A robust visible and infrared image matching algorithm for power equipment based on phase congruency and scale-invariant feature

•An intensity invariant feature detection method based on phase congruency and corner detector is proposed to detect feature points with high repeatability.•A novel descriptor named the histogram of phase congruency orientation (HPCO) is proposed for feature description.•An effective refinement strategy is devised to achieve dense and accurate feature matching.•Performance of the visible and infrared image matching algorithm is validated with experiments carried out on power equipment images. Dense and accurate matching for visible and infrared images of power equipment is crucial to intelligent diagnosis system of power grid, but existing matching methods usually fail in aligning visible and infrared image pairs because of significant intensity, resolution and viewpoint differences. In this paper, we propose a matching algorithm based on phase congruency and scale-invariant feature to address this problem. The proposed method consists of four steps. First, the maximum moment map of phase congruency of input image is computed based on phase congruency theory, which is then used to enhance the raw image. Second, Canny operator and contour tracking method are employed to detect image contours and scale-invariant feature points are extracted by the curvature scale space (CSS) corner detector. Third, the novel histogram of phase congruency orientation (HPCO) descriptors based on phase congruency information are computed for all feature points. Finally, a set of preliminary matches is obtained by the bidirectional matching, and refinement procedures are implemented to achieve dense and accurate matching results. We conduct the experiments on public available dataset. Experimental results show that the proposed method can robustly match feature points in visible and infrared image pairs of power equipment in spite of intensity, resolution and viewpoint differences, and achieve favorable performance compared to state-of-the-art approaches.