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A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.

A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.

A. Shekhovtsov, Kohli, P., and Rother, C., “Curvature prior for MRF-based segmentation and shape inpainting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7476 LNCS, pp. 41–51.

D. Schlesinger, Jug, F., Myers, G., Rother, C., and Kainmueller, D., “Crowd sourcing image segmentation with iaSTAPLE”, in Proceedings - International Symposium on Biomedical Imaging, 2017, pp. 401–405.

S. Vicente, Kolmogorov, V., and Rother, C., “Cosegmentation revisited: Models and optimization”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010, vol. 6312 LNCS, pp. 465–479.

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.

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.

F. Kluger, Brachmann, E., Ackermann, H., Rother, C., Yang, M. Ying, and Rosenhahn, B., “CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus”, in CVPR 2020, 2020.

PDF (9.95 MB)

A. Arnab, Zheng, S., Jayasumana, S., Romera-paredes, B., Kirillov, A., Savchynskyy, B., Rother, C., Kahl, F., and Torr, P., “Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation”, Cvpr, vol. XX, pp. 1–15, 2018.

V. Kolmogorov and Rother, C., “Comparison of energy minimization algorithms for highly connected graphs”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 3952 LNCS, pp. 1–15.

V. Kolmogorov and Rother, C., “Comparison of energy minimization algorithms for highly connected graphs”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2006, vol. 3952 LNCS, pp. 1–15.

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, pp. 1-30, 2015.

Technical Report (1.5 MB)

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, 2014.

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, Int.~J.~Comp.~Vision, 2015.

Technical Report (5.12 MB)

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, CoRR, vol. abs/1404.0533, 2014.

Technical Report (3.32 MB)

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.

J. H. Kappes, 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., “A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems”, International Journal of Computer Vision, vol. 115, pp. 155–184, 2015.

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Sungwoong, K., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problems”, in CVPR 2013. Proceedings, 2013.

Technical Report (1.35 MB)

J. H. Kappes, Andres, B., Hamprecht, F. A., Schnörr, C., Nowozin, S., Batra, D., Kim, S., Kausler, B. X., Lellmann, J., Komodakis, N., and Rother, C., “A Comparative Study of Modern Inference Techniques for Discrete Energy Minimization Problem”, in CVPR, 2013.

Technical Report (1.35 MB)

R. Szeliski, Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1068–1080, 2008.

R. Szeliski, Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., and Rother, C., “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, pp. 1068–1080, 2008.

A. Kannan, Winn, J., and Rother, C., “Clustering appearance and shape by learning jigsaws”, in Advances in Neural Information Processing Systems, 2007, pp. 657–664.

A. Kannan, Winn, J., and Rother, C., “Clustering appearance and shape by learning jigsaws”, in Advances in Neural Information Processing Systems, 2007, pp. 657–664.

R. Mackowiak, Lenz, P., Ghori, O., Diego, F., Lange, O., and Rother, C., “CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation”, in British Machine Vision Conference 2018, BMVC 2018, 2019.

S. Karthik Mustikovela, Yang, M. Ying, and Rother, C., “Can ground truth label propagation from video help semantic segmentation?”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, vol. 9915 LNCS, pp. 804–820.

B

V. Lempitsky, Blake, A., and Rother, C., “Branch-and-mincut: Global optimization for image segmentation with high-level priors”, Journal of Mathematical Imaging and Vision, vol. 44, pp. 315–329, 2012.

A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.

A. Behl, Hosseini Jafari, O., Mustikovela, S. Karthik, Abu Alhaija, H., Rother, C., and Geiger, A., “Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?”, in Proceedings of the IEEE International Conference on Computer Vision, 2017, vol. 2017-Octob, pp. 2593–2602.

T. Hodaň, Michel, F., Brachmann, E., Kehl, W., Buch, A. Glent, Kraft, D., Drost, B., Vidal, J., Ihrke, S., Zabulis, X., Sahin, C., Manhardt, F., Tombari, F., Kim, T. Kyun, Matas, J., and Rother, C., “BOP: Benchmark for 6D object pose estimation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11214 LNCS, pp. 19–35.

V. Kolmogorov, Criminisi, A., Blake, A., Cross, G., and Rother, C., “Bi-layer segmentation of binocular stereo video”, in Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, 2005, vol. II, pp. 407–414.

C. Kamann and Rother, C., “Benchmarking the Robustness of Semantic Segmentation Models”, 2019.

J. Kruse, Ardizzone, L., Rother, C., and Köthe, U., “Benchmarking Invertible Architectures on Inverse Problems”, i, 2019.

P. Vincent Gehler, Rother, C., Blake, A., Minka, T., and Sharp, T., “Bayesian color constancy revisited”, in 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR, 2008.

A

H. Abu Alhaija, Mustikovela, S. Karthik, Mescheder, L., Geiger, A., and Rother, C., “Augmented reality meets deep learning for car instance segmentation in urban scenes”, in British Machine Vision Conference 2017, BMVC 2017, 2017.

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