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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
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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.
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.
1 Google Scholar 
2 CiteSeerX 
3 Scribd 
4 SlideShare 
5 PdfSR 
A. Bab-Hadiashar, N. Gheissari, and D. Suter, “Robust Model Based Motion Segmentation,” ICPR 2002, Quebec, Canada, 2002, pp. 753-757.
A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, New Jersey, 1989.
A. M. Tekalp, Digital Video Processing, Prentice-Hall, New Jersey, 1995.
A. M. Thompson, J. C. Brown, J. W. Kay, D. M. Titterington, “A study of methods for choosing the smoothing parameter in image restoration by regularization,” IEEE Trans. P.A.M.I., Vol. 13, No. 4, 1991, pp. 326-339.
A. Petrovic, O. Divorra Escoda and P. Vandergheynst, “Multiresolution segmentation of natural images: from linear to non-linear scale-space representations,” IEEE Transactions on Image Processing, Vol. 13, No 8, 2004, pp. 1104-1114.
G. H. Golub, M. Heath, G.Wahba, “Generalized cross-validation as a method for choosing a good ridge parameter,” Technometrics, Vol. 21, No. 2, 1979, pp. 215-223.
G. Wahba, Spline Models for Observational Data, SIAM, Philadelphia, 1990.
J. Biemond, L. Looijenga, D. E. Boekee, R. H. J. M. Plompen, “A pel-recursive Wienerbased displacement estimation algorithm,” Signal Proc., 13, 1987, pp. 399-412.
J. C. Brailean, A. K. Katsaggelos, “Simultaneous recursive displacement estimation and restoration of noisy-blurred image sequences,” IEEE Trans. Image Proc., Vol. 4, No. 9, 1995, pp. 1236-1268.
J.L. Barron, D.J. Fleet, and S.S. Beauchemin, “Performance of optical flow techniques,” International Journal of Computer Vision, 12, 1994, pp. 43–77.
MPEG-7 Overview, ISO/IEC JTC1/SC29/WG11 WG11N6828, 2004.
N. Gheissari, and A. Bab-Hadiashar, “Motion analysis: model selection and motion segmentation,” Proceedings of 12th International Conference on Image Analysis and Processing, 2003, pp. 442-448.
N. P. Galatsanos, A. K. Katsaggelos, “Methods for choosing the regularization parameter and estimating the noise variance in image restoration and their relation,” IEEE Trans. Image Proc., Vol. 1, No. 3, 1992, pp. 322-336.
R. Kapela and A. Rybarczyk, “Real-time shape description system based on MPEG-7 descriptors,” Journal of Systems Architecture, Volume 53, Issue 9, 2007, pp. 602-618.
R. Kapela, and A. Rybarczyk, “The neighboring pixel representation for efficient binary image processing operations,” International Symposium on Parallel Computing in Electrical Engineering (PARELEC'06), 2006, pp. 396-404.
T. Ebrahimi, Y. Abdeljaoued, R. Figueras i Ventura, and O. Divorra Escoda, “MPEG-7 Camera,” IEEE Proc. Int. Conference on Image Processing (ICIP), Thessaloniki, Greece, Oct. 2001.
V. V. Estrela, N. P. Galatsanos, “Spatially-adaptive regularized pel-recursive motion estimation based on cross-validation,” ICIP 98 Proceedings (2), 1998, pp. 200-203.
Associate Professor Alessandra Martins Coelho
CEFET-Rio Pomba - Brazil
Associate Professor Vania Vieira Estrela
UFF - Brazil
vestrela@id.uff.br


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