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Stereo matching based on orthogonal Gaussian-Hermite moments

Stereo correspondence methods rely on matching costs for computing the similarity of image locations. In this paper, a stereo matching cost based on orthogonal Gaussian-Hermite moments (OGHMs) space is proposed and evaluated together with different matching costs with respect to radiometric variations of the input images. Their performance is measured in the presence of global intensity changes, local intensity changes, and image noise. Using existing stereo datasets with ground-truth disparities taken under controlled changes of exposure and lighting, the experiment shows the novel matching cost method guarantee a more insensitive result against different kinds of radiometric differences.