Real-time stereo matching algorithms based on dynamic programming method

Depth of image information in stereo matching is typically calculated from the disparity map of image sets. This method suffers from expensive computation, which is unable to run in real time. This paper focuses on the problem of cost aggregation and disparity optimization calculation and proposes an improved real-time stereo matching algorithm based on the dynamic programming method. According to continuity constraints, an adaptive-shape-window-based rapid cost aggregation strategy is utilized to increase the computational efficiency of arm length and cost aggregation. By means of edge detection technology, the boundary information is obtained and the transfer equation of dynamic programming is modified such that the boundary pixels can select the disparity in the whole disparity space. Thus, the error-matching rate of disparity can be reduced without increasing computational complexity. The experiments show that the integration of the above two improvements can achieve a fairly good disparity map in real-time together with better matching accuracy.