Three-dimensional Reconstruction Of The Stereo Matching Problem,

Stereo matching is the key problem in Stereo Vision. Area based stereo matching needs to get information from the neighbor region when computing the depth of a point. How to select size and shape of supported window with strong adaptive activity is the key problem in area based stereo matching. This thesis firstly analyzes the every computing stage effect on accuracy and time cost for computing disparity image. Based on this analysis, a new stereo matching algorithm of adaptive windows via region growing is proposed. This region growing method can obtain appropriate size and shape of supported windows adaptively. In addition to this, a new cost matching function which have good adaptability is proposed. This function can eliminate the linear errors and the non-linear errors in theory.In stereo matching, the edge problem and the occluded areas problem always affect the accuracy of the disparity image. Here, the value of DSI function and the uniqueness assumption are mixed together to solve it. Aimed at reducing time cost, estimating the scope of disparity, approximating the disparity of the core window, adjusting the step dynamically, restricting edge are used to improve the algorithm. The result proves that all of them can decrease the time cost effectively. In order to verify the accuracy of the result, tow standard functions are introduced into this paper. The experiment result proves that this strategy can get good accuracy disparity image. Finally, a test bed is given to evaluate the algorithms that are appeared in this thesis.