{\rtf1\ansi\deff0\deftab360 {\fonttbl {\f0\fswiss\fcharset0 Arial} {\f1\froman\fcharset0 Times New Roman} {\f2\fswiss\fcharset0 Verdana} {\f3\froman\fcharset2 Symbol} } {\colortbl; \red0\green0\blue0; } {\info {\author Biblio 7.x}{\operator }{\title Biblio RTF Export}} \f1\fs24 \paperw11907\paperh16839 \pgncont\pgndec\pgnstarts1\pgnrestart 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/2001.02643\par \par Ardizzone, L, Mackowiak, R, Rother, C and K\'f6the, U (2020). Exact Information Bottleneck with Invertible Neural Networks: Getting the Best of Discriminative and Generative Modeling. http://arxiv.org/abs/2001.06448\par \par Haller, S, Prakash, M, Hutschenreiter, L, Pietzsch, T, Rother, C, Jug, F, Swoboda, P and Savchynskyy, B (2020). A Primal-Dual Solver for Large-Scale Tracking-by-Assignment. AISTATS 2020\par \par Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2020). Taxonomy of Dual Block-Coordinate Ascent Methods for Discrete Energy Minimization. AISTATS 2020. https://gitlab.com/\par \par Kruse, J, Ardizzone, L, Rother, C and K\'f6the, U (2019). Benchmarking Invertible Architectures On Inverse Problems\par \par Kamann, C and Rother, C (2019). Benchmarking the Robustness of Semantic Segmentation Models. http://arxiv.org/abs/1908.05005\par \par 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\par \par 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\par \par 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\par \par 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\par \par Ardizzone, L, L\'fcth, C, Kruse, J, Rother, C and K\'f6the, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392\par \par Ardizzone, L, L\'fcth, C, Kruse, J, Rother, C and K\'f6the, U (2019). Guided Image Generation with Conditional Invertible Neural Networks. http://arxiv.org/abs/1907.02392\par \par Hosseini Jafari, O, Mustikovela, S Karthik, Pertsch, K, Brachmann, E and Rother, C (2019). iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11363 LNCS 477?492\par \par Leistner, T, Schilling, H, Mackowiak, R, Gumhold, S and Rother, C (2019). Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift. Proceedings - 2019 International Conference on 3D Vision, 3DV 2019. 249?257. http://arxiv.org/abs/1909.09059 http://dx.doi.org/10.1109/3DV.2019.00036\par \par Li, W, Hosseini Jafari, O and Rother, C (2019). Localizing Common Objects Using Common Component Activation Map\par \par Brachmann, E and Rother, C (2019). Neural-guided RANSAC: Learning where to sample model hypotheses. Proceedings of the IEEE International Conference on Computer Vision. 2019-Octob 4321?4330. http://arxiv.org/abs/1905.04132\par \par Adler, T J, Ayala, L, Ardizzone, L, Kenngott, H G, Vemuri, A, M\'fcller-Stich, B P, Rother, C, K\'f6the, U and Maier-Hein, L (2019). Out of Distribution Detection for Intra-operative Functional Imaging. MICCAI UNSURE Workshop 2019. 11840 LNCS 75?82\par \par Bhowmik, A, Gumhold, S, Rother, C and Brachmann, E (2019). Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level Task. http://arxiv.org/abs/1912.00623\par \par Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2018). Augmented Reality Meets Computer Vision. International Journal of Computer Vision. In press 1?13\par \par Abu Alhaija, H, Mustikovela, S K, Mescheder, A, Geiger, C and Rother, C (2018). Augmented Reality Meets Computer Vision Efficient Data Generation for Urban Driving Scenes. IJCV. 1-12\par \par Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2018). Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes. International Journal of Computer Vision. 126 961?972. http://arxiv.org/abs/1708.01566\par \par Hoda?, 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\par \par Arnab, A, Zheng, S, Jayasumana, S, Romera-paredes, B, Kirillov, A, Savchynskyy, B, Rother, C, Kahl, F and Torr, P (2018). Conditional Random Fields Meet Deep Neural Networks for Semantic Segmentation. Cvpr. XX 1?15. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.308.8889&rep=rep1&type=pdf%0Ahttp://dx.doi.org/10.1109/CVPR.2012.6248050\par \par Abu Alhaija, H, Mustikovela, S K, Geiger, A and Rother, C (2018). Geometric Image Synthesis. ACCV. Proceedings, in press\par \par Hosseini Jafari, O, Mustikovela, S K, Pertsch, K, Brachmann, E and Rother, C (2018). iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects. ACCV. Proceedings, in press\par \par Brachmann, E and Rother, C (2018). Learning Less is More - 6D Camera Localization via 3D Surface Regression. