dc.date.accessioned2019-01-29T22:19:56Z
dc.date.accessioned2023-05-30T23:27:54Z
dc.date.available2019-01-29T22:19:56Z
dc.date.available2023-05-30T23:27:54Z
dc.date.created2019-01-29T22:19:56Z
dc.date.issued2007
dc.identifierurn:isbn:9783540768555
dc.identifier3029743
dc.identifierhttp://repositorio.ucsp.edu.pe/handle/UCSP/15910
dc.identifierhttps://doi.org/10.1007/978-3-540-76856-2_19
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6477722
dc.description.abstractThis paper proposes a new texture classification system, which is distinguished by: (1) a new rotation-invariant image descriptor based on Steerable Pyramid Decomposition, and (2) by a novel multi-class recognition method based on Optimum Path Forest. 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 dataset. High classification rates demonstrate the superiority of the proposed method. © Springer-Verlag Berlin Heidelberg 2007.
dc.languageeng
dc.publisherScopus
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-38149038001&partnerID=40&md5=fba3a1481de3f71fda7b80604208c844
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceRepositorio Institucional - UCSP
dc.sourceUniversidad Católica San Pablo
dc.sourceScopus
dc.subjectClassification (of information)
dc.subjectData structures
dc.subjectImage analysis
dc.subjectImage descriptor
dc.subjectOptimum Path Forest
dc.subjectSteerable Pyramid Decomposition
dc.subjectTexture classification system
dc.subjectPattern recognition
dc.titleRotation-invariant texture recognition
dc.typeinfo:eu-repo/semantics/article


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