Binocular stereo vision matching is currently a hot and difficult problem in computer vision.Advantages and disadvantages of several common stereo matching algorithm are analyzed.An improved method that based on image segmentation of binocular stereo matching algorithm is proposed.Firstly

images are collected by binocular vision camera

then the images are corrected and denoising processing.Secondly

images are segmented using color features algorithm

and then a fast and efficient block matching algorithm are used for stereo image matching.In matching process

small window of sum of absolute differences（SAD） is used to look for matching points between two images.Finally

the final disparity map is got through filtering.Experimental results show that the algorithm can solve mis-matching error caused by the repeat regions

the low texture

texture similar

and block regions

clearly dense and accurate disparity map can be got.