Pixels Matching in No Obvious Feature Area in Binocular Vision Based on Peripheral Feature Points In binocular vision, the pixel matching of no obvious feature refers to the matching of pixels in the area where the gray value does not change significantly or in the area where there is no significant gradient change in the gray level. The basic idea is that the position of the matching pixels in the area with no obvious features can be determined by the peripheral feature points. First, the camera is calibrated by the chessboard calibration method, and the internal and external parameters of the camera are obtained. The distortion of the left and right images is corrected by the calibration results. Then, the SIFT algorithm is used to extract the feature points, the Euclidean distance threshold is set to determine the better matching points, and the random sample consensus (RANSAC) algorithm is used to eliminate the wrong matching points. Finally, four target matching points with the same distance and direction information are obtained, and the final target matching points are obtained by averaging the four target matching points. Experiments show that any pair of pixels in the unmarked area can be matched accurately.

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.