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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15908
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dc.contributor.authorMontoya Zegarra, Javier-
dc.contributor.authorPaulo Papa, Joao-
dc.contributor.authorLeite, Neucimar-
dc.contributor.authorda Silva Torres, Ricardo-
dc.contributor.authorFalcao, Alexandre-
dc.date.accessioned2019-01-29T22:19:56Z-
dc.date.available2019-01-29T22:19:56Z-
dc.date.issued2008-
dc.identifier.issn16876172es_PE
dc.identifier.urihttp://repositorio.ucsp.edu.pe/handle/UCSP/15908-
dc.description.abstractLearning 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.es_PE
dc.description.uriTrabajo de investigaciónes_PE
dc.language.isoenges_PE
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-45749084524&doi=10.1155%2f2008%2f691924&partnerID=40&md5=94ff5c1565eccb6cc9b0d2400d9cfaa2es_PE
dc.rightsinfo:eu-repo/semantics/restrictedAccesses_PE
dc.sourceRepositorio Institucional - UCSPes_PE
dc.sourceUniversidad Católica San Pabloes_PE
dc.sourceScopuses_PE
dc.subjectClassification (of information)es_PE
dc.subjectComputer networkses_PE
dc.subjectImage enhancementes_PE
dc.subjectRotationes_PE
dc.subjectTextureses_PE
dc.subjectBrodatzes_PE
dc.subjectClassification rateses_PE
dc.subjectData setses_PE
dc.subjectDiscriminating poweres_PE
dc.subjectDistorted imageses_PE
dc.subjectFeature vector (FV)es_PE
dc.subjectimage descriptores_PE
dc.subjectInvariant featureses_PE
dc.subjectmulticlass recognitiones_PE
dc.subjectrotation invariantes_PE
dc.subjectSteerable pyramid (SP)es_PE
dc.subjectTexture featureses_PE
dc.subjecttexture imageses_PE
dc.subjectTexture recognitiones_PE
dc.subjectFeature extractiones_PE
dc.titleLearning how to extract rotation-invariant and scale-invariant features from texture imageses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.identifier.doi10.1155/2008/691924es_PE
Appears in Collections:Artículos de investigación

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