Stereo Matching Algorithm Based On Feature Points Is Studied

Stereo matching is to match the corresponding points in pieces of different images, and it’s the most critical step of obtaining the depth information of the images. It’s an important research direction in computer vision field. At the same time, it’s bottle-neck problem in the research of stereo vision. It’s a great challenge to investigate a robust, precise, stable and applicable stereo matching algorithm, which is both theoretically and practically valuable.This paper finished deep theoretic research and sufficient practical work on feature point extraction algorithm and the method how to structure feature descriptor for stereo matching. This paper improved the Harris feature point extraction algorithm, which presented a new feature point extraction algorithm. In order to obtain a relatively small amount of corner points, this algorithm makes an initial selection for all pixels of the images, and removes a part of ordinary pixel firstly. In this way, the range of extraction of Harris corn points is reduced, so the time complexity of the algorithm is reduced, which improves accuracy and real time of the algorithm. By using the rotational invariance of the circle, generation method of the SIFT feature descriptor is improved. Firstly, this method structures five circle areas, and each center is feature point. Then, characterization of feature points information with eigenvectors is generated by accumulate the gradients of eight directions for all Pixels in the same circle. The descriptor generated by this way has rotational invariance intrinsically. In addition, it is useful for the consecutive matching to reduce the dimension of the eigenvectors. The last, a stereo matching algorithm that is embedded in Improved algorithm is base on Harris comer and SIFT feature descriptor. Firstly, the algorithm extracts the corner points with improved Harris corner detection operator, then it generates the feature descriptors, at last, it matches to feature points by using the similarities of descriptors, and false matches are removed according to the constraint criteria. The experimental results are proved that the algorithm presented in this paper can improve the accuracy and real time.