Research On Stereo Matching In Binocular Vision And Its Implementation

Computer vision is one of the most popular branches of artificial intelligence.Binocular Stereo Vision,which as a branch of Computer vision can enable computers to perceive the world,and is hot technology.Stereo Matching is a key technique of computer Stereo Vision.It restores the three-dimensional information of the object through two different angles of parallax image,and is widely used in object recognition?navigation?industrial parts and measuring industry.In this thesis,Camera Calibration,Fundamental Matrix estimation and Stereo Matching for binocular stereo vision is well studied.The main work of this paper is as follows:1.First of all,by using Opencv and the Calibration Toolbox of Matlab,two cameras on the left side and right side are calibrated separately based on Zhang Zhengyou plane calibration.Then the results are processed using Stereo Calibration model to complete the Stereo Calibration of camera.2.In the collected images,preprocessing methods such as image graying,image smoothing and contrast enhancement are used to enhance the key part of the images,so that the quality and feature recognition of the images can be improved,making the feature point extraction easier.3.Algorithm of Mundamental Matrix is well studied.In consideration of the time-consuming defects of RANSAC algorithm,an improved RANSAC algorithm is proposed.Before the internal and external inspection of the samples,T(c,d)is introduced to do the pre detection for the subset of the samples,so that the internal and external inspection time of bad samples is reduced.In the process of sampling the original sampling space,a new interior point sampling set is constructed based on the sampling results.Cross sampling between the original sampling space and the new interior point set is done to reduce the time required for the sampling of the good samples.Through the experimental results and analysis,in the cases of similar accuracy,the timeliness of the improved algorithm is significantly improved.Compared to the original algorithm,the algorithm execution time can be reduced by about 20%.4.Stereo Matching based on feature points is realized.First of all the feature points of left and right images are extracted by Harris corner algorithm,and the initial matching is established according to the gray correlation criterion.Then the relaxation iterative method is used to remove the false matching,establishing the robust matching of medium intensity.Finally the Fundamental Matrix is solved using the improved RANSAC algorithm,and the matching points set is further debugged and expanded according to the polar constraint relation.