All Publications

2015

Abu Alhaija, H, Sellent, A, Kondermann, D and Rother, C (2015). Graphflow—6D large displacement scene flow via graph matching. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9358 285–296
Schelten, K, Nowozin, S, Jancsary, J, Rother, C and Roth, S (2015). Interleaved regression tree field cascades for blind image deconvolution. Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015. 494–501
Mund, J, Zouhar, A, Meyer, L, Fricke, H and Rother, C (2015). Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons. Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems. 85–94
Mund, J, Zouhar, A, Meyer, L, Fricke, H and Rother, C (2015). Performance evaluation of LiDAR point clouds towards automated FOD detection on airport aprons. Proceedings of ATACCS 2015 - 5th International Conference on Application and Theory of Automation in Command and Control Systems. 85–94
Nair, R, Fitzgibbon, A, Kondermann, D and Rother, C (2015). Reflection modeling for passive stereo. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter 2291–2299
Kirillov, A, Savchynskyy, B, Schlesinger, D, Vetrov, D and Rother, C (2015). Inferring M-best diverse labelings in a single one. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter 1814–1822
Krull, A, Brachmann, E, Michel, F, Yang, M Ying, Gumhold, S and Rother, C (2015). Learning analysis-by-synthesis for 6d pose estimation in RGB-D images. Proceedings of the IEEE International Conference on Computer Vision. 2015 Inter 954–962

2014

Horn, M (2014). Arriving at Z / f. 1–2
Eyjolfsdottir, E, Branson, S, Burgos-Artizzu, X P, Hoopfer, E D, Schor, J, Anderson, D J and Perona, P (2014). Detection of social actions in fruit flies. Lecture Notes in Computer Science. Springer International Publishing, Cham. 8690 772–787. http://link.springer.com/10.1007/978-3-319-10605-2 http://www.ncbi.nlm.nih.gov/pubmed/31629782
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
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
Márquez-Neila, P, Kohli, P, Rother, C and Baumela, L (2014). Non-parametric higher-order random fields for image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8694 LNCS 269–284
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
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
Hornáček, M, Besse, F, Kautz, J, Fitzgibbon, A and Rother, C (2014). Highly overparameterized optical flow using PatchMatch belief propagation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 8691 LNCS 220–234
He, X, Wang, H, Zhang, F, Wang, G and Zhou, K (2014). Robust Simulation of Small-Scale Thin Features in SPH-based Free Surface Flows. Life.Kunzhou.Net. 1 1–8. http://doi.acm.org/10.1145/XXXXXXX.YYYYYYY http://life.kunzhou.net/2013/SPHsurfacetension.pdf
Hoai, M, Torresani, L, De La Torre, F and Rother, C (2014). Learning discriminative localization from weakly labeled data. Pattern Recognition. 47 1523–1534
Hornáček, M, Fitzgibbon, A and Rother, C (2014). SphereFlow: 6 DoF scene flow from RGB-D pairs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3526–3533
Zheng, S, Cheng, M Ming, Warrell, J, Sturgess, P, Vineet, V, Rother, C and Torr, P H S (2014). Dense semantic image segmentation with objects and attributes. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3214–3221. http://www.robots.ox.ac.uk/˜tvg/http://tu-dresden.de/inf/cvld
Distributions, L (2014). Proof of Lemma 2 Proof of Lemma 3 Proof of Theorem 4 Proof of Lemma 10. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics. 9–11

2013

Schmidt, U, Rother, C, Nowozin, S, Jancsary, J and Roth, S (2013). Discriminative Non-Blind Deblurring
Jancsary, J, Nowozin, S and Rother, C (2013). Learning convex QP relaxations for structured prediction. 30th International Conference on Machine Learning, ICML 2013. 1952–1960
Vineet, V, Rother, C and Torr, P H S (2013). Higher order priors for joint intrinsic image, objects, and attributes estimation. Advances in Neural Information Processing Systems
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
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
Hornáček, M, Rhemann, C, Gelautz, M and Rother, C (2013). Depth super resolution by rigid body self-similarity in 3D. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1123–1130

2012

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
Lempitsky, V, Blake, A and Rother, C (2012). Branch-and-mincut: Global optimization for image segmentation with high-level priors. Journal of Mathematical Imaging and Vision. 44 315–329
Mansfield, A, Gehler, P, Van Gool, L and Rother, C (2012). Visibility maps for improving seam carving. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6554 LNCS 131–144. http://www.adobe.com/products/photoshop/photoshopextended/features/
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
Bleyer, M, Rhemann, C and Rother, C (2012). Extracting 3D scene-consistent object proposals and depth from stereo images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7576 LNCS 467–481. http://vision.middlebury.edu/stereo/
Bleyer, M, Rhemann, C and Rother, C (2012). Extracting 3D scene-consistent object proposals and depth from stereo images. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7576 LNCS 467–481
Jancsary, J, Nowozin, S and Rother, C (2012). Loss-specific training of non-parametric image restoration models: A new state of the art. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7578 LNCS 112–125
Jancsary, J, Nowozin, S and Rother, C (2012). Loss-specific training of non-parametric image restoration models: A new state of the art. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7578 LNCS 112–125
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
Jancsary, J, Nowozin, S and Rother, C (2012). Non-parametric crfs for image labeling. NIPS Workshop Modern Nonparametric Methods in Machine Learning. 1–5. http://www.nowozin.net/sebastian/papers/jancsary2012nonparametriccrf.pdf
Meister, S, Izadi, S, Kohli, P and M Hämmerle, M \ (2012). When can we use KinectFusion for ground truth acquisition?. Proc Workshop on \ldots. 3–8. http://meshlab.sourceforge.net/ http://www.msr-waypoint.net/en-us/um/people/pkohli/papers/mikhrk_iros_dataset_2012.pdf%5Cnpapers3://publication/uuid/2615CF9D-C632-4E39-B1C4-B32A4A5D339C
Jancsary, J, Nowozin, S, Sharp, T and Rother, C (2012). Regression Tree Fields An efficient, non-parametric approach to image labeling problems. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2376–2383

Pages