Adaptive ternary-derivative pattern for disparity enhancement

Abstract

High dynamic range conditions are major obstacles to the implementation of practical stereovision systems in real scenes. We address this problem by introducing an adaptive local ternary-derivative pattern (ALTDP) which is a fusion of the local ternary pattern (LTP) and local derivative pattern (LDP). We make three main contributions in this study: (i) ALTDP encodes more detail information than LDP by extending to eight directions; (ii) ALDTP is better at discriminating and less sensitive to noise in uniform regions with three-value encoding (−1,0,1) without using a pre-defined threshold; and (iii) ALTDP significantly improves the performance of hierarchical belief propagation (BP) by substituting ALTDP data cost for the different intensity data cost. Moreover, our proposed method performs slightly better than LBP and LDP with three datasets: synthetic sequences (set 2) in the EISATS dataset, bright differences sequences (set 5) in the EISATS dataset, and the bumblebee xb3 dataset.