Abstract
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.
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