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Image registration based on subpixel localization and Cauchy-Schwarz divergence

We define a new matching metric — corner Cauchy-Schwarz divergence (CCSD) and present a new approach based on the proposed CCSD and subpixel localization for image registration. First, we detect the corners in an image by a multiscale Harris operator and take them as initial interest points. And then, a subpixel localization technique is applied to determine the locations of the corners and eliminate the false and unstable corners. After that, CCSD is defined to obtain the initial matching corners. Finally, we use random sample consensus to robustly estimate the parameters based on the initial matching. The experimental results demonstrate that the proposed algorithm has a good performance in terms of both accuracy and efficiency.