Performance evaluation of the non-local cost aggregation in stereo matching algorithm

Abstract

This paper presents the evaluation of the efficiency and accuracy of the stereo matching algorithm using a census transform with the non-local aggregation technique. This technique is proposed to reduce the average error of the overall matching process in order to produce an accurate disparity map and to access the efficiency of the matching algorithm. Fundamentally, the stereo matching process highly depends on the quality of the image pair. The properties different between left and right images such as image brightness, exposure, occlusion and less texture area have a significant influence on the matching algorithm's accuracy. Thus, this paper analysed the performance of the stereo matching algorithm using Census Transform at matching cost computation, the non-local technique of cost aggregation and winner-take-all strategy at cost optimization. The initial disparity value obtained in cost optimization is used as the final disparity map. The disparity maps obtained from three different aggregation methods are evaluated in terms of accuracy and processing time. The experimental results using the Middlebury dataset show that the non-local method is more accurate compared with other aggregation methods and while maintaining the efficiency of the algorithm.