A. Bab-hadiashar and D. Suter, Robust Optic Flow Computation, International Journal of Computer Vision, vol.29, issue.1, pp.59-77, 1998.
DOI : 10.1023/A:1008090730467
URL : http://zeno.eng.monash.edu.au/ali/rtlscpaw.ps

S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black et al., A Database and Evaluation Methodology for Optical Flow, International Journal of Computer Vision, vol.27, issue.3, pp.1-31, 2010.
DOI : 10.1145/1186562.1015766
URL : http://www.cs.brown.edu/people/black/Papers/ofevaltr.pdf

M. J. Black and P. Anandan, Robust dynamic motion estimation over time, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.296-302, 1991.
DOI : 10.1109/CVPR.1991.139705
URL : http://www.cs.brown.edu/people/black/Papers/cvpr91.pdf

M. J. Black and P. Anandan, The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields, Computer Vision and Image Understanding, vol.63, issue.1, pp.75-104, 1996.
DOI : 10.1006/cviu.1996.0006

T. Brox and J. Malik, Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.3, pp.500-513, 2011.
DOI : 10.1109/TPAMI.2010.143

T. Brox and J. Weickert, Nonlinear Matrix Diffusion for Optic Flow Estimation, Pattern Recognition, number 2449 in Lecture Notes in Computer Science, pp.446-453, 2002.
DOI : 10.1007/3-540-45783-6_54
URL : http://www.mia.uni-saarland.de/weickert/Papers/dagm02_brox.pdf

Z. Chen, H. Jin, Z. Lin, S. Cohen, and Y. Wu, Large Displacement Optical Flow from Nearest Neighbor Fields, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.2443-2450, 2013.
DOI : 10.1109/CVPR.2013.316

G. Farneback, Fast and accurate motion estimation using orientation tensors and parametric motion models, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, pp.135-139, 2000.
DOI : 10.1109/ICPR.2000.905291

B. K. Horn and B. G. Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2
URL : http://www.liralab.it/teaching/SINA/papers/horn-schunck-81.pdf

T. H. Kim, H. S. Lee, L. , and K. M. , Optical Flow via Locally Adaptive Fusion of Complementary Data Costs, 2013 IEEE International Conference on Computer Vision, pp.3344-3351, 2013.
DOI : 10.1109/ICCV.2013.415

H. Liu, R. Chellappa, and A. Rosenfeld, Accurate dense optical flow estimation using adaptive structure tensors and a parametric model, IEEE Transactions on Image Processing, issue.10, pp.121170-1180, 2003.

B. D. Lucas and T. Kanade, An Iterative Image Registration Technique with an Application to Stereo Vision, pp.674-679, 1981.

M. Middendorf and H. H. Nagel, Estimation and interpretation of discontinuities in optical flow fields, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001, pp.178-183, 2001.
DOI : 10.1109/ICCV.2001.937515

H. Nagel and A. Gehrke, Spatiotemporally adaptive estimation and segmentation of OF-fields, 1998.
DOI : 10.1007/BFb0054735

D. Sun, S. Roth, and M. J. Black, Secrets of optical flow estimation and their principles, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2432-2439, 2010.
DOI : 10.1109/CVPR.2010.5539939

A. Wedel, T. Pock, C. Zach, H. Bischof, C. et al., An Improved Algorithm for TV-L 1 Optical Flow, Statistical and Geometrical Approaches to Visual Motion Analysis, pp.23-45, 2009.
DOI : 10.1007/978-3-642-03061-1_2
URL : http://vision.in.tum.de/_media/spezial/bib/dagstuhlopticalflowchapter.pdf

P. Weinzaepfel, J. Revaud, Z. Harchaoui, and C. Schmid, DeepFlow: Large Displacement Optical Flow with Deep Matching, 2013 IEEE International Conference on Computer Vision, 2013.
DOI : 10.1109/ICCV.2013.175
URL : https://hal.archives-ouvertes.fr/hal-00873592

L. Xu, J. Jia, and Y. Matsushita, Motion detail preserving optical flow estimation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.1293-1300, 2010.
DOI : 10.1109/CVPR.2010.5539820

J. Yang and H. Li, Dense, accurate optical flow estimation with piecewise parametric model, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1019-1027, 2015.
DOI : 10.1109/CVPR.2015.7298704

J. Yves-bouguet, Pyramidal implementation of the lucas kanade feature tracker, 2000.