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Data-Driven Motion Estimation With Spatial Adaptation
Alessandra Martins Coelho, Vania Vieira Estrela
Pages - 54 - 67     |    Revised - 15-01-2012     |    Published - 21-02-2012
Volume - 6   Issue - 1    |    Publication Date - February 2012  Table of Contents
Motion Estimation, Cross-Validation, Regularization, Inverse Problems in Image Processing, Model validation
The pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. Our proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalised Cross Validation to estimate the best regularisation scheme for a given pixel. In our model, the regularisation parameter is a general matrix whose entries can account for different sources of error. The motion vector estimation takes into consideration local image properties following a spatially adaptive approach where each moving pixel is supposed to have its own regularisation matrix. Preliminary experiments indicate that this approach provides robust estimates of the optical flow.
CITED BY (3)  
1 Coelho, A. M., & Estrela, V. V. (2012). A study on the effect of regularization matrices in motion estimation. International journal of computer applications, 51(19), 17.
2 Estrela, V. V., & Coelho, A. M. (2012). State-of-the Art Motion Estimation in the Context of 3D TV. Multimedia Networking and Coding, 148.
3 Coelho, A. M., Estrela, V. V., do Carmo, F. P., & Fernandes, S. R. (2012). Error concealment by means of motion refinement and regularized bregman divergence. In Intelligent Data Engineering and Automated Learning-IDEAL 2012 (pp. 650-657). Springer Berlin Heidelberg.
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Associate Professor Alessandra Martins Coelho
CEFET-Rio Pomba - Brazil
Associate Professor Vania Vieira Estrela
UFF - Brazil