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Author Title [ Type(Asc)] Year
Filters: Author is Carsten Rother  [Clear All Filters]
Techreport
D. Singaraju, Rother, C., and Rhemann, C., “Supplementary material for New Appearance Models for Image Matting”, 2009.
A. Sellent, Rother, C., and Roth, S., “Stereo Video Deblurring-Supplemental Material”, 2016.
W. Li, Hosseini Jafari, O., and Rother, C., “Localizing Common Objects Using Common Component Activation Map”, 2019.
J. Kruse, Rother, C., Schmidt, U., and Dresden, T. U., “Learning to Push the Limits of Efficient FFT-based Image Deconvolution - Supplemental Material”, 2017.
U. Schmidt, Rother, C., Nowozin, S., Jancsary, J., and Roth, S., “Discriminative Non-blind Deblurring”, 2013.
J. Kruse, Ardizzone, L., Rother, C., and Köthe, U., “Benchmarking Invertible Architectures on Inverse Problems”, i, 2019.
PhD Thesis
C. Rother, “Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane”. 2003.
Miscellaneous
D. Massiceti, Krull, A., Brachmann, E., Rother, C., and Torr, P. H. S., “Random Forests versus Neural Networks − What's best for camera location”. 2017.
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.
Journal Article
P. Kohli, Nickisch, H., Rother, C., and Rhemann, C., “User-centric learning and evaluation of interactive segmentation systems”, International Journal of Computer Vision, vol. 100, pp. 261–274, 2012.
J. Shotton, Winn, J., Rother, C., and Criminisi, A., “TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context”, International Journal of Computer Vision, vol. 81, pp. 2–23, 2009.
C. Rother, “Sparse Higher Order Functions of Discrete Variables–-Representation and Optimization”, research.microsoft.com, vol. 45, 2011.
M. Hullin, Klein, R., Schultz, T., Yao, A., Li, W., Hosseini Jafari, O., and Rother, C., “Semantic-Aware Image Smoothing”, Vision, Modeling, and Visualization, 2017.
A. Bhowmik, Gumhold, S., Rother, C., and Brachmann, E., “Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task”, 2019.
V. Kolmogorov, Criminisi, A., Blake, A., Cross, G., and Rother, C., “Probabilistic fusion of stereo with color and contrast for bilayer segmentation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 1480–1492, 2006.
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.
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.
C. Rother, Kolmogorov, V., Lempitsky, V., and Szummer, M., “Optimizing Binary MRFs via Extended Roof Duality Technical Report MSR-TR-2007-46”, Computing, 2007.
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, “Linear Multi-View Reconstruction for Translating Cameras”, Nada.Kth.Se, 2003.
C. Rother and Carlsson, S., “Linear multi view reconstruction and camera recovery using a reference plane”, International Journal of Computer Vision, vol. 49, pp. 117–141, 2002.
C. Rother and Kolmogorov, V., “Interactive foreground extraction using graph cut”, Advances in Markov \ldots, pp. 1–20, 2011.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
L. Ardizzone, Lüth, C., Kruse, J., Rother, C., and Köthe, U., “Guided Image Generation with Conditional Invertible Neural Networks”, 2019.
A. Blake, Criminisi, A., Cross, G., Kolmogorov, V., and Rother, C., “Fusion of stereo, colour and contrast”, Springer Tracts in Advanced Robotics, vol. 28, 2007.
V. Lempitsky, Rother, C., Roth, S., and Blake, A., “Fusion moves for markov random field optimization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 1392–1405, 2010.
V. Lempitsky, Rother, C., Roth, S., and Blake, A., “Fusion moves for markov random field optimization”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, pp. 1392–1405, 2010.
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.
L. Ardizzone, Mackowiak, R., Rother, C., and Köthe, U., “Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling”, 2020.PDF icon PDF (2.87 MB)
A. Criminisi, Blake, A., Rother, C., Shotton, J., and Torr, P. H. S., “Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming”, International Journal of Computer Vision, vol. 71, pp. 89–110, 2007.
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.
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”, International Journal of Computer Vision, pp. 1-30, 2015.PDF icon Technical Report (1.5 MB)

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