On the Influence of Markovian Models for Contextual-Based Optimum-Path Forest Classification

dc.contributorUniversidade Federal de São Carlos (UFSCar)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2019-10-04T12:29:42Z
dc.date.accessioned2022-12-19T17:58:41Z
dc.date.available2019-10-04T12:29:42Z
dc.date.available2022-12-19T17:58:41Z
dc.date.created2019-10-04T12:29:42Z
dc.date.issued2014-01-01
dc.identifierProgress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014. Berlin: Springer-verlag Berlin, v. 8827, p. 462-469, 2014.
dc.identifier0302-9743
dc.identifierhttp://hdl.handle.net/11449/184745
dc.identifierWOS:000346407400057
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5365798
dc.description.abstractContextual classification considers the information about a sample's neighborhood to improve standard pixel-based classification approaches. In this work, we evaluated four different Markovian models for Optimum-Path Forest contextual classification considering land use recognition in remote sensing data. Some insights about the situations in which each of them should be applied are stated, as well as the idea behind them is explained.
dc.languageeng
dc.publisherSpringer
dc.relationProgress In Pattern Recognition Image Analysis, Computer Vision, And Applications, Ciarp 2014
dc.rightsAcesso aberto
dc.sourceWeb of Science
dc.subjectOptimum-Path Forest
dc.subjectContextual Classification
dc.subjectMarkov Random Fields
dc.titleOn the Influence of Markovian Models for Contextual-Based Optimum-Path Forest Classification
dc.typeActas de congresos


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