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Image registration based on kernel-predictability
JOSE LUIS MARROQUIN ZALETA
Acceso Abierto
Atribución-NoComercial-CompartirIgual
Kernel - Lenguaje de Programación
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.
2008
Artículo
Inglés
Investigadores
LENGUAJES ALGORÍTMICOS
Versión publicada
publishedVersion - Versión publicada
Aparece en las colecciones: Ciencias de la Computación

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