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Proceedings Paper

Adaptive mixed norm optical flow estimation
Author(s): Vania Vieira Estrela; Matthias O. Franz; Ricardo T. Lopes; A. P. De Araújo
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Paper Abstract

The pel-recursive computation of 2-D optical flow has been extensively studied in computer vision to estimate motion from image sequences, but it still raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. It relies on spatio-temporal brightness variations due to motion. Our proposed adaptive regularized approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Mixed Norm (MN) to estimate the best motion vector for a given pixel. In our model, various types of noise can be handled, representing different sources of error. The motion vector estimation takes into consideration local image properties and it results from the minimization of a mixed norm functional with a regularization parameter depending on the kurtosis. This parameter determines the relative importance of the fourth norm and makes the functional convex. The main advantage of the developed procedure is that no knowledge of the noise distribution is necessary. Experiments indicate that this approach provides robust estimates of the optical flow.

Paper Details

Date Published: 24 June 2005
PDF: 8 pages
Proc. SPIE 5960, Visual Communications and Image Processing 2005, 59603W (24 June 2005); doi: 10.1117/12.632674
Show Author Affiliations
Vania Vieira Estrela, LCMAT-CCT, Univ. Estadual do Norte Fluminense (Brazil)
Matthias O. Franz, Max Planck Institute for Biological Cybernetics (Germany)
Ricardo T. Lopes, Nuclear Instrumentation Lab./EE, COPPE/UFRJ (Brazil)
A. P. De Araújo, Univ. Estadual do Norte Fluminense (Brazil)

Published in SPIE Proceedings Vol. 5960:
Visual Communications and Image Processing 2005
Shipeng Li; Fernando Pereira; Heung-Yeung Shum; Andrew G. Tescher, Editor(s)

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