A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them
暂无分享,去创建一个
[1] Horst Bischof,et al. Motion estimation with non-local total variation regularization , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[2] Karl Krissian,et al. Symmetric Optical Flow , 2007, EUROCAST.
[3] P. Anandan,et al. Hierarchical Model-Based Motion Estimation , 1992, ECCV.
[4] David Suter,et al. Robust Optic Flow Computation , 1998, International Journal of Computer Vision.
[5] L. Rudin,et al. Nonlinear total variation based noise removal algorithms , 1992 .
[6] Michael J. Black,et al. Estimating Optical Flow in Segmented Images Using Variable-Order Parametric Models With Local Deformations , 1996, IEEE Trans. Pattern Anal. Mach. Intell..
[7] Xiaofeng Ren,et al. Local grouping for optical flow , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Daniel Cremers,et al. Large displacement optical flow computation withoutwarping , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[9] Seth J. Teller,et al. Particle Video: Long-Range Motion Estimation Using Point Trajectories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[10] Carsten Rother,et al. FusionFlow: Discrete-continuous optimization for optical flow estimation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[11] R. Weale. Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information. David Marr , 1983 .
[12] Qi Gao,et al. A generative perspective on MRFs in low-level vision , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[13] Alfred M. Bruckstein,et al. Over-Parameterized Variational Optical Flow , 2007, International Journal of Computer Vision.
[14] Ying Wu,et al. Decomposing and regularizing sparse/non-sparse components for motion field estimation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[15] M. Nikolova. Model distortions in Bayesian MAP reconstruction , 2007 .
[16] Richard Szeliski,et al. Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.
[17] David J. Fleet,et al. Performance of optical flow techniques , 1994, International Journal of Computer Vision.
[18] Gabriel J. Brostow,et al. Learning to find occlusion regions , 2011, CVPR 2011.
[19] Guy Gilboa,et al. Nonlocal Operators with Applications to Image Processing , 2008, Multiscale Model. Simul..
[20] Hans-Peter Seidel,et al. Complementary Optic Flow , 2009, EMMCVPR.
[21] Daniel Cremers,et al. Anisotropic Huber-L1 Optical Flow , 2009, BMVC.
[22] Joachim Weickert,et al. Towards ultimate motion estimation: combining highest accuracy with real-time performance , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[23] Horst Bischof,et al. A Duality Based Approach for Realtime TV-L1 Optical Flow , 2007, DAGM-Symposium.
[24] Donald Geman,et al. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .
[25] J. Weickert,et al. Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods , 2005 .
[26] Nebojsa Jojic,et al. Consistent segmentation for optical flow estimation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[27] Edward H. Adelson,et al. Representing moving images with layers , 1994, IEEE Trans. Image Process..
[28] Daniel Cremers,et al. An Improved Algorithm for TV-L 1 Optical Flow , 2009, Statistical and Geometrical Approaches to Visual Motion Analysis.
[29] Hui Cheng,et al. Bilateral Filtering-Based Optical Flow Estimation with Occlusion Detection , 2006, ECCV.
[30] Edward H. Adelson,et al. Human-assisted motion annotation , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[31] Andrew Blake,et al. Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.
[32] Joachim Weickert,et al. Universität Des Saarlandes Fachrichtung 6.1 – Mathematik Optic Flow in Harmony Optic Flow in Harmony Optic Flow in Harmony , 2022 .
[33] Wu-chi Feng,et al. Enabling warping on stereoscopic images , 2012, ACM Trans. Graph..
[34] Edward H. Adelson,et al. PYRAMID METHODS IN IMAGE PROCESSING. , 1984 .
[35] John W. Fisher,et al. Low level vision via switchable Markov random fields , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[36] Yasuyuki Matsushita,et al. Motion detail preserving optical flow estimation , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[37] Reinhard Klette,et al. Residual Images Remove Illumination Artifacts! , 2009, DAGM-Symposium.
[38] Michael J. Black,et al. A Fully-Connected Layered Model of Foreground and Background Flow , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[39] Jean-Michel Morel,et al. A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[40] Marc Pollefeys,et al. Segmenting video into classes of algorithm-suitability , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[41] S. Osher,et al. A new median formula with applications to PDE based denoising , 2009 .
[42] Jitendra Malik,et al. Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[43] Ying Wu,et al. Large Displacement Optical Flow from Nearest Neighbor Fields , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[44] Berthold K. P. Horn,et al. Determining Optical Flow , 1981, Other Conferences.
[45] Michael J. Black,et al. Learning Optical Flow , 2008, ECCV.
[46] Takeo Kanade,et al. An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.
[47] D. Cremers,et al. Duality TV-L1 flow with fundamental matrix prior , 2008, 2008 23rd International Conference Image and Vision Computing New Zealand.
[48] F. Glazer,et al. Scene Matching by Hierarchical Correlation , 1983 .
[49] Michael J. Black,et al. Secrets of optical flow estimation and their principles , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[50] In-So Kweon,et al. Adaptive Support-Weight Approach for Correspondence Search , 2006, IEEE Trans. Pattern Anal. Mach. Intell..
[51] Andreas Geiger,et al. Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[52] F. A. Seiler,et al. Numerical Recipes in C: The Art of Scientific Computing , 1989 .
[53] Michael J. Black,et al. The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields , 1996, Comput. Vis. Image Underst..
[54] Richard Szeliski,et al. A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[55] William H. Press,et al. Numerical recipes in C. The art of scientific computing , 1987 .
[56] Andrew Blake,et al. Fusion Moves for Markov Random Field Optimization , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[57] Cheng Lei,et al. Optical flow estimation on coarse-to-fine region-trees using discrete optimization , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[58] R. Keys. Cubic convolution interpolation for digital image processing , 1981 .
[59] F. Wilcoxon. Individual Comparisons by Ranking Methods , 1945 .
[60] Michael J. Black,et al. Layered segmentation and optical flow estimation over time , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.
[61] Vladlen Koltun,et al. Efficient Nonlocal Regularization for Optical Flow , 2012, ECCV.
[62] Daniel Cremers,et al. Structure- and motion-adaptive regularization for high accuracy optic flow , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[63] Anand Rangarajan,et al. A new convex edge-preserving median prior with applications to tomography , 2003, IEEE Transactions on Medical Imaging.
[64] Michael J. Black,et al. Layered image motion with explicit occlusions, temporal consistency, and depth ordering , 2010, NIPS.
[65] D. Shulman,et al. Regularization of discontinuous flow fields , 1989, [1989] Proceedings. Workshop on Visual Motion.
[66] Xiaogang Wang,et al. Optical flow estimation using learned sparse model , 2011, 2011 International Conference on Computer Vision.
[67] Michael J. Black,et al. A Naturalistic Open Source Movie for Optical Flow Evaluation , 2012, ECCV.
[68] Thomas Brox,et al. High Accuracy Optical Flow Estimation Based on a Theory for Warping , 2004, ECCV.
[69] Y. J. Tejwani,et al. Robot vision , 1989, IEEE International Symposium on Circuits and Systems,.
[70] Ohad Ben-Shahar,et al. A polar representation of motion and implications for optical flow , 2011, CVPR 2011.
[71] Hans-Hellmut Nagel,et al. An Investigation of Smoothness Constraints for the Estimation of Displacement Vector Fields from Image Sequences , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[72] Li Xu,et al. A Segmentation Based Variational Model for Accurate Optical Flow Estimation , 2008, ECCV.