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 4654?4662. http://arxiv.org/abs/1711.10228\par \par Tourani, S, Shekhovtsov, A, Rother, C and Savchynskyy, B (2018). MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical Models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 11208 LNCS 264?281\par \par Schilling, H, Diebold, M, Rother, C and J\'e4hne, B (2018). Trust your Model: Light Field Depth Estimation with inline Occlusion Handling. CVPR. Proceedings\par \par Schilling, H, Diebold, M, Rother, C and J\'e4hne, B (2018). Trust your Model: Light Field Depth Estimation with Inline Occlusion Handling. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 4530?4538\par \par Hosseini Jafari, O, Groth, O, Kirillov, A, Yang, M Ying and Rother, C (2017). Analyzing modular CNN architectures for joint depth prediction and semantic segmentation. Proceedings - IEEE International Conference on Robotics and Automation. 4620?4627. http://arxiv.org/abs/1702.08009 http://dx.doi.org/10.1109/ICRA.2017.7989537\par \par Abu Alhaija, H, Mustikovela, S Karthik, Mescheder, L, Geiger, A and Rother, C (2017). Augmented reality meets deep learning for car instance segmentation in urban scenes. British Machine Vision Conference 2017, BMVC 2017\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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\par \par Michel, F, Kirillov, A, Brachmann, E, Krull, A, Gumhold, S, Savchynskyy, B and Rother, C (2017). Global hypothesis generation for 6D object pose estimation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 115?124. http://arxiv.org/abs/1612.02287\par \par Kirillov, A, Levinkov, E, Andres, B, Savchynskyy, B and Rother, C (2017). InstanceCut: From edges to instances with MultiCut. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 7322?7331\par \par Levinkov, E, Uhrig, J, Tang, S, Omran, M, Insafutdinov, E, Kirillov, A, Rother, C, Brox, T, Schiele, B and Andres, B (2017). Joint graph decomposition & node labeling: Problem, algorithms, applications. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 1904?1912\par \par Kirillov, A, Schlesinger, D, Zheng, S, Savchynskyy, B, Torr, P H S and Rother, C (2017). Joint training of generic CNN-CRF models with stochastic optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 10112 LNCS 221?236. http://host.robots.ox.ac.uk:8080/leaderboard\par \par Kruse, J, Rother, C and Schmidt, U (2017). Learning to Push the Limits of Efficient FFT-Based Image Deconvolution. Proceedings of the IEEE International Conference on Computer Vision. 2017-Octob 4596?4604\par \par Kruse, J, Rother, C, Schmidt, U and Dresden, T U (2017). Learning To Push The Limits Of Efficient Fft-Based Image Deconvolution - Supplemental Material\par \par Krull, A, Brachmann, E, Nowozin, S, Michel, F, Shotton, J and Rother, C (2017). PoseAgent: Budget-constrained 6D object pose estimation via reinforcement learning. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017-Janua 2566?2574. http://arxiv.org/abs/1612.03779\par \par Massiceti, D, Krull, A, Brachmann, E, Rother, C and Torr, P H S (2017). Random Forests versus Neural Networks ? What's best for camera location\par \par Hullin, M, Klein, R, Schultz, T, Yao, A, Li, W, Hosseini Jafari, O and Rother, C (2017). Semantic-Aware Image Smoothing. Vision, Modeling, and Visualization. https://hci.iwr.uni-heidelberg.de/vislearn/wp-content/uploads/2014/08/paper1024_CRC.pdf\par \par 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\par \par 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\par \par Mund, J, Michel, F, Dieke-Meier, F, Fricke, H, Meyer, L and Rother, C (2016). Introducing LiDAR Point Cloud-based Object Classification for Safer Apron Operations. International Symposium on Enhanced Solutions for Aircraft and Vehicle Surveillance Applications. https://goo.gl/28Yoqh\par \par Pinggera, P, Ramos, S, Gehrig, S, Franke, U, Rother, C and Mester, R (2016). Lost and found: Detecting small road hazards for self-driving vehicles. IEEE International Conference on Intelligent Robots and Systems. 2016-Novem 1099?1106. http://www.6d-vision.com/lostandfounddataset\par \par Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1?144.12. https://github.com/BVLC/caffe/wiki/Model-Zoo\\#fcn\par \par Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1?144.12\par \par Richmond, D L, Kainmueller, D, Yang, M Y, Myers, E W and Rother, C (2016). Mapping auto-context decision forests to deep convnets for semantic segmentation. British Machine Vision Conference 2016, BMVC 2016. 