Disparity and occlusion estimation for multiview image sequences using dynamic programming
An efficient disparity estimation and occlusion detection and characterization algorithm for multiocular systems is presented. A dynamic programming algorithm, using a multiview matching cost as well as pure geometrical constraints, is used to provide an estimate of the disparity field and to identify occluded areas. An important advantage of this approach is that not only are the occluded points simultaneously detected, but they are also characterized by the number of views where each point is occluded. Specifically, a "state" map describing the number of matches for each imaged pixel and thus identifying occluded points in the multiview sequence is produced. The disparity and state information is then applied to obtain virtual images from intermediate viewpoints. Experimental results, obtained using a four-view image sequence, illustrate the performance of the proposed technique.