Optical Flow in Mostly Rigid Scenes
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[1] William B. Thompson,et al. Detecting moving objects , 1989, International Journal of Computer Vision.
[2] Harpreet S. Sawhney,et al. Independent motion detection in 3D scenes , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[3] P. Anandan,et al. Direct Recovery of Planar-Parallax from Multiple Frames , 1999, IEEE Trans. Pattern Anal. Mach. Intell..
[4] Vladlen Koltun,et al. Dense Monocular Depth Estimation in Complex Dynamic Scenes , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Michael J. Black,et al. A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.
[6] Andrew Blake,et al. "GrabCut": interactive foreground extraction using iterated graph cuts , 2004, ACM Trans. Graph..
[7] P. Anandan,et al. A unified approach to moving object detection in 2D and 3D scenes , 1996, Proceedings of 13th International Conference on Pattern Recognition.
[8] Vladlen Koltun,et al. Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials , 2011, NIPS.
[9] Min Bai,et al. Exploiting Semantic Information and Deep Matching for Optical Flow , 2016, ECCV.
[10] W. James MacLean. Removal of translation bias when using subspace methods , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[11] P. Anandan,et al. Hierarchical Model-Based Motion Estimation , 1992, ECCV.
[12] Lihi Zelnik-Manor,et al. Multi-Frame Estimation of Planar Motion , 2000, IEEE Trans. Pattern Anal. Mach. Intell..
[13] Allan D. Jepson,et al. Subspace methods for recovering rigid motion I: Algorithm and implementation , 2004, International Journal of Computer Vision.
[14] Gilad Adiv,et al. Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[15] Gérard G. Medioni,et al. Detecting Motion Regions in the Presence of a Strong Parallax from a Moving Camera by Multiview Geometric Constraints , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[16] Daphna Weinshall,et al. From Reference Frames to Reference Planes: Multi-View Parallax Geometry and Applications , 1998, ECCV.
[17] Raquel Urtasun,et al. Robust Monocular Epipolar Flow Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[18] Jiri Matas,et al. Forward-Backward Error: Automatic Detection of Tracking Failures , 2010, 2010 20th International Conference on Pattern Recognition.
[19] Marc Pollefeys,et al. Learning a Confidence Measure for Optical Flow , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Lionel Oisel,et al. Epipolar constrained motion estimation for reconstruction from video sequences , 1998, Electronic Imaging.
[21] Berthold K. P. Horn,et al. Direct methods for recovering motion , 1988, International Journal of Computer Vision.
[22] Lourdes Agapito,et al. Dense multibody motion estimation and reconstruction from a handheld camera , 2012, 2012 IEEE International Symposium on Mixed and Augmented Reality (ISMAR).
[23] Iasonas Kokkinos,et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs , 2014, ICLR.
[24] Guillermo Sapiro,et al. Edges as Outliers: Anisotropic Smoothing Using Local Image Statistics , 1999, Scale-Space.
[25] Michael J. Black,et al. Optical Flow with Semantic Segmentation and Localized Layers , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Thomas Brox,et al. High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.
[27] Harpreet S. Sawhney,et al. 3D geometry from planar parallax , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
[28] P. Anandan,et al. Direct Recovery of Planar-Parallax from Multiple Frames , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[29] P. Anandan,et al. Efficient representations of video sequences and their applications , 1996, Signal Process. Image Commun..
[30] Didier Stricker,et al. Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[31] Daniel Cremers,et al. Structure- and motion-adaptive regularization for high accuracy optic flow , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[32] J. Nocedal. Updating Quasi-Newton Matrices With Limited Storage , 1980 .
[33] Joachim Weickert,et al. A Variational Model for the Joint Recovery of the Fundamental Matrix and the Optical Flow , 2008, DAGM-Symposium.
[34] Christian Heipke,et al. Discrete Optimization for Optical Flow , 2015, GCPR.
[35] Michal Irani,et al. Recovery of Ego-Motion Using Region Alignment , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[36] Pushmeet Kohli,et al. Reduce, reuse & recycle: Efficiently solving multi-label MRFs , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[37] Carsten Rother,et al. FusionFlow: Discrete-continuous optimization for optical flow estimation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[38] Bill Triggs,et al. Plane+Parallax, Tensors and Factorization , 2000, ECCV.
[39] Jitendra Malik,et al. Rigid Body Segmentation and Shape Description from Dense Optical Flow Under Weak Perspective , 1997, IEEE Trans. Pattern Anal. Mach. Intell..
[40] Andreas Geiger,et al. Object scene flow for autonomous vehicles , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Luc Van Gool,et al. The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.
[42] Stefan Roth,et al. Joint Optical Flow and Temporally Consistent Semantic Segmentation , 2016, ECCV Workshops.