2016-Septe 144.1?144.12. http://arxiv.org/abs/1507.07583\par \par Sellent, A, Rother, C and Roth, S (2016). Stereo video deblurring. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9906 LNCS 558?575\par \par Sellent, A, Rother, C and Roth, S (2016). Stereo Video Deblurring-Supplemental Material\par \par Brachmann, E, Michel, F, Krull, A, Yang, M Ying, Gumhold, S and Rother, C (2016). Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 3364?3372\par \par Brachmann, E, Michel, F, Krull, A, Yang, M Ying, Gumhold, S and Rother, C (2016). Uncertainty-Driven 6D Pose Estimation of Objects and Scenes from a Single RGB Image. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2016-Decem 3364?3372\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. Int.~J.~Comp.~Vision\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 1-30\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2015). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. International Journal of Computer Vision. 115 155?184. http://hci.iwr.uni-heidelberg.de/opengm2/\par \par 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\par \par 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\par \par 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\par \par 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\par \par Kirillov, A, Schlesinger, D, Vetrov, D, Rother, C and Savchynskyy, B (2015). M-best-diverse labelings for submodular energies and beyond. Advances in Neural Information Processing Systems. 2015-Janua 613?621\par \par Zheng, S, Prisacariu, V Adrian, Averkiou, M, Cheng, M Ming, Mitra, N J, Shotton, J, Torr, P H S and Rother, C (2015). Object proposals estimation in depth image using compact 3D shape manifolds. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9358 196?208\par \par Mitra, N J, Stam, J, Xu, K, Cheng, M - M, Prisacariu, V Adrian, Zheng, S, Torr, P H S and Rother, C (2015). Pacific Graphics 2015 DenseCut: Densely Connected CRFs for Realtime GrabCut. 34. http://mftp.mmcheng.net/Papers/DenseCut.pdf\par \par 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\par \par 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\par \par Michel, F, Krull, A, Brachmann, E, Yang, M Ying, Gumhold, S and Rother, C (2015). Pose Estimation of Kinematic Chain Instances via Object Coordinate Regression. 181.1?181.11\par \par 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\par \par Zouhar, A, Rother, C and Fuchs, S (2015). Semantic 3-D labeling of ear implants using a global parametric transition prior. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 9350 177?184\par \par Richmond, D, Kainmueller, D, Glocker, B, Rother, C and Myers, G (2015). 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). 9349 653?660\par \par 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\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for StructuredDiscrete Energy Minimization Problems. CoRR. http://arxiv.org/abs/1404.0533\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, C, Nowozin, S, Batra, D, Kim, S, Kausler, B X, Kr\'f6ger, T, Lellmann, J, Komodakis, N, Savchynskyy, B and Rother, C (2014). A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems. CoRR. abs/1404.0533. http://hci.iwr.uni-heidelberg.de/opengm2/\par \par 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\par \par Horn\'e1?ek, 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\par \par Hoai, M, Torresani, L, De La Torre, F and Rother, C (2014). Learning discriminative localization from weakly labeled data. Pattern Recognition. 47 1523?1534\par \par M\'e1rquez-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\par \par Jug, F, Pietzsch, T, Kainm\'fcller, 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\par \par 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\par \par 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\par \par 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\par \par Horn\'e1?ek, 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\par \par 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\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, 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. CVPR\par \par Kappes, J H, Andres, B, Hamprecht, F A, Schn\'f6rr, 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. Proceedings\par \par Horn\'e1?ek, 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\par \par Schmidt, U, Rother, C, Nowozin, S, Jancsary, J and Roth, S (2013). Discriminative Non-Blind