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Filters: Author is Carsten Rother and First Letter Of Title is C  [Clear All Filters]
2020
Kluger, F, Brachmann, E, Ackermann, H, Rother, C, Yang, M Ying and Rosenhahn, B (2020). CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. CVPR 2020. http://arxiv.org/abs/2001.02643PDF icon PDF (9.95 MB)
2019
Mackowiak, R, Lenz, P, Ghori, O, Diego, F, Lange, O and Rother, C (2019). CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation. British Machine Vision Conference 2018, BMVC 2018
2018
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
2017
Schlesinger, D, Jug, F, Myers, G, Rother, C and Kainmueller, D (2017). Crowd sourcing image segmentation with iaSTAPLE. Proceedings - International Symposium on Biomedical Imaging. 401–405
2016
Mustikovela, S Karthik, Yang, M Ying and Rother, C (2016). Can ground truth label propagation from video help semantic segmentation?. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9915 LNCS 804–820
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
2015
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 (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 1-30PDF icon Technical Report (1.5 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 (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. Int.~J.~Comp.~VisionPDF icon Technical Report (5.12 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 (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155–184
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 (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155–184
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 (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155–184. http://hci.iwr.uni-heidelberg.de/opengm2/
2014
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
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)
2013
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Sungwoong, K, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems. CVPR 2013. ProceedingsPDF icon Technical Report (1.35 MB)
Kappes, J H, Andres, B, Hamprecht, F A, Schnörr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Lellmann, J, Komodakis, N and Rother, C (2013). A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem. CVPRPDF icon Technical Report (1.35 MB)
2012
Shekhovtsov, A, Kohli, P and Rother, C (2012). Curvature prior for MRF-based segmentation and shape inpainting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7476 LNCS 41–51
Shekhovtsov, A, Kohli, P and Rother, C (2012). Curvature prior for MRF-based segmentation and shape inpainting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7476 LNCS 41–51. www.research.microsoft.com/vision/cambridge http://www.cs.ucl.ac.uk/staff/V.Kolmogorov/papers/StereoSegmentation_PAMI06.pdf%5Cnpapers3://publication/uuid/F008E9F4-510D-4478-A3C0-1BFB22F6AEA0
Shekhovtsov, A, Kohli, P and Rother, C (2012). Curvature prior for MRF-based segmentation and shape inpainting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7476 LNCS 41–51. http://arxiv.org/abs/1109.1480
2010
Vicente, S, Kolmogorov, V and Rother, C (2010). Cosegmentation revisited: Models and optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6312 LNCS 465–479
2008
Szeliski, R, Zabih, R, Scharstein, D, Veksler, O, Kolmogorov, V, Agarwala, A, Tappen, M and Rother, C (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30 1068–1080
Szeliski, R, Zabih, R, Scharstein, D, Veksler, O, Kolmogorov, V, Agarwala, A, Tappen, M and Rother, C (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. Springer-Verlag. 30 1068–1080. http://vision.middlebury.edu/MRF.
2007
Kannan, A, Winn, J and Rother, C (2007). Clustering appearance and shape by learning jigsaws. Advances in Neural Information Processing Systems. 657–664
Kannan, A, Winn, J and Rother, C (2007). Clustering appearance and shape by learning jigsaws. Advances in Neural Information Processing Systems. 657–664
2006
Kolmogorov, V and Rother, C (2006). Comparison of energy minimization algorithms for highly connected graphs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3952 LNCS 1–15
Kolmogorov, V and Rother, C (2006). Comparison of energy minimization algorithms for highly connected graphs. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3952 LNCS 1–15
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/