On a parameter estimation method for Gibbs-Markov random fields

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dc.citation.epage430en_US
dc.citation.issueNumber4en_US
dc.citation.spage424en_US
dc.citation.volumeNumber16en_US
dc.contributor.authorGürelli, M. I.en_US
dc.contributor.authorOnural, L.en_US
dc.date.accessioned2016-02-08T10:53:44Z
dc.date.available2016-02-08T10:53:44Z
dc.date.issued1994en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThis correspondence is about a Gibbs-Markov random field (GMRF) parameter estimation technique proposed by Derin and Elliott. We will refer to this technique as the histogramming (H) method. First, the relation of the H method to the (conditional) maximum likelihood (ML) method is considered. Second, a bias-reduction based modification of the H method is proposed to improve its performance, especially in the case of small amounts of image data.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T10:53:44Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 1994en
dc.identifier.doi10.1109/34.277597en_US
dc.identifier.issn0162-8828
dc.identifier.urihttp://hdl.handle.net/11693/26011
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/34.277597en_US
dc.source.titleIEEE Transactions on Pattern Analysis and Machine Intelligenceen_US
dc.subjectGibbs-Markov random fieldsen_US
dc.subjectImage modelingen_US
dc.subjectparameter estimationen_US
dc.subjectpattern recognitionen_US
dc.subjectTextureen_US
dc.subjectImage analysisen_US
dc.subjectImage processingen_US
dc.subjectParameter estimationen_US
dc.subjectStatistical methodsen_US
dc.subjectBias reduction based modificationen_US
dc.subjectGibbs Markov random fieldsen_US
dc.subjectHistogrammingen_US
dc.subjectImage modellingen_US
dc.subjectMaximum likelihood methoden_US
dc.subjectPattern recognitionen_US
dc.titleOn a parameter estimation method for Gibbs-Markov random fieldsen_US
dc.typeArticleen_US

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