Kernel-Predictability: A New Information Measure and Its Application to Image Registration

A new information measure for probability distributions is presented; based on it, a similarity measure between images is derived, which is used for constructing a robust image registration algorithm based on random sampling, similar to classical approaches like mutual information. It is shown that the registration method obtained with the new similarity measure shows a significantly better performance for small sampling sets; this makes it specially suited for the estimation of non-parametric deformation fields, where the estimation of the local transformation is done on small windows. This is confirmed by extensive comparisons using synthetic deformations of real images.

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