Abstract
This paper proposes an improved optical flow estimation approach based on the total variational L1 minimization technique with weighted median filter. Furthermore, recovering image details using modified census transform algorithm improves the overall accuracy of estimating large scale displacements optical flow. On the other hand, the use of the Taylor expansion approximation in most of the optical flow approaches limits the ability to estimate movement of fast objects. Hence, a coarse-to-fine scheme is used to overcome such a problem of the cost of losing small details in the interpolation process where initial values are propagated from the coarse level to the fine one. The proposed algorithm improves the accuracy of the estimation process by integrating the correspondence results of the modified census transform into the coarse-to-fine module in order to recover the lost details. The outcome of the proposed approach yields state-of-the-art results on the Middlebury optical flow evaluations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Horn, B., Schunck, B.: Determining optical flow. Artificial intelligence 17, 185–203 (1981)
Lukas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Image Understanding Workshop (1981)
Bruhn, A., Weickert, J., Schnörr, C.: Lucas/kanade meets horn/schunck: Combining local and global optic flow methods. International Journal of Computer Vision 61, 211–231 (2005)
Drulea, M., Nedevschi, S.: Total variation regularization of local-global optical flow. In: 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 318–323. IEEE (2011)
Chambolle, A.: An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision 20, 89–97 (2004)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Computer Vision-ECCV 2004, pp. 25–36 (2004)
Sun, D., Roth, S., Black, M.: Secrets of optical flow estimation and their principles. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2432–2439. IEEE (2010)
Xu, L., Jia, J., Matsushita, Y.: Motion detail preserving optical flow estimation. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1293–1300. IEEE (2010)
Liu, C., Yuen, J., Torralba, A.: Sift flow: Dense correspondence across scenes and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 978–994 (2011)
Brox, T., Malik, J.: Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 500–513 (2011)
Stein, F.: Efficient computation of optical flow using the census transform. Pattern Recognition, 79–86 (2004)
Puxbaum, P., Ambrosch, K.: Gradient-based modified census transform for optical flow. Advances in Visual Computing, 437–448 (2010)
Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Computer Visionâ ECCV 1994, pp. 151–158 (1994)
Froba, B., Ernst, A.: Face detection with the modified census transform. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 91–96. IEEE (2004)
Buades, A., Coll, B., Morel, J.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65. IEEE (2005)
Gilboa, G., Osher, S.: Nonlocal operators with applications to image processing. Multiscale Model. Simul. 7, 1005–1028 (2008)
Rashwan, H., Puig, D., Garcia, M.: On improving the robustness of differential optical flow. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 876–881. IEEE (2011)
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. International Journal of Computer Vision 92, 1–31 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mohamed, M.A., Mertsching, B. (2012). TV-L1 Optical Flow Estimation with Image Details Recovering Based on Modified Census Transform. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7431. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33179-4_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-33179-4_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33178-7
Online ISBN: 978-3-642-33179-4
eBook Packages: Computer ScienceComputer Science (R0)