dc.creatorMontoya-Zegarra, JA
dc.creatorPapa, JP
dc.creatorLeite, NJ
dc.creatorTorres, RDS
dc.creatorFalcao, AX
dc.date2008
dc.date2014-11-18T03:39:57Z
dc.date2015-11-26T17:45:57Z
dc.date2014-11-18T03:39:57Z
dc.date2015-11-26T17:45:57Z
dc.date.accessioned2018-03-29T00:28:24Z
dc.date.available2018-03-29T00:28:24Z
dc.identifierEurasip Journal On Advances In Signal Processing. Springer, 2008.
dc.identifier1687-6172
dc.identifierWOS:000256728300001
dc.identifier10.1155/2008/691924
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/80822
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/80822
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/80822
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1288344
dc.descriptionLearning how to extract texture features from noncontrolled environments characterized by distorted images is a still-open task. By using a new rotation-invariant and scale-invariant image descriptor based on steerable pyramid decomposition, and a novel multiclass recognition method based on optimum-path forest, a new texture recognition system is proposed. By combining the discriminating power of our image descriptor and classifier, our system uses small-size feature vectors to characterize texture images without compromising overall classification rates. State-of-the-art recognition results are further presented on the Brodatz data set. High classification rates demonstrate the superiority of the proposed system. Copyright (c) 2008 Javier A. Montoya-Zegarra et al.
dc.languageen
dc.publisherSpringer
dc.publisherNew York
dc.publisherEUA
dc.relationEurasip Journal On Advances In Signal Processing
dc.relationEURASIP J. Adv. Signal Process.
dc.rightsaberto
dc.rightshttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dc.sourceWeb of Science
dc.subjectFuzzy Connectedness
dc.subjectClassification
dc.subjectSegmentation
dc.subjectRetrieval
dc.subjectFilters
dc.subjectModels
dc.subjectAlgorithms
dc.titleLearning how to extract rotation-invariant and scale-invariant features from texture images
dc.typeArtículos de revistas


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