A method for mixed states texture segmentation with simultaneous parameter estimation
In this work a method for mixed-state model motion texture segmentation and parameter estimation is presented. We use the Expectation Maximization algorithm for mixture parameter estimation, introducing the Gibbs distribution for moving points, excluding zero discrete component associated with no mo...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2011 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/93032 |
| Acceso en línea: | http://hdl.handle.net/11336/93032 |
| Access Level: | acceso abierto |
| Palabra clave: | EXPECTATION MAXIMIZATION MARKOV RANDOM FIELDS MOTION TEXTURES PSEUDO-LIKELIHOOD SEGMENTATION https://purl.org/becyt/ford/2.2 https://purl.org/becyt/ford/2 |
| Sumario: | In this work a method for mixed-state model motion texture segmentation and parameter estimation is presented. We use the Expectation Maximization algorithm for mixture parameter estimation, introducing the Gibbs distribution for moving points, excluding zero discrete component associated with no motion regions. We use then the a posteriori probabilities to generate an alternative field to segment the textures according to its statistical parameters. |
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