Stereo Matching Using Iterative Dynamic Programming Based on Color Segmentation of Images

The traditional dynamic programming stereo matching algorithms usually adopt the disparity assumption based on the intensity change of images; With the development of stereo matching technique, the disparity assumption based on  image color segmentation is proved to meet better the need of true scenes. The paper introduces the disparity assumption into the stereo matching using dynamic programming, proposes a new global energy function, which not only resolves the problem of traditional dynamic programming stereo matching algorthm that the energy function is short of intensity and disparity constraints between scan lines but also can be computed more exactly because the adopted dissimillarity function propsed by Birchfield is extended from 2-connect neighborhood to 8-connect neighborhood. The energy function  can converge fast because of the proposed pruning algorithm based on color segmentation. The experiments show that the proposed method produces competitive results with the 2-dimensional energy function minimized algorithm but has the much lower computing cost than them.