Classifying Estimated Stereo Correspondences Based on Delaunay Triangulation

The stereo vision problem gives rise to two sub-problems: the correspondence and the stereo calibration. Once the sub-problems have been solved, depth is estimated by triangulation. Depth estimation, using stereo images, is based on disparity – the relative displacement between corresponding points – and the camera geometry. Small errors in disparity may produce large errors in depth estimates due to the ill-posedness nature of the stereo vision problem. Moreover, if the camera geometry is unknown, it is estimated using corresponding points. A solution of stereo vision problem may be improved by identifying corresponding points which are inaccurately estimated. In this paper, the classification of a set of estimated corresponding points uses Delaunay triangulation by restricting it to a given subset of estimated corresponding points. Delaunay edges among estimated corresponding points are used to build undirected graphs. Classification criteria based on adjacencies are defined in order to decide whether or not a corresponding point is correctly estimated. Experimental evaluation, using ground truth image sets for quantitative analysis, shown values of specificity around 70% while sensitivity up to 96%.