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Smoothing of optical flow using robustified diffusion kernels

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Publication details

JournalImage and Vision Computing
DatePublished - Dec 2010
Issue number12
Volume28
Number of pages15
Pages (from-to)1575-1589
Original languageEnglish

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. (c) 2010 Elsevier B.V. All rights reserved.

    Research areas

  • Anisotropic diffusion, Robust statistics, Optical flow smoothing, BASIS FUNCTION NETWORK, ANISOTROPIC DIFFUSION, NONLINEAR DIFFUSION, EDGE-DETECTION, IMAGE, SEGMENTATION, REGULARIZATION, PDES

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