Fuzzy Encoding Pattern for Stereo Matching Cost

Abstract

We propose a novel fuzzy encoding pattern that fuzzily encodes the relative orders between pixel pairs. An image window is divided into disjoint neighboring pixel sets for the window’s center pixel, and the relative order is established not only between the center pixel and its neighbors but also between the pixel pairs in each neighboring pixel set. The relative orders are fuzzily encoded to extract more detailed information from a local structure. We successfully apply the pattern as a matching cost function for stereo correspondence under severe radiometric variations. We conduct experiments using the proposed matching cost function and compare it with functions employing the census transform, supporting local binary pattern, and adaptive normalized cross correlation, as well as a mutual information-based matching cost function, using different stereo data sets. Compared with the census transform, the proposed function reduces the error from 33.1% to 16.9% in the Middlebury data set and from 17.6% to 9.5% in the Kitti data set. The experimental results indicate that the proposed function is superior to the state-of-the-art functions under radiometric variations. In addition, the proposed function is faster than recently developed functions, such as the adaptive normalized cross correlation, a mutual information-based function, and support local binary pattern.