Trellis-based parallel stereo matching

We present a center-referenced basis for discrete representation of stereo correspondence that includes new occlusion nodes. This basis improves the inclusion of constraints and the parallelism of the final algorithm. Disparity estimation is formulated in a MAP context and natural constraints are incorporated, resulting in an optimal path problem in a sparsely connected trellis. Like other dynamic programming methods, the computational complexity is low at O(MN²) for M×N pixel images. However, this method is better suited to parallel solution, scaling up to O(MN) processors. Experimental results confirm the performance of this method