Abstract
How to get accurate depth information is an essential issue in computer vision. Stereo matching has been proved to be the effective way to compute such dense and reliable disparity maps. In this thesis, several stereo matching algorithms are proposed to compute disparity maps for several kinds of applications, such as videos, fingertip-tracking, etc. Basically, these algorithms are local approach with the concept of weighted propagation combining both color and spatial structure simultaneously. Also, several refinement techniques are joined to improve the accuracy on border, occluded and unreliable pixels. Furthermore, for the applications of video processing, the issue of temporal consistency is considered and solved. And, for the fingertip-tracking applications, the skin-color is heavily weighted to enhance the data integrity within the hand regions. The experimental results show that the proposed algorithms provide comparable high-quality disparity maps for both images and videos and suitable for some applications, such as fingertip-tracking.