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Kolmogorov, V, Criminisi, A, Blake, A, Cross, G and Rother, C (2006). Probabilistic fusion of stereo with color and contrast for bilayer segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 1480–1492. http://research.microsoft.com/vision/cambridge
Haller, S, Prakash, M, Hutschenreiter, L, Pietzsch, T, Rother, C, Jug, F, Swoboda, P and Savchynskyy, B (2020). A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. AISTATS 2020PDF icon PDF (1.04 MB)
Krull, A, Brachmann, E, Nowozin, S, Michel, F, Shotton, J and Rother, C (2017). PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 2566–2574. http://arxiv.org/abs/1612.03779
Michel, F, Krull, A, Brachmann, E, Yang, M Ying, Gumhold, S and Rother, C (2015). Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression. 181.1–181.11
Besse, F, Rother, C, Fitzgibbon, A and Kautz, J (2014). PMBP: PatchMatch Belief Propagation for correspondence field estimation. International Journal of Computer Vision. 110 2–13
Besse, F, Rother, C, Fitzgibbon, A and Kautz, J (2014). PMBP: PatchMatch Belief Propagation for correspondence field estimation. International Journal of Computer Vision. 110 2–13
Besse, F, Rother, C, Fitzgibbon, A and Kautz, J (2014). PMBP: PatchMatch Belief Propagation for correspondence field estimation. International Journal of Computer Vision. Kluwer Academic Publishers. 110 2–13
Lalonde, J François, Hoiem, D, Efros, A A, Rother, C, Winn, J and Criminisi, A (2007). Photo clip art. Proceedings of the ACM SIGGRAPH Conference on Computer Graphics. http://graphics.cs.cmu.edu/projects/photoclipart/
Mund, J, Zouhar, A, Meyer, L, Fricke, H and Rother, C (2015). Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons. Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems. 85–94
Mund, J, Zouhar, A, Meyer, L, Fricke, H and Rother, C (2015). Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons. Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems. 85–94
Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826–1833. www.alphamatting.com.
Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826–1833. www.alphamatting.com.
Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826–1833
Bleyer, M, Rhemann, C and Rother, C (2011). PatchMatch Stereo - Stereo Matching with Slanted Support Windows. 14.1–14.11
Kohli, P, Shekhovtsov, A, Rother, C, Kolmogorov, V and Torr, P (2008). On partial optimality in multi-label MRFs. Proceedings of the 25th International Conference on Machine Learning. 480–487
Mitra, N J, Stam, J, Xu, K, Cheng, M - M, Prisacariu, V Adrian, Zheng, S, Torr, P H S and Rother, C (2015). Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut. 34. http://mftp.mmcheng.net/Papers/DenseCut.pdf
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Adler, T J, Ayala, L, Ardizzone, L, Kenngott, H G, Vemuri, A, Müller-Stich, B P, Rother, C, Köthe, U and Maier-Hein, L (2019). Out of Distribution Detection for Intra-operative Functional Imaging. MICCAI UNSURE Workshop 2019. 11840 LNCS 75–82PDF icon PDF (3.1 MB)
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, 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
Jug, F, Pietzsch, T, Kainmüller, D, Funke, J, Kaiser, M, van Nimwegen, E, Rother, C and Myers, G (2014). Optimal joint segmentation and tracking of escherichia coli in the mother machine. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8677 25–36
Bleyer, M, Rother, C, Kohli, P, Scharstein, D and Sinha, S (2011). Object stereo Joint stereo matching and object segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3081–3088
Zheng, S, Prisacariu, V Adrian, Averkiou, M, Cheng, M Ming, Mitra, N J, Shotton, J, Torr, P H S and Rother, C (2015). Object proposals estimation in depth image using compact 3D shape manifolds. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9358 196–208
Vicente, S, Rother, C and Kolmogorov, V (2011). Object cosegmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2217–2224
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Márquez-Neila, P, Kohli, P, Rother, C and Baumela, L (2014). Non-parametric higher-order random fields for image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8694 LNCS 269–284
Jancsary, J, Nowozin, S and Rother, C (2012). Non-parametric crfs for image labeling. NIPS Workshop Modern Nonparametric Methods in Machine Learning. 1–5. http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf
Rother, C (2002). A new approach to vanishing point detection in architectural environments. Image and Vision Computing. 20 647–655
Singaraju, D, Rother, C and Rhemann, C (2010). New appearance models for natural image matting. 659–666
Singaraju, D, Rother, C and Rhemann, C (2009). New appearance models for natural image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 659–666
Brachmann, E and Rother, C (2019). Neural-guided RANSAC: Learning where to sample model hypotheses. Proceedings of the IEEE International Conference on Computer Vision. 2019-Octob 4321–4330. http://arxiv.org/abs/1905.04132PDF icon PDF (8.02 MB)
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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.
Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2018). MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 264–281
Rother, C, Kohli, P, Feng, W and Jia, J (2010). Minimizing sparse higher order energy functions of discrete variables. 1382–1389
Kolmogorov, V and Rother, C (2007). Minimizing nonsubmodular functions with graph cuts - A review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29 1274–1279
Kirillov, A, Schlesinger, D, Vetrov, D, Rother, C and Savchynskyy, B (2015). M-best-diverse labelings for submodular energies and beyond. Advances in Neural Information Processing Systems. 2015-Janua 613–621
Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1–144.12

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