Adaptive Local Stereo Matching Based on Improved Census Transform

Abstract

The local stereo matching algorithm based on traditional census transform has some problems, such as over-reliance on the center pixel, fixed transform window and poor matching effect in weak texture region. An improved census transform stereo matching algorithm combined with adaptive window is proposed. Firstly, according to the mean square error of pixel gray level in the initial transformation window, the size of the transformation window is adjusted. Secondly, the census transform is improved and combined with the improved AD algorithm to form the initial matching cost; Then, the cost aggregation adopts the cross domain algorithm; Finally, the winner-take-all algorithm and multi-step optimization are used to obtain disparity map. Experimental results show that the proposed algorithm has good anti-interference performance against noise. By evaluating the standard images on Middlebury dataset, the average error of this method is reduced by 7.05 ^% compared with the traditional census transform stereo matching algorithm.