Two-step mutual information-based stereo matching
A fast mutual information (MI) based stereo matching method is proposed that estimates the optimal intensity transfer function in a Bayesian framework. Previous MI-based stereo matching methods require several iterations, where each iteration includes both data cost computation and global energy optimisation such as dynamic programming and graph-cuts. These computations are redundant and time-consuming. In this Letter, a fast two-step MI-based stereo matching method by combining local feature correspondences and global statistics constraints is proposed. Experimental results show that the method is fast, yet generates results comparable with the original MI-based method.