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Multimodal image registration by iteratively searching keypoint correspondences

This paper proposes a multimodal image registration algorithm through searching the best matched keypoints by employing the global information. Keypoints are detected from images from both the reference and test images. For each test keypoint, a certain number of reference keypoints are chosen as mapping candidates. A triplet of keypoint mappings determine an affine transformation, and then it is evaluated with the similarity metric between the reference image and the transformed test image by the determined transformation. An iterative process is conducted on triplets of keypoint mappings, and for every test keypoint updates and stores its best matched reference keypoint. The similarity metric is defined to be the number of overlapped edge pixels over entire images, allowing for global information being incorporated in evaluating triplets of mappings. Experimental results show that the proposed algorithm can provide more accurate registration than existing methods on EO-IR images.