Deblurring\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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/\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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\par \par 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/\par \par Meister, S, Izadi, S, Kohli, P, H\'e4mmerle, M, Rother, C and Kondermann, D (2012). When Can We Use KinectFusion for Ground Truth Acquisition?. Workshop on Color-Depth Camera Fusion in Robotics, IEEE InternationalConference on Intelligent Robots and Systems\par \par 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\par \par He, K, Rhemann, C, Rother, C, Tang, X and Sun, J (2011). A global sampling method for alpha matting. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2049?2056\par \par T\'f6ppe, E, Oswald, M R, Cremers, D and Rother, C (2011). Image-based 3D modeling via cheeger sets. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6492 LNCS 53?64\par \par Rother, C and Kolmogorov, V (2011). Interactive foreground extraction using graph cut. Advances in Markov \\ldots. 1?20. http://research.microsoft.com/pubs/147408/rotheretalmrfbook-grabcut.pdf\par \par Vicente, S, Rother, C and Kolmogorov, V (2011). Object cosegmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2217?2224\par \par Bleyer, M, Rother, C, Kohli, P, Scharstein, D and Sinha, S (2011). Object stereo Joint stereo matching and object segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 3081?3088\par \par Bleyer, M, Rhemann, C and Rother, C (2011). PatchMatch Stereo - Stereo Matching with Slanted Support Windows. 14.1?14.11\par \par Pletscher, P, Nowozin, S, Kohli, P and Rother, C (2011). Putting MAP back on the map. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6835 LNCS 111?121\par \par Pletscher, P, Nowozin, S, Kohli, P and Rother, C (2011). Putting MAP back on the map. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6835 LNCS 111?121\par \par Gehler, P Vincent, Rother, C, Kiefel, M, Zhang, L and Sch\'f6lkopf, B (2011). Recovering intrinsic images with a global sparsity prior on reflectance. Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011\par \par Rother, C (2011). Sparse Higher Order Functions of Discrete Variables?-Representation and Optimization. research.microsoft.com. 45. http://research.microsoft.com/pubs/147370/RotherKohli-SparseHigherOrder.pdf\par \par 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\par \par Lempitsky, V, Rother, C, Roth, S and Blake, A (2010). Fusion moves for markov random field optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 1392?1405\par \par Lempitsky, V, Rother, C, Roth, S and Blake, A (2010). Fusion moves for markov random field optimization. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32 1392?1405\par \par 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\par \par Rother, C, Kohli, P, Feng, W and Jia, J (2010). Minimizing sparse higher order energy functions of discrete variables. 1382?1389\par \par Singaraju, D, Rother, C and Rhemann, C (2010). New appearance models for natural image matting. 659?666\par \par Mansfield, A, Gehler, P, Van Gool, L and Rother, C (2010). Scene carving: Scene consistent image retargeting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6311 LNCS 143?156. www.fujifilm.com/products/3d/camera/finepix_\par \par Rhemann, C, Rother, C, Kohli, P and Gelautz, M (2010). A spatially varying PSF-based prior for alpha matting. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2149?2156\par \par Bleyer, M, Rother, C and Kohli, P (2010). Surface stereo with soft segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1570?1577\par \par Glocker, B, T. Heibel, H, Navab, N, Kohli, P and Rother, C (2010). TriangleFlow: Optical flow with triangulation-based higher-order likelihoods. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 6313 LNCS 272?285. http://vision.middlebury.edu/flow/\par \par Shesh, A, Criminisi, A, Rother, C and Smyth, G (2009). 3D-aware image editing for out of bounds photography. Proceedings - Graphics Interface. 47?54. http://www.flickr.com/groups/oob/\par \par Lempitsky, V, Kohli, P, Rother, C and Sharp, T (2009). Image segmentation with a bounding box prior. Proceedings of the IEEE International Conference on Computer Vision. 277?284\par \par Vicente, S, Kolmogorov, V and Rother, C (2009). Joint optimization of segmentation and appearance models. Proceedings of the IEEE International Conference on Computer Vision. 