S. Baker and I. Matthews, Lucas-Kanade 20 Years On: A Unifying Framework, International Journal of Computer Vision, vol.56, issue.3, 2004.
DOI : 10.1023/B:VISI.0000011205.11775.fd

S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black et al., A database and evaluation methodology for optical flow, IJCV, vol.2, issue.1 6, 2011.

C. Barnes, E. Shechtman, D. B. Goldman, and A. Finkelstein, The Generalized PatchMatch Correspondence Algorithm, ECCV, 2010.
DOI : 10.1007/978-3-642-15558-1_3

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, 1996.
DOI : 10.1006/cviu.1996.0006

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, High Accuracy Optical Flow Estimation Based on a Theory for Warping, ECCV, 2004.
DOI : 10.1007/978-3-540-24673-2_3

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, 2008.
DOI : 10.1109/TPAMI.2010.143

A. Bruhn and J. Weickert, Towards ultimate motion estimation: combining highest accuracy with real-time performance, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, 2005.
DOI : 10.1109/ICCV.2005.240

D. J. Butler, J. Wulff, G. B. Stanley, and M. J. Black, A Naturalistic Open Source Movie for Optical Flow Evaluation, ECCV, 2006.
DOI : 10.1007/978-3-642-33783-3_44

A. Ecker and S. Ullman, A hierarchical non-parametric method for capturing non-rigid deformations, Image and Vision Computing, issue.2, 2009.

A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, Vision meets robotics: The KITTI dataset, The International Journal of Robotics Research, vol.32, issue.11, 2013.
DOI : 10.1177/0278364913491297

L. Gorelick, M. Blank, E. Shechtman, M. Irani, and R. Basri, Actions as Space-Time Shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.12, 2007.
DOI : 10.1109/TPAMI.2007.70711

D. Keysers, T. Deselaers, C. Gollan, and H. Ney, Deformation Models for Image Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.8, p.3, 2007.
DOI : 10.1109/TPAMI.2007.1153

J. Kim, C. Liu, F. Sha, and K. Grauman, Deformable Spatial Pyramid Matching for Fast Dense Correspondences, 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013.
DOI : 10.1109/CVPR.2013.299

I. Laptev and P. Pérez, Retrieving actions in movies, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4409105

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradientbased learning applied to document recognition, Proceedings of the IEEE, 1998.

M. Leordeanu, A. Zanfir, and C. Sminchisescu, Locally Affine Sparse-to-Dense Matching for Motion and Occlusion Estimation, 2013 IEEE International Conference on Computer Vision, p.7
DOI : 10.1109/ICCV.2013.216

C. Liu, J. Yuen, and A. Torralba, SIFT Flow: Dense Correspondence Across Scenes and Its Applications, IEEE Trans. PAMI, issue.2, 2011.
DOI : 10.1007/978-3-319-23048-1_2

J. Malik and P. Perona, Preattentive texture discrimination with early vision mechanisms, Journal of the Optical Society of America A, vol.7, issue.5, 1990.
DOI : 10.1364/JOSAA.7.000923

P. Matikainen, M. Hebert, and R. Sukthankar, Trajectons: Action recognition through the motion analysis of tracked features, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops, 2009.
DOI : 10.1109/ICCVW.2009.5457659

K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas et al., A Comparison of Affine Region Detectors, International Journal of Computer Vision, vol.65, issue.1-2, 2005.
DOI : 10.1007/s11263-005-3848-x

URL : https://hal.archives-ouvertes.fr/inria-00548528

J. Revaud, P. Weinzaepfel, Z. Harchaoui, and C. Schmid, Deep matching and its application to large displacement optical flow, 2013.

F. Steinbrucker, T. Pock, and D. Cremers, Large displacement optical flow computation without warping, ICCV, 2009.
DOI : 10.1109/iccv.2009.5459364

M. Stoll, S. Volz, and A. Bruhn, Adaptive Integration of Feature Matches into Variational Optical Flow Methods, ACCV, 2012.
DOI : 10.1007/978-3-642-37431-9_1

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, 2008.
DOI : 10.1109/CVPR.2010.5539939

R. Szeliski, Computer Vision: Algorithms and Applications, 2006.
DOI : 10.1007/978-1-84882-935-0

E. Tola, V. Lepetit, and P. Fua, A fast local descriptor for dense matching, 2008 IEEE Conference on Computer Vision and Pattern Recognition, 2008.
DOI : 10.1109/CVPR.2008.4587673

S. Uchida and H. Sakoe, A monotonic and continuous twodimensional warping based on dynamic programming, ICPR, p.3, 1998.

C. Vogel, S. Roth, and K. Schindler, An Evaluation of Data Costs for Optical Flow, GCPR, 2013, p.8
DOI : 10.1007/978-3-642-40602-7_37

H. Wang, A. Kläser, C. Schmid, and C. Liu, Dense Trajectories and Motion Boundary Descriptors for Action Recognition, International Journal of Computer Vision, vol.73, issue.2, 2013.
DOI : 10.1007/s11263-012-0594-8

URL : https://hal.archives-ouvertes.fr/hal-00725627

M. Werlberger, W. Trobin, T. Pock, A. Wedel, D. Cremers et al., Anisotropic Huber-L1 Optical Flow, Procedings of the British Machine Vision Conference 2009, 2009.
DOI : 10.5244/C.23.108

J. Wills, S. Agarwal, and S. Belongie, A Feature-based Approach for Dense Segmentation and Estimation of Large Disparity Motion, International Journal of Computer Vision, vol.II, issue.12, 2006.
DOI : 10.1007/s11263-006-6660-3

L. Xu, J. Jia, and Y. Matsushita, Motion detail preserving optical flow estimation, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2010.5539820

H. Zimmer, A. Bruhn, and J. Weickert, Optic Flow in Harmony, International Journal of Computer Vision, vol.28, issue.4, 2011.
DOI : 10.1007/s11263-011-0422-6