Publications

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Journal Article
Kohli, P, Nickisch, H, Rother, C and Rhemann, C (2012). User-centric learning and evaluation of interactive segmentation systems. International Journal of Computer Vision. 100 261–274
Shotton, J, Winn, J, Rother, C and Criminisi, A (2009). TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision. 81 2–23. http://jamie.shotton.org/work/code.html
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
Hullin, M, Klein, R, Schultz, T, Yao, A, Li, W, Hosseini Jafari, O and Rother, C (2017). Semantic-Aware Image Smoothing. Vision, Modeling, and Visualization. https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf
Bhowmik, A, Gumhold, S, Rother, C and Brachmann, E (2019). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. http://arxiv.org/abs/1912.00623
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
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
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
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/
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
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 and camera recovery using a reference plane. International Journal of Computer Vision. 49 117–141
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
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392
Ardizzone, L, Lüth, C, Kruse, J, Rother, C and Köthe, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392
Blake, A, Criminisi, A, Cross, G, Kolmogorov, V and Rother, C (2007). Fusion of stereo, colour and contrast. Springer Tracts in Advanced Robotics. 28. www.research.microsoft.com/vision/cambridge
Lempitsky, V, Rother, C, Roth, S and Blake, A (2010). Fusion moves for markov random field optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 1392–1405
Lempitsky, V, Rother, C, Roth, S and Blake, A (2010). Fusion moves for markov random field optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 1392–1405
Hosni, A, Rhemann, C, Bleyer, M, Rother, C and Gelautz, M (2013). Fast cost-volume filtering for visual correspondence and beyond. IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 504–511
Ardizzone, L, Mackowiak, R, Rother, C and Köthe, U (2020). Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling. http://arxiv.org/abs/2001.06448PDF icon PDF (2.87 MB)
Criminisi, A, Blake, A, Rother, C, Shotton, J and Torr, P H S (2007). Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming. International Journal of Computer Vision. Kluwer Academic Publishers. 71 89–110
Arnab, A, Zheng, S, Jayasumana, S, Romera-paredes, B, Kirillov, A, Savchynskyy, B, Rother, C, Kahl, F and Torr, P (2018). Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation. Cvpr. XX 1–15. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889&rep=rep1&type=pdf%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. abs/1404.0533. http://hci.iwr.uni-heidelberg.de/opengm2/PDF icon Technical Report (3.32 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kröger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. http://arxiv.org/abs/1404.0533

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