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dc.contributor.authorMarana, Aparecido Nilceu-
dc.contributor.authorda, L.-
dc.contributor.authorVelastin, S. A.-
dc.contributor.authorLotufo, R. A.-
dc.identifier.citationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, v. 4, p. 2773-2775.-
dc.description.abstractThis paper presents a technique for oriented texture classification which is based on the Hough transform and Kohonen's neural network model. In this technique, oriented texture features are extracted from the Hough space by means of two distinct strategies. While the first operates on a non-uniformly sampled Hough space, the second concentrates on the peaks produced in the Hough space. The described technique gives good results for the classification of oriented textures, a common phenomenon in nature underlying an important class of images. Experimental results are presented to demonstrate the performance of the new technique in comparison, with an implemented technique based on Gabor filters.en
dc.subjectMathematical transformations-
dc.subjectNeural networks-
dc.subjectHough transform-
dc.subjectKohonen's self organizing map-
dc.subjectOriented texture classification-
dc.subjectFeature extraction-
dc.titleOriented texture classification based on self-organizing neural network and Hough transformen
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUnesp, Sao Paulo-
dc.description.affiliationUnespUnesp, Sao Paulo-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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