep Metric Learning. International Conference on Machine Learning (ICML). https://arxiv.org/pdf/2002.08473.pdf
Roth, V and Ommer, B (2006). Exploiting Low-level Image Segmentation for Object Recognition. Pattern Recognition, Symposium of the DAGM. Springer. 4174 11--20PDF icon Technical Report (473.84 KB)

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