Heterogeneous scene matching based on the gradient direction distribution field

Abstract

AbstractHeterogeneous scene matching is a key technology in the field of computer vision. The image rotation problem is popular and difficult in the field of heterogeneous scene matching. In this paper, a heterogeneous scene matching method based on the gradient direction distribution field is proposed, and distributed field theory is introduced into heterogeneous scene matching for the first time. First, the distribution field of the gradient direction is constructed and fuzzified, and then the effective regions are selected. Then, the distribution field of the main direction is defined to solve the matching errors due to the existence of rotational transformations between heterogeneous source images. Third, the chi-square distance is introduced as a similarity measure. Finally, the hill-climbing method search strategy, which greatly improves the efficiency of the algorithm, is adopted. Experimental results on 8 pairs of infrared and visible heterogeneous images demonstrate that the proposed method outperforms the other state-of-the-art region-based matching methods in terms of the robustness, accuracy, and real-time performance.