Please use this identifier to cite or link to this item:
http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1871
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor | 31249 | es_ES |
dc.contributor | 20608 | - |
dc.contributor.other | https://orcid.org/0000-0002-7337-8974 | - |
dc.coverage.spatial | Global | es_ES |
dc.creator | Gutiérrez, Osvaldo | - |
dc.creator | De la Rosa Vargas, José Ismael | - |
dc.creator | Villa Hernández, José de Jesús | - |
dc.creator | Escalante, Nivia | - |
dc.date.accessioned | 2020-05-05T18:46:12Z | - |
dc.date.available | 2020-05-05T18:46:12Z | - |
dc.date.issued | 2012-11 | - |
dc.identifier | info:eu-repo/semantics/publishedVersion | es_ES |
dc.identifier.isbn | 978-607-95476-6-0 | es_ES |
dc.identifier.uri | http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1871 | - |
dc.identifier.uri | https://doi.org/10.48779/q70a-xx81 | - |
dc.description.abstract | In this work we introduce a new approach for robust image segmentation. The idea is to combine two strategies within a Bayesian framework. The first one is to use a Márkov Random Field (MRF), which allows to introduce prior information with the purpose of preserve the edges in the image. The second strategy comes from the fact that the probability density function (pdf) of the likelihood function is non Gaussian or unknown, so it should be approximated by an estimated version, and for this, it is used the classical non-parametric or kernel density estimation. This two strategies together lead us to the definition of a new maximum a posteriori (MAP) estimator based on the minimization of the entropy of the estimated pdf of the likelihood function and the MRF at the same time, named MAP entropy estimator (MAPEE). Some experiments were made for different kind of images degraded with impulsive noise and the segmentation results are very satisfactory and promising. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | ROPEC | es_ES |
dc.publisher | IEEE | es_ES |
dc.relation.uri | generalPublic | es_ES |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 Estados Unidos de América | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.source | Proc. de la XIV Reunión de Otoño de Potencia, Electrónica y Computación, ROPEC 2012 INTERNACIONAL, Vol. 1, pp.387-392, Colima, Colima, Nov. 2012. | es_ES |
dc.subject.classification | INGENIERIA Y TECNOLOGIA [7] | es_ES |
dc.subject.other | Image segmentation | es_ES |
dc.subject.other | MRFs | es_ES |
dc.title | New approach of entropy estimation for robust image segmentation | es_ES |
dc.type | info:eu-repo/semantics/conferencePaper | es_ES |
Appears in Collections: | *Documentos Académicos*-- M. en Ciencias del Proc. de la Info. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
50_Gutierrez_DelaRosa_ROPEC 2012.pdf | Gutierrez_DelaRosa_ROPEC 2012 | 1,2 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License