Adaptive hierarchical stereo matching using object segmentation and window warping

In this paper, we propose an adaptive stereo matching algorithm to treat stereo matching problems in projective distortion regions. Since the disparities in the projective distortion region can not be estimated in terms of fixed-size block matching algorithm, an adaptive window warping method with hierarchical matching process is used to compensate perspective distortions. In addition, a probability model, based on the statistical distribution of matched errors and constraint functions, is adopted to handle the uncertainty of matching points. Since the proposed window warping process is based on a statistical window warping step with the reliability estimation of matching points, any relaxation process need not to use. As a result, overall processing time is reduced, compared with conventional stereo matching algorithm including a relaxation step, and improved matching results are obtained. Experimental results on both disparity map and 3D model view show that the proposed matching algorithm is effective for various images, even if the image has projective distortion regions and repeated patterns.