Low overlap image registration based on both entropy and mutual information measures

In this paper, a new approach to image matching based on both entropy and mutual information measures that can correctly perform association under very low overlap conditions is presented. The method is feature-based, working on a list of interest points detected on the acquired images. Unlike the classical SSD/RanSaC method, our algorithm is robust to the existence of many similar regions in the images, enabling us to handle situations where the interest points correspond to local details of an habitat that is dispersed with a regular-like structure across the ocean bottom. Actually, our method requires only that four corresponding points be detected in the two images being aligned. This allows matching under minimal overlap of the images. The improvement in efficiency of our method, when compared to previous techniques, relies on explicitly accounting for ambiguities in the association between the templates of the two images, thus preventing that useful information be discarded at an early association step.