Publications
A
Kainmueller, D, Jug, F, Rother, C and Myers, G (2014).
Active graph matching for automatic joint segmentation and annotation of C. elegans.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
8673 LNCS 81–88
Sindeev, M, Konushin, A and Rother, C (2013).
Alpha-flow for video matting.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
7726 LNCS 438–452
Kolmogorov, V, Boykov, Y and Rother, C (2007).
Applications of parametric maxflow in computer vision.
Proceedings of the IEEE International Conference on Computer Vision Kolmogorov, V, Boykov, Y and Rother, C (2007).
Applications of parametric maxflow in computer vision.
Proceedings of the IEEE International Conference on Computer Vision B
Gehler, P Vincent, Rother, C, Blake, A, Minka, T and Sharp, T (2008).
Bayesian color constancy revisited.
26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR Hodaň, T, 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 (2018).
BOP: Benchmark for 6D object pose estimation.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
11214 LNCS 19–35.
http://arxiv.org/abs/1808.08319 Behl, A, Hosseini Jafari, O, Mustikovela, S Karthik, Abu Alhaija, H, Rother, C and Geiger, A (2017).
Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?.
Proceedings of the IEEE International Conference on Computer Vision.
2017-Octob 2593–2602
Behl, A, Hosseini Jafari, O, Mustikovela, S Karthik, Abu Alhaija, H, Rother, C and Geiger, A (2017).
Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?.
Proceedings of the IEEE International Conference on Computer Vision.
2017-Octob 2593–2602
C
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
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 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
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. 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. Proceedings 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.
CVPR Technical Report (1.35 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. 1-30
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.~Vision 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 (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/ 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 (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/ 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 (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
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
Pages