Learning the Regularization Operator for the Optical Flow Problem (original) (raw)

References

1. Horn, B.K.P. and Schunck, B.G., Determining optical flow, Artif. Intell., 1981, vol. 17, nos. 1–3, pp. 185–203.
   Article Google Scholar
2. Sun, D., Roth, S., and Black, M.J., Secrets of optical flow estimation and their principles, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2010, pp. 2432–2439
   Google Scholar
3. Chen, Q. and Koltun, V., Full flow: Optical flow estimation by global optimization over regular grids, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4706–4714
   Google Scholar
4. Menze, M., Heipke, C., and Geiger, A., Discrete optimization for optical flow, Proc. German Conf. Pattern Recognition, 2015, pp. 16–28
   Google Scholar
5. Güney, F., Geiger, A., Deep discrete flow, Proc. Asian Conf. Computer Vision, 2016, pp. 207–224
   Google Scholar
6. Weinzaepfel, Ph., Revaud, J., Harchaoui, Z., et al., DeepFlow: Large displacement optical flow with deep matching, Proc. IEEE Int. Conf. Computer Vision, 2013, pp. 1385–1392
   Google Scholar
7. Hu, Y., Li, Y., and Song, R., Robust interpolation of correspondences for large displacement optical flow, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2017.
   Google Scholar
8. Zweig, Sh. and Wolf, L., InterpoNet: A brain inspired neural network for optical flow dense interpolation, Preprint no. 1611.09803, 2016.
   Google Scholar
9. Revaud, J., Weinzaepfel, Ph., Harchaoui, Z., et al., Epicflow: Edge-preserving interpolation of correspondences for optical flow, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1164–1172
   Google Scholar
10. Min Ba., Wenjie Luo, Kaustav Kundu, et al., Exploiting semantic information and deep matching for optical flow, Proc. European Conf. Computer Vision, 2016, pp. 154–170
    Google Scholar
11. Sevilla-Lara, L., Sun, D., Jampani, V., et al., Optical flow with semantic segmentation and localized layers, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3889–3898
    Google Scholar
12. Hur, J. and Roth, S., Joint optical flow and temporally consistent semantic segmentation, Proc. European Conf. Computer Vision, 2016, pp. 163–177
    Google Scholar
13. Dosovitskiy, A., Fischer, Ph., Ilg, E., et al., Flownet: Learning optical flow with convolutional networks, Proc. IEEE Int. Conf. Computer Vision, 2015, pp. 2758–2766
    Google Scholar
14. Ilg, E., Mayer, N., Saikia, T., et al., Flownet 2.0: Evolution of optical flow estimation with deep networks, Preprint no. 1612.01925, 2016.
    Google Scholar
15. Xu, J., Ranftl, R., and Koltun, V., Accurate optical flow via direct cost volume processing, Preprint no. 1704. 07325, 2017.
    Google Scholar
16. Rudin, L.I., Osher, S., and Fatemi, E., Nonlinear total variation based noise removal algorithms, Phys. D (Amsterdam, Neth.), 1992, vol. 60, nos. 1–4, pp. 259–268.
    Article MathSciNet MATH Google Scholar
17. Zach, Ch., Pock, Th., and Bischof, H., A duality based approach for realtime TV-L1 optical flow, Pattern Recognit., 2007, pp. 214–223
    Chapter Google Scholar
18. Wedel, A., Pock, Th., Zach, Ch., et al., An improved algorithm for TV-L1 optical flow, Statistical and Geometrical Approaches to Visual Motion Analysis, Springer, 2009, pp. 23–45
    Google Scholar
19. Bredies, K., Kunisch, K., and Pock, Th., Total generalized variation, SIAM J. Image Sci., 2010, vol. 3, no. 3, pp. 492–526.
    Article MathSciNet MATH Google Scholar
20. Knoll, F., Bredies, K., Pock, Th., et al., Second order total generalized variation (TGV) for MRI, Magn. Reson. Med., 2011, vol. 65, no. 2, pp. 480–491.
    Article Google Scholar
21. Ranftl, R., Gehrig, S., Pock, Th., et al., Pushing the limits of stereo using variational stereo estimation, Proc IEEE Intelligent Vehicles Symp. (IV), 2012, pp. 401–407
    Google Scholar
22. Werlberger, M., Convex approaches for high performance video processing, 2012.
    Google Scholar
23. Werlberger, M., Pock, Th., and Bischof, H., Motion estimation with non-local total variation regularization, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2010, pp. 2464–2471
    Google Scholar
24. Ranftl, R., Bredies, K., and Pock, Th., Non-local total generalized variation for optical flow estimation, Proc. European Conf. Computer Vision, 2014, pp. 439–454
    Google Scholar
25. Steinbrucker, F., Pock, Th., and Cremers, D., Large displacement optical flow computation without warping, Proc. 12th IEEE Int. Conf. Computer Vision, 2009, pp. 1609–1614
    Google Scholar
26. Schmidt, U. and Roth, S., Shrinkage fields for effective image restoration, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2014, pp. 2774–2781
    Google Scholar
27. Lefkimmiatis Stamatios, Non-local color image denoising with convolutional neural networks, Preprint no. 1611.06757, 2016.
    Google Scholar
28. Roth, S. and Black, M.J., Fields of experts: A framework for learning image priors, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2005, vol. 2, pp. 860–867.
    Google Scholar
29. Wang, Sh., Fidler, S., and Urtasun, R., Proximal deep structured models, Advances in Neural Information Processing Systems, 2016, pp. 865–873
    Google Scholar
30. Jaderberg, M., Simonyan, K., Zisserman, A., et al., Spatial transformer networks, Advances in Neural Information Processing Systems, 2015, pp. 2017–2025
    Google Scholar
31. Butler, D.J., Wulff, J., Stanley, G.B., et al., A naturalistic open source movie for optical flow evaluation, Proc. European Conf. Computer Vision, 2012. pp. 611–625.
    Google Scholar
32. Kingma, D. and Ba, J., Adam: A method for stochastic optimization, Preprint no. 1412.6980, 2014.
    Google Scholar
33. Brox, Th. and Malik, J., Large displacement optical flow: Descriptor matching in variational motion estimation, IEEE Trans. Pattern Anal. Mach. Intell., 2011, vol. 33, no. 3, pp. 500–513.
    Article Google Scholar
34. Sundaram, N., Brox, Th., and Keutzer, K., Dense point trajectories by GPU-accelerated large displacement optical flow, Proc. European Conf. Computer Vision, 2010, pp. 438–451
    Google Scholar
35. Revaud, J., Weinzaepfel, Ph., Harchaoui, Z., et al., Deepmatching: Hierarchical deformable dense matching, Int. J. Comput. Vision, 2016, vol. 120, no. 3, pp. 300–323.
    Article MathSciNet Google Scholar
36. Meister, S., Hur, J., and Roth, S., UnFlow: Unsupervised learning of optical flow with a bidirectional census loss, Preprint no. 1711.07837, 2017.
    Google Scholar
37. Bao Linchao, Yang Qingxion., and Jin Hailin, Fast edge-preserving patchmatch for large displacement optical flow, Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2014, pp. 3534–3541
    Google Scholar
38. Brox, Th., Bruhn, A., Papenberg, N., et al., High accuracy optical flow estimation based on a theory for warping, Comput. Vision ECCV, 2004, pp. 25–36
    Google Scholar
39. Linchao Bao, Hailin Jin, Byungmoon Kim, et al., A comparison of TV-L1 optical flow solvers on GPU, Proc. GPU Technol. Conf. (GTC) Posters, 2014, pp. 42–54
    Google Scholar

Download references