Publications

Export 185 results:
Author Title [ Type(Asc)] Year
Filters: Author is Carsten Rother  [Clear All Filters]
Conference Paper
Abu Alhaija, H, Mustikovela, S Karthik, Geiger, A and Rother, C (2019). Geometric Image Synthesis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11366 LNCS 85–100. https://youtu.be/W2tFCz9xJoU
Gulshan, V, Rother, C, Criminisi, A, Blake, A and Zisserman, A (2010). Geodesic star convexity for interactive image segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3129–3136
Lempitsky, V, Roth, S and Rother, C (2008). FusionFlow: Discrete-continuous optimization for optical flow estimation. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR
Torresani, L, Kolmogorov, V and Rother, C (2008). Feature correspondence via graph matching: Models and global optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5303 LNCS 596–609
Torresani, L, Kolmogorov, V and Rother, C (2008). Feature correspondence via graph matching: Models and global optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5303 LNCS 596–609
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
Brachmann, E and Rother, C (2019). Expert sample consensus applied to camera re-localization. Proceedings of the IEEE International Conference on Computer Vision. 2019-Octob 7524–7533. http://arxiv.org/abs/1908.02484
Brachmann, E, Krull, A, Nowozin, S, Shotton, J, Michel, F, Gumhold, S and Rother, C (2017). DSAC - Differentiable RANSAC for camera localization. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 2492–2500. http://arxiv.org/abs/1611.05705
Sellen, A, Fogg, A, Aitken, M, Hodges, S, Rother, C and Wood, K (2007). Do life-logging technologies support memory for the past? An experimental study using sensecam. Conference on Human Factors in Computing Systems - Proceedings. 81–90
Rother, C, Kumar, S, Kolmogorov, V and Blake, A (2005). Digital tapestry [automatic image synthesis]. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. 1 589–596. http://research.microsoft.com/ http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1467321%5Cnhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1467321
Ramos, S, Gehrig, S, Pinggera, P, Franke, U and Rother, C (2017). Detecting unexpected obstacles for self-driving cars: Fusing deep learning and geometric modeling. IEEE Intelligent Vehicles Symposium, Proceedings. 1025–1032. http://arxiv.org/abs/1612.06573
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
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
Li, W, Hosseini Jafari, O and Rother, C (2019). Deep Object Co-segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11363 LNCS 638–653
Nowozin, S, Rother, C, Bagon, S, Sharp, T, Yao, B and Kohli, P (2011). Decision tree fields. Proceedings of the IEEE International Conference on Computer Vision. 1668–1675
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
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
Schlesinger, D, Jug, F, Myers, G, Rother, C and Kainmueller, D (2017). Crowd sourcing image segmentation with iaSTAPLE. Proceedings - International Symposium on Biomedical Imaging. 401–405
Vicente, S, Kolmogorov, V and Rother, C (2010). Cosegmentation revisited: Models and optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6312 LNCS 465–479
Rother, C, Kolmogorov, V, Minka, T and Blake, A (2006). Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1 994–1000. http://research.microsoft.com/vision/cambridge/
Royer, L A, Richmond, D L, Rother, C, Andres, B and Kainmueller, D (2016). Convexity shape constraints for image segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 402–410. http://arxiv.org/abs/1509.02122
Kluger, F, Brachmann, E, Ackermann, H, Rother, C, Yang, M Ying and Rosenhahn, B (2020). CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus. CVPR 2020. http://arxiv.org/abs/2001.02643PDF icon PDF (9.95 MB)
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
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
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. ProceedingsPDF icon 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. CVPRPDF icon Technical Report (1.35 MB)
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
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
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
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
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

Pages