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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
Published in International Journal of Image Processing (IJIP)
MORE INFORMATION
KEYWORDS
Motion Estimation, Cross-Validation, Regularization, Inverse Problems in Image Processing, Model validation
ABSTRACT
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.
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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
vestrela@id.uff.br
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