Trinocular stereo matching with composite disparity space image

In this paper we propose a method that smartly improves occlusion handling in stereo matching using trinocular stereo. The main idea is based on the assumption that any occluded region in a matched stereo pair (middle-left images) in general is not occluded in the opposite matched pair (middle-right images). Then two disparity space images (DSI) are merged in one composite DSI. The proposed integration differs from the known approach that uses a cumulative cost. The experimental results are evaluated on the Middlebury data set, showing high performance of the proposed algorithm especially in the occluded regions. Our method solves the problem on the base of a real matching cost, in such a way a global optimization problem is solved just once, and the resultant solution does not have to be corrected in the occluded regions. In contrast, the traditional methods that use two images approach have to complicate a lot their algorithms by additional add hog or heuristic techniques to reach competitive results in occluded regions.