Publications

Export 185 results:
Author [ Title(Desc)] Type Year
Filters: Author is Carsten Rother  [Clear All Filters]
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z 
L
C. Rother and Carlsson, S., Linear multi view reconstruction with missing data, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2002, vol. 2351, pp. 209–324.
C. Rother, Linear Multi-View Reconstruction for Translating Cameras, Nada.Kth.Se, 2003.
C. Rother, Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane, in Proceedings of the IEEE International Conference on Computer Vision, 2003, vol. 2, pp. 1210–1217.
W. Li, Hosseini Jafari, O., and Rother, C., Localizing Common Objects Using Common Component Activation Map, 2019.
V. Lempitsky, Rother, C., and Blake, A., LogCut - Efficient graph cut optimization for markov random fields, in Proceedings of the IEEE International Conference on Computer Vision, 2007.
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.
P. Pinggera, Ramos, S., Gehrig, S., Franke, U., Rother, C., and Mester, R., Lost and found: Detecting small road hazards for self-driving vehicles, in IEEE International Conference on Intelligent Robots and Systems, 2016, vol. 2016-Novem, pp. 1099–1106.
M
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., Mapping auto-context decision forests to deep convnets for semantic segmentation, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., Mapping auto-context decision forests to deep convnets for semantic segmentation, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
D. L. Richmond, Kainmueller, D., Yang, M. Y., Myers, E. W., and Rother, C., Mapping auto-context decision forests to deep convnets for semantic segmentation, in British Machine Vision Conference 2016, BMVC 2016, 2016, vol. 2016-Septe, pp. 144.1–144.12.
A. Kirillov, Schlesinger, D., Vetrov, D., Rother, C., and Savchynskyy, B., M-best-diverse labelings for submodular energies and beyond, in Advances in Neural Information Processing Systems, 2015, vol. 2015-Janua, pp. 613–621.
V. Kolmogorov and Rother, C., Minimizing nonsubmodular functions with graph cuts - A review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29. pp. 1274–1279, 2007.
C. Rother, Kohli, P., Feng, W., and Jia, J., Minimizing sparse higher order energy functions of discrete variables, 2010, pp. 1382–1389.
S. Tourani, Shekhovtsov, A., Rother, C., and Savchynskyy, B., MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, vol. 11208 LNCS, pp. 264–281.
C. Rother, Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane. 2003.
N
E. Brachmann and Rother, C., Neural-guided RANSAC: Learning where to sample model hypotheses, in Proceedings of the IEEE International Conference on Computer Vision, 2019, vol. 2019-Octob, pp. 4321–4330.PDF icon PDF (8.02 MB)
D. Singaraju, Rother, C., and Rhemann, C., New appearance models for natural image matting, 2010, pp. 659–666.
D. Singaraju, Rother, C., and Rhemann, C., New appearance models for natural image matting, in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 659–666.
C. Rother, A new approach to vanishing point detection in architectural environments, in Image and Vision Computing, 2002, vol. 20, pp. 647–655.
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.
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.
O
S. Vicente, Rother, C., and Kolmogorov, V., Object cosegmentation, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 2217–2224.
S. Zheng, Prisacariu, V. Adrian, Averkiou, M., Cheng, M. Ming, Mitra, N. J., Shotton, J., Torr, P. H. S., and Rother, C., Object proposals estimation in depth image using compact 3D shape manifolds, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2015, vol. 9358, pp. 196–208.
M. Bleyer, Rother, C., Kohli, P., Scharstein, D., and Sinha, S., Object stereo Joint stereo matching and object segmentation, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2011, pp. 3081–3088.
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.
C. Rother, Kolmogorov, V., Lempitsky, V., and Szummer, M., Optimizing binary MRFs via extended roof duality, in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
C. Rother, Kolmogorov, V., Lempitsky, V., and Szummer, M., Optimizing Binary MRFs via Extended Roof Duality Technical Report MSR-TR-2007-46, Computing, 2007.
T. J. Adler, Ayala, L., Ardizzone, L., Kenngott, H. G., Vemuri, A., Müller-Stich, B. P., Rother, C., Köthe, U., and Maier-Hein, L., Out of Distribution Detection for Intra-operative Functional Imaging, in MICCAI UNSURE Workshop 2019, 2019, vol. 11840 LNCS, pp. 75–82.PDF icon PDF (3.1 MB)
P
N. J. Mitra, Stam, J., Xu, K., Cheng, M. - M., Prisacariu, V. Adrian, Zheng, S., Torr, P. H. S., and Rother, C., Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut, vol. 34, 2015.
P. Kohli, Shekhovtsov, A., Rother, C., Kolmogorov, V., and Torr, P., On partial optimality in multi-label MRFs, in Proceedings of the 25th International Conference on Machine Learning, 2008, pp. 480–487.
M. Bleyer, Rhemann, C., and Rother, C., PatchMatch Stereo - Stereo Matching with Slanted Support Windows, 2011, pp. 14.1–14.11.
C. Rhemann, Rother, C., Wang, J., Gelautz, M., Kohli, P., and Rott, P., A perceptually motivated online benchmark for image matting, in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 1826–1833.
C. Rhemann, Rother, C., Wang, J., Gelautz, M., Kohli, P., and Rott, P., A perceptually motivated online benchmark for image matting, in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 1826–1833.
C. Rhemann, Rother, C., Wang, J., Gelautz, M., Kohli, P., and Rott, P., A perceptually motivated online benchmark for image matting, in 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, vol. 2009 IEEE, pp. 1826–1833.

Pages