PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Smoothing of optical flow using robustified diffusion kernels
A. Doshi and Adrian Bors
Image and Vision Computing Volume 28, Number 12, pp. 1575-1589, 2010. ISSN 0262-8856

Abstract

This paper proposes a new optical flow smoothing methodology combining vector diffusion and robust statistics. Vector smoothing using diffusion preserves moving object boundaries and the main motion discontinuities. According to a study provided in the paper, diffusion does not remove the outliers but spreads them out, introducing a bias in the neighbourhood. In this paper robust statistics operators such as the median and alpha-trimmed mean are considered for robustifying the diffusion kernels. The robust diffusion smoothing process is extended to 3-D lattices as well. The proposed algorithms are applied for smoothing artificially generated vector fields as well as the optical flow estimated from image sequences.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8440
Deposited By:Adrian Bors
Deposited On:06 January 2012