Research And Application On Stereo Matching

To solve those problems on stereo vision in computer vision, we have studied on stereo rectification, the optimization and parallelization of belief propagation based on MRF and the static object segmentation based on disparity map.The main work and innovation of this dissertation are as follows:1) A method of stereo rectification with uncalibrated cameras is proposed.In this method,we at first detect and match the interest points to achieve the spatial relationship between the two images, and then finish the rectification by finding the projections in which the epipolar lines run parallel with the x-axis according to the epipolar geometry. Experimental results show that this method can rectify the stereo pairs accurately and meet the requirement of stereo matching.2) A self-adaptive algorithm with convergence detection to reduce the computational complexity of HBP is proposed. Convergence detection is introduced to stop the iterations of messages which are already converged to optimal values. Thus the overall computational time is reduced. Experimental results show the self-adaptive algorithm reduces computational time effectively, and the computational time is insensitive with iteration up bound. The convergence detection methodology can also be applied to other HBP related applications.3) An efficient CUDA-based graphic processing unit is introduced into implementation of the belief propagation algorithm. After analysis for the belief propagation algorithm, we achieve the parallelization on pixel-level by CUDA.Experimental results show that this method can be used to speed up stereo image processing without much loss of accuracy.4) An object segmentation algorithm based on accurate disparity map is presented. The accurate disparity map is available by a stereo match algorithm including initial matching cost estimation, mismatched pixels checking, plane estimation and self-adaptive hierarchical belief-propagation. Then, self-adaptive threshold segmentation is performed on the result that is achieved from the first step. Experimental results show that the proposed algorithm is an effective object extraction method suitable for stereoscopic static scenes and image sequences with unitary global motion.