An invariant feature extraction for multi-modal images matching
Abstract
This paper aims at providing an effective multi-modal images invariant feature extraction and matching algorithm for the application of multi-source data analysis. Focusing on the differences and correlation of multi-modal images, a feature-based matching algorithm is implemented. The key technologies include phase congruency (PC) and Shi-Tomasi feature point for keypoints detection, LogGabor filter and a weighted partial main orientation map (WPMOM) for feature extraction, and a multi-scale process to deal with scale differences and optimize matching results. The experimental results on practical data from multiple sources prove that the algorithm has effective performances on multi-modal images, which achieves accurate spatial alignment, showing practical application value and good generalization.
Cite
@article{arxiv.2311.02842,
title = {An invariant feature extraction for multi-modal images matching},
author = {Chenzhong Gao and Wei Li},
journal= {arXiv preprint arXiv:2311.02842},
year = {2023}
}