Abstract
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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Gómez-García, H.F., Marroquín, J.L., Van Horebeek, J. (2006). Kernel-Predictability: A New Information Measure and Its Application to Image Registration. In: Leonardis, A., Bischof, H., Pinz, A. (eds) Computer Vision – ECCV 2006. ECCV 2006. Lecture Notes in Computer Science, vol 3953. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11744078_39
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DOI: https://doi.org/10.1007/11744078_39
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