Comparative Analysis of Stereo Matching Algorithms Computer stereo vision (CSV) is the extraction of 3D data from two pictures of a similar scene taken from two parallel vantage focuses. Stereo vision has a great importance in the robotics, image analysis, object recognition etc. The correspondence problem in stereo vision refers to the problem of ascertaining similar features in stereo images. The major challenges in corresponds are due to change in viewpoints, camera noise and difference in bias. To solve this matching problem along with these challenges, many algorithms are proposed so far. This paper investigates the current stereo vision applications. The stereo vision algorithms are classified and the comparative analysis is presented. For comparison, middle bury database are used. The grapgh cut based algorithm has least bad pixel of 4.06 % and found to be best among all the available methods.

Comparative analysis of local binocular and trinocular depth estimation approaches In this paper, we present a comparative analysis of local trinocular and binocular depth estimation techniques. Local techniques are chosen because of their higher computational efficiency compared to global approaches. Our aim is to quantify the benefits of the third camera with respect to performance and computational burden. We have adopted the colorweighted localwindow approach in stereo matching, where pixels within local spatial window around the pixel being processed are penalized by their colors in order to ensure better adaptivity to local structures. Thus, the window size becomes the main parameter which influences the quality and determines the execution time. Extensive experiments on large set of data have been carried out to test trinocular versus binocular setting in terms of quality of estimated depth and execution time. Both natural and artificial scenes have been tested. A set of quality measures has been used to support the comparisons. MPEG Depth Estimation Reference Software has been used as a reference benchmark as well. Results show that from some window size on, the trinocular setting outperforms the binocular in general: providing higher quality for less computational time. While comparisons were done for u0027pureu0027 depth estimation, we also run postprocessing on depth estimates in order to analyze the potential of estimated depths to be further improved.