A Laplacian spectral method for stereo correspondence

This paper presents a novel algorithm of stereo correspondence by using Laplacian spectra of graphs. Firstly, according to the feature points of two images to be matched, a Laplacian matrix with Gaussian-weighted distance is defined and a closed-form solution is given in terms of the matching matrix constructed on the vectors of eigenspace of the Laplacian matrix. Secondly, we introduce a new method to judge correspondences by using doubly stochastic matrix. Thirdly, in order to render our method robust, we describe an approach to embedding the Laplacian spectral method within the framework of iterative correspondence and transformation estimation. Experimental results show the feasibility and comparatively high accuracy of our methods.