Image registration based on kernel-predictability
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 inform...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2008 |
| País: | México |
| Institución: | Centro de Investigación en Matemáticas |
| Repositorio: | Repositorio Institucional CIMAT |
| Idioma: | inglés |
| OAI Identifier: | oai:cimat.repositorioinstitucional.mx:1008/946 |
| Acceso en línea: | http://cimat.repositorioinstitucional.mx/jspui/handle/1008/946 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/MSC/Kernel - Lenguaje de Programación info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120302 |
| Sumario: | 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. |
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