755?762\par \par Singaraju, D, Rother, C and Rhemann, C (2009). New appearance models for natural image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 659?666\par \par Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826?1833\par \par Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826?1833. www.alphamatting.com.\par \par Rhemann, C, Rother, C, Wang, J, Gelautz, M, Kohli, P and Rott, P (2009). A perceptually motivated online benchmark for image matting. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 1826?1833. www.alphamatting.com.\par \par Bleyer, M, Gelautz, M, Rother, C and Rhemann, C (2009). A stereo approach that handles the matting problem via imagewarping. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009. 2009 IEEE 501?508\par \par Singaraju, D, Rother, C and Rhemann, C (2009). Supplementary Material For New Appearance Models For Image Matting\par \par Shotton, J, Winn, J, Rother, C and Criminisi, A (2009). TextonBoost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context. International Journal of Computer Vision. 81 2?23. http://jamie.shotton.org/work/code.html\par \par Nguyen, M Hoai, Torresani, L, De La Torre, F and Rother, C (2009). Weakly supervised discriminative localization and classification: A joint learning process. Proceedings of the IEEE International Conference on Computer Vision. 1925?1932\par \par Nguyen, M Hoai, Torresani, L, De La Torre, F and Rother, C (2009). Weakly supervised discriminative localization and classification: A joint learning process. Proceedings of the IEEE International Conference on Computer Vision. 1925?1932\par \par Gehler, P Vincent, Rother, C, Blake, A, Minka, T and Sharp, T (2008). Bayesian color constancy revisited. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR\par \par Szeliski, R, Zabih, R, Scharstein, D, Veksler, O, Kolmogorov, V, Agarwala, A, Tappen, M and Rother, C (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. Springer-Verlag. 30 1068?1080. http://vision.middlebury.edu/MRF.\par \par Szeliski, R, Zabih, R, Scharstein, D, Veksler, O, Kolmogorov, V, Agarwala, A, Tappen, M and Rother, C (2008). A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30 1068?1080\par \par 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\par \par 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\par \par 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\par \par Vicente, S, Kolmogorov, V and Rother, C (2008). Graph cut based image segmentation with connectivity priors. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR\par \par Rhemann, C, Rother, C, Rav-Acha, A and Sharp, T (2008). High resolution matting via interactive trimap segmentation. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR\par \par Rhemann, C, Rother, C, Rav-Acha, A and Sharp, T (2008). High resolution matting via interactive trimap segmentation. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR\par \par Rhemann, C, Rother, C, Rav-Acha, A and Sharp, T (2008). High resolution matting via interactive trimap segmentation. 26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR\par \par Lempitsky, V, Blake, A and Rother, C (2008). Image segmentation by branch-and-mincut. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5305 LNCS 15?29\par \par Lempitsky, V, Blake, A and Rother, C (2008). Image segmentation by branch-and-mincut. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5305 LNCS 15?29\par \par Rhemann, C, Rother, C and Gelautz, M (2008). Improving color modeling for alpha matting. BMVC 2008 - Proceedings of the British Machine Vision Conference 2008\par \par Kohli, P, Shekhovtsov, A, Rother, C, Kolmogorov, V and Torr, P (2008). On partial optimality in multi-label MRFs. Proceedings of the 25th International Conference on Machine Learning. 480?487\par \par Hoiem, D, Rother, C and Winn, J (2007). 3D LayoutCRF for multi-view object class recognition and segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition\par \par Kolmogorov, V, Boykov, Y and Rother, C (2007). Applications of parametric maxflow in computer vision. Proceedings of the IEEE International Conference on Computer Vision\par \par Kolmogorov, V, Boykov, Y and Rother, C (2007). Applications of parametric maxflow in computer vision. Proceedings of the IEEE International Conference on Computer Vision\par \par Kannan, A, Winn, J and Rother, C (2007). Clustering appearance and shape by learning jigsaws. Advances in Neural Information Processing Systems. 657?664\par \par Kannan, A, Winn, J and Rother, C (2007). Clustering appearance and shape by learning jigsaws. Advances in Neural Information Processing Systems. 657?664\par \par 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\par \par Criminisi, A, Blake, A, Rother, C, Shotton, J and Torr, P H S (2007). Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming. International Journal of Computer Vision. Kluwer Academic Publishers. 71 89?110\par \par Blake, A, Criminisi, A, Cross, G, Kolmogorov, V and Rother, C (2007). Fusion of stereo, colour and contrast. Springer Tracts in Advanced Robotics. 28. www.research.microsoft.com/vision/cambridge\par \par Lempitsky, V, Rother, C and Blake, A (2007). LogCut - Efficient graph cut optimization for markov random fields. Proceedings of the IEEE International Conference on Computer Vision\par \par Kolmogorov, V and Rother, C (2007). Minimizing nonsubmodular functions with graph cuts - A review. IEEE Transactions on Pattern Analysis and Machine Intelligence. 29 1274?1279\par \par Rother, C, Kolmogorov, V, Lempitsky, V and Szummer, M (2007). Optimizing binary MRFs via extended roof duality. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition\par \par Rother, C, Kolmogorov, V, Lempitsky, V and Szummer, M (2007). Optimizing Binary MRFs via Extended Roof Duality Technical Report MSR-TR-2007-46. Computing. http://research.microsoft.com/vision/cambridge/\par \par Lalonde, J Fran\'e7ois, Hoiem, D, Efros, A A, Rother, C, Winn, J and Criminisi, A (2007). Photo clip art. Proceedings of the ACM SIGGRAPH Conference on Computer Graphics. http://graphics.cs.cmu.edu/projects/photoclipart/\par \par 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\par \par 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\par \par 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/\par \par Kolmogorov, V, Criminisi, A, Blake, A, Cross, G and Rother, C (2006). Probabilistic fusion of stereo with color and contrast for bilayer segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28 1480?1492. http://research.microsoft.com/vision/cambridge\par \par Shotton, J, Winn, J, Rother, C and Criminisi, A (2006). TextonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3951 LNCS 1?15. http://research.microsoft.com/vision/cambridge/recognition/.\par \par Kirk, D S, Sellen, A J, Rother, C and Wood, K R (2006). Understanding photowork. Conference on Human Factors in Computing Systems - Proceedings. 2 761?770\par \par Kolmogorov, V, Criminisi, A, Blake, A, Cross, G and Rother, C (2005). Bi-layer segmentation of binocular stereo video. Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. II 407?414. http://research.microsoft.com/vision/cambridge\par \par 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\par \par Rother, C, Kolmogorov, V and Blake, A (2004). "GrabCut" - Interactive foreground extraction using iterated graph cuts. ACM Transactions on Graphics. 23 309?314\par \par Rother, C (2003). Linear Multi-View Reconstruction for Translating Cameras. Nada.Kth.Se. http://www.nada.kth.se/ carstenr/papers/paper_ssab03.pdf\par \par Rother, C (2003). Linear multi-view reconstruction of points, lines, planes and cameras using a reference plane. Proceedings of the IEEE International Conference on Computer Vision. 2 1210?1217. http://www.nada.kth.se/carstenr\par \par Rother, C (2003). Multi-View Reconstruction and Camera Recovery using a Real or Virtual Reference Plane. http://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=4&cad=rja&uact=8&ved=0CDUQFjAD&url=http%3A%2F%2Fwww.nada.kth.se%2Futbildning%2Fforsk.utb%2Favhandlingar%2Fdokt%2Frother.pdf&ei=AyX_VPKmIomeNqeOgpgL&usg=AFQjCNHCmc75P5EHYWLtBUaHtUAs4yOnJw&bvm=bv.\par \par Rother, C and Carlsson, S (2002). Linear multi view reconstruction and camera recovery using a reference plane. International Journal of Computer Vision. 49 117?141\par \par Rother, C and Carlsson, S (2002). Linear multi view reconstruction with missing data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2351 209?324\par \par Rother, C (2002). A new approach to vanishing point detection in architectural environments. Image and Vision Computing. 20 647?655\par \par Rother, C, Carlsson, S and Tell, D (2002). Projective factorization of planes and cameras in multiple views. Proceedings - International Conference on Pattern Recognition. 16 737?740\par \par Rother, C and Carlsson, S (2001). Linear multi view reconstruction and camera recovery. Proceedings of the IEEE International Conference on Computer Vision. 1 42?49\par \par }