A sub-pixel stereo matching algorithm and its applications in fabric imaging

Abstract

In this paper, we describe a sub-pixel stereo matching algorithm where disparities are iteratively refined within a regularization framework. We choose normalized cross-correlation as the matching metric, and perform disparity refinement based on correlation gradients, which is distinguished from intensity gradient-based methods. We propose a discontinuity-preserving regularization technique which utilizes local coherence in the disparity space image, instead of estimating discontinuities in the intensity images. A concise numerical solution is derived by parameterizing the disparity space with dense bicubic B-splines. Experimental results show that the proposed algorithm performs better than correlation fitting methods without regularization. The algorithm has been implemented for applications in fabric imaging. We have shown its potentials in wrinkle evaluation, drape measurement, and pilling assessment.