Image registration based on kernel-predictability

Authors:
Héctor Fernando Gómez-García;José L. Marroquín;Johan Van Horebeek
Affiliations:
Center for Research in Mathematics (CIMAT), Department of Computer Science, Apartado Postal 402, C.P. 36000 Guanajuato, Gto, Mexico and Department of Basic Sciences and Engineering, Universidad de ...;Center for Research in Mathematics (CIMAT), Department of Computer Science, Apartado Postal 402, C.P. 36000 Guanajuato, Gto, Mexico;Center for Research in Mathematics (CIMAT), Department of Computer Science, Apartado Postal 402, C.P. 36000 Guanajuato, Gto, Mexico
Venue:
Computer Vision and Image Understanding
Year:
2008

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Abstract

In this work, a new similarity measure between images is presented, which is based on the concept of predictability of random variables evaluated through kernel functions. Image registration is achieved maximizing this measure, analogously to registration methods based on entropy, like mutual information and normalized mutual information. Compared experimentally with these methods in different problems, our proposal exhibits a more robust performance specially for problems involving large transformations and in cases where the registration is done using a small number of samples, such as in nonparametric registration.