Multimodal Image Matching using Phase Congruency-based Self-Similarity Structural Features
Jianwei Fan, Qing Xiong, Jian Li, Guichi Liu, Wanying Song · 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV) · 2022
Due to the significant differences in geometric and nonlinear intensity, multimodal image matching is still a challenging problem. To address this issue, this paper proposes a novel matching method using phase congruency (PC)-based self-similarity structural features for multimodal images. Firstly, the feature points are extracted from the PC maps of the original images by the Harris detector. Then, combined with the theory of the self-similarity, a PC-based self-similarity structural (PCSS) descriptor is designed for multimodal images. Finally, the Euclidean distance is used as the matching measure for the corresponding point recognition. Experimental results conducted on various real multimodal image pairs demonstrate that the proposed method can achieve better matching performance in terms of the number of correct matches and the registration precision in comparison with the traditional methods.