Stereo matching algorithm based on illumination normal similarity and adaptive support weight

Kai Gao; He-xin Chen; Yan Zhao; Ying-nan Geng; Gang Wang

doi:10.1117/1.OE.52.2.027201

1 February 2013 Stereo matching algorithm based on illumination normal similarity and adaptive support weight

Kai Gao, He-xin Chen, Yan Zhao, Ying-nan Geng, Gang Wang

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Optical Engineering, Vol. 52, Issue 2, 027201 (February 2013). https://doi.org/10.1117/1.OE.52.2.027201

Abstract

For the purpose of representing the feature of the gray image, illumination normal of pixels in a two-dimensional gray image plane is proposed, which can reflect the high-frequency information of the gray image. In order to get an accurate dense disparity map based on the adaptive support weight (ASW) approach in RGB vector space, a matching algorithm is proposed that combines the illumination normal similarity, gradient similarity, color similarity, and Euclidean distance similarity to compute the corresponding support weights and dissimilarity measurements. After testing by the Middlebury stereo benchmark, the result of the proposed algorithm shows more accurate disparity than many state-of-the-art stereo matching algorithms.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Kai Gao, He-xin Chen, Yan Zhao, Ying-nan Geng, and Gang Wang "Stereo matching algorithm based on illumination normal similarity and adaptive support weight," Optical Engineering 52(2), 027201 (1 February 2013). https://doi.org/10.1117/1.OE.52.2.027201

Published: 1 February 2013

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