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dc.contributor.author
Gimenez Romero, Javier Alejandro
dc.contributor.author
Frery, Alejandro César
dc.contributor.author
Flesia, Ana Georgina
dc.date.available
2018-03-08T19:38:00Z
dc.date.issued
2015-01
dc.identifier.citation
Gimenez Romero, Javier Alejandro; Frery, Alejandro César; Flesia, Ana Georgina; When data do not bring information: A case study in markov random fields estimation; Institute of Electrical and Electronics Engineers; Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing; 8; 1; 1-2015; 195-203
dc.identifier.issn
1939-1404
dc.identifier.uri
http://hdl.handle.net/11336/38298
dc.description.abstract
The Potts model is frequently used to describe the behavior of image classes, since it allows to incorporate contextual information linking neighboring pixels in a simple way. Its isotropic version has only one real parameter β, known as smoothness parameter or inverse temperature, which regulates the classes map homogeneity. The classes are unavailable and estimating them is central in important image processing procedures as, for instance, image classification. Methods for estimating the classes which stem from a Bayesian approach under the Potts model require to adequately specify a value for β. The estimation of such parameter can be efficiently made solving the pseudo maximum-likelihood (PML) equations in two different schemes, using the prior or the posterior model. Having only radiometric data available, the first scheme needs the computation of an initial segmentation, whereas the second uses both the segmentation and the radiometric data to make the estimation. In this paper, we compare these two PML estimators by computing the mean-square error (MSE), bias, and sensitivity to deviations from the hypothesis of the model. We conclude that the use of extra data does not improve the accuracy of the PML; moreover, under gross deviations from the model, this extra information introduces unpredictable distortions and bias.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Potts Model
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Pseudo-Likelihood
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Segmentation
dc.subject.classification
Ciencias de la Computación
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Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
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Ingeniería de Sistemas y Comunicaciones
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Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
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INGENIERÍAS Y TECNOLOGÍAS
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Matemática Pura
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Matemáticas
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CIENCIAS NATURALES Y EXACTAS
dc.title
When data do not bring information: A case study in markov random fields estimation
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-03-02T14:19:33Z
dc.journal.volume
8
dc.journal.number
1
dc.journal.pagination
195-203
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Gimenez Romero, Javier Alejandro. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Frery, Alejandro César. Universidade Federal de Alagoas; Brasil
dc.description.fil
Fil: Flesia, Ana Georgina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomia y Física. Sección Matemática. Grupo de Probabilidad y Estadística; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Ieee Journal Of Selected Topics In Applied Earth Observations And Remote Sensing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/JSTARS.2014.2323713
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://ieeexplore.ieee.org/document/6869017/
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