Abstract
In this thesis, we present a novel algorithm to infer disparity map from given a pair of rectified images. We first employ image over-segmentation to construct a Content-based Hierarchical Markov Random Field (CHMRF). This image representation contains two advantages for vision applications. One is the hierarchical MRF construction, and the other is the regular graph structure. The former has been widely applied to computer vision problems to improve the efficiency in MRF optimization. The latter can simplify the message passing and hardware implementation of MRF optimization techniques. After the construction of CHMRF, we perform symmetric stereo matching and occlusion handing using Hierarchical Belief Propagation (HBP) based on the proposed graphical model. Finally, a refinement process for the disparity map is introduced (e.g. plane fitting or bilateral filtering) to reduce the disparity errors caused by occlusion, textureless region or image noise, etc. Our experimental results show that we can efficiently obtain disparity maps of comparable accuracy when compared to most global stereo algorithms. For real stereo video sequences, we are able to accurately estimate the depth information for each frame with the pre-processing of robust self image rectification.