Abstract: Abstract: Aiming at the measure selection problem of stereo matching algorithm based on multi-measure fusion, a measure selection method based on measure complementarity coefficient is proposed. Using this method, the present study fuses multiple measures as the matching cost, and adopts the improved semi-global algorithm for cost aggregation to realize the stereo matching algorithm of multi-measures fusion. Firstly, the complementary coefficients are defined, and a variety of similarity measures are fused by the complementary coefficients as matching costs. Then, in attempt to solve the problem of poor stereo matching effect caused by semi-global cost aggregation using randomly initialized disparity map, we carried out the semi-global cost aggregation taking disparity based on SURF features as initial disparity. Finally, the disparities are calculated and optimized to obtain the final disparity map. The experimental results show that the complementary features can be selected by using the measure selection method, and the stereo matching effect can be improved by combining the improved semi-global cost aggregation method.

Key words: Keywords: multi-measure fusion, measure complementarity coefficient, semi-global cost aggregation, stereo matching