Bayesian Segmentation of Range Images of Polyhedra Objects Using Entropy Controlled Quadratic Markov Measure Field Models
In this paper, a method based on Bayesian estimation with prior MRF models for segmentation of range images of polyhedral objects is presented. This method includes new ways to determine the confidence associated with the information given for every pixel in the image as well an improved method for...
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| Tipo de recurso: | informe técnico |
| Estado: | Versión publicada |
| Fecha de publicación: | 2007 |
| País: | México |
| Institución: | Centro de Investigación en Matemáticas |
| Repositorio: | Repositorio Institucional CIMAT |
| Idioma: | inglés |
| OAI Identifier: | oai:cimat.repositorioinstitucional.mx:1008/638 |
| Acceso en línea: | http://cimat.repositorioinstitucional.mx/jspui/handle/1008/638 |
| Access Level: | acceso abierto |
| Palabra clave: | info:eu-repo/classification/MSC/Procesamiento de Imágenes info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 info:eu-repo/classification/cti/120308 |
| Sumario: | In this paper, a method based on Bayesian estimation with prior MRF models for segmentation of range images of polyhedral objects is presented. This method includes new ways to determine the confidence associated with the information given for every pixel in the image as well an improved method for the localization of the boundaries between regions. The performance of the method compares favorably with other state of the art procedures when evaluated using standard benchmark. |
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