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2015
F. Michel, Krull, A., Brachmann, E., Yang, M. Ying, Gumhold, S., and Rother, C., “Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression”, 2015, pp. 181.1–181.11.
R. Nair, Fitzgibbon, A., Kondermann, D., and Rother, C., “Reflection modeling for passive stereo”, in Proceedings of the IEEE International Conference on Computer Vision, 2015, vol. 2015 Inter, pp. 2291–2299.
A. Zouhar, Rother, C., and Fuchs, S., “Semantic 3-D labeling of ear implants using a global parametric transition prior”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9350, pp. 177–184.
D. Richmond, Kainmueller, D., Glocker, B., Rother, C., and Myers, G., “Uncertainty-driven forest predictors for vertebra localization and segmentation”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9349. pp. 653–660, 2015.
2014
D. Kainmueller, Jug, F., Rother, C., and Myers, G., “Active graph matching for automatic joint segmentation and annotation of C. elegans”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8673 LNCS, pp. 81–88.
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”, CoRR, vol. abs/1404.0533, 2014.PDF icon Technical Report (3.32 MB)
S. Zheng, Cheng, M. Ming, Warrell, J., Sturgess, P., Vineet, V., Rother, C., and Torr, P. H. S., “Dense semantic image segmentation with objects and attributes”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3214–3221.
M. Hornáček, Besse, F., Kautz, J., Fitzgibbon, A., and Rother, C., “Highly overparameterized optical flow using PatchMatch belief propagation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8691 LNCS, pp. 220–234.
M. Hoai, Torresani, L., De La Torre, F., and Rother, C., “Learning discriminative localization from weakly labeled data”, in Pattern Recognition, 2014, vol. 47, pp. 1523–1534.
P. Márquez-Neila, Kohli, P., Rother, C., and Baumela, L., “Non-parametric higher-order random fields for image segmentation”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2014, vol. 8694 LNCS, pp. 269–284.
F. Jug, Pietzsch, T., Kainmüller, D., Funke, J., Kaiser, M., van Nimwegen, E., Rother, C., and Myers, G., “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), vol. 8677, pp. 25–36, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
F. Besse, Rother, C., Fitzgibbon, A., and Kautz, J., “PMBP: PatchMatch Belief Propagation for correspondence field estimation”, International Journal of Computer Vision, vol. 110, pp. 2–13, 2014.
M. Hornáček, Fitzgibbon, A., and Rother, C., “SphereFlow: 6 DoF scene flow from RGB-D pairs”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2014, pp. 3526–3533.
2013
M. Sindeev, Konushin, A., and Rother, C., “Alpha-flow for video matting”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, vol. 7726 LNCS, pp. 438–452.
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.PDF icon 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.PDF icon Technical Report (1.35 MB)
M. Hornáček, Rhemann, C., Gelautz, M., and Rother, C., “Depth super resolution by rigid body self-similarity in 3D”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2013, pp. 1123–1130.
U. Schmidt, Rother, C., Nowozin, S., Jancsary, J., and Roth, S., “Discriminative Non-blind Deblurring”, 2013.
A. Hosni, Rhemann, C., Bleyer, M., Rother, C., and Gelautz, M., “Fast cost-volume filtering for visual correspondence and beyond”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, pp. 504–511, 2013.
V. Vineet, Rother, C., and Torr, P. H. S., “Higher order priors for joint intrinsic image, objects, and attributes estimation”, in Advances in Neural Information Processing Systems, 2013.
J. Jancsary, Nowozin, S., and Rother, C., “Learning convex QP relaxations for structured prediction”, in 30th International Conference on Machine Learning, ICML 2013, 2013, pp. 1952–1960.
2012
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. 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.
M. Bleyer, Rhemann, C., and Rother, C., “Extracting 3D scene-consistent object proposals and depth from stereo images”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7576 LNCS, pp. 467–481.
M. Bleyer, Rhemann, C., and Rother, C., “Extracting 3D scene-consistent object proposals and depth from stereo images”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7576 LNCS, pp. 467–481.
J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.
J. Jancsary, Nowozin, S., and Rother, C., “Loss-specific training of non-parametric image restoration models: A new state of the art”, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2012, vol. 7578 LNCS, pp. 112–125.
J. Jancsary, Nowozin, S., and Rother, C., “Non-parametric crfs for image labeling”, in NIPS Workshop Modern Nonparametric Methods in Machine Learning, 2012, pp. 1–5.
J. Jancsary, Nowozin, S., Sharp, T., and Rother, C., “Regression Tree Fields An efficient, non-parametric approach to image labeling problems”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 2376–2383.
J. Jancsary, Nowozin, S., Sharp, T., and Rother, C., “Regression Tree Fields An efficient, non-parametric approach to image labeling problems”, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2012, pp. 2376–2383.

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