Joint integral histograms and its application in stereo matching

In this paper, we first propose a technique, referred as joint integral histograms, for weighted filtering with O(1) computational complexity. The technique is built on the classic integral images and the recent integral histograms. In a joint integral histogram, instead of remembering bin occurrences, the value at each bin indicates an integral defined by two signals. Beyond the integral histograms, our method supports weighted filtering with a more general form, where the weight could be a function of a signal different from the signal to be filtered. Then, we present a local stereo matching approach as an instantiation of the technique. Using the joint integral histograms, we achieve a speedup factor of about two orders of magnitude. Thanks to the huge speedup, the stereo method is among the best local approaches in terms of the trade-off between matching accuracy and execution speed. Experimental results demonstrate the advantages of both the joint integral histograms technique and the stereo matching approach.