Fast determination of textural periodicity using distance matching function

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dc.contributor.authorOh, Gko
dc.contributor.authorLee, Sko
dc.contributor.authorShin, Sung-Yongko
dc.date.accessioned2013-03-03T03:16:56Z-
dc.date.available2013-03-03T03:16:56Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued1999-02-
dc.identifier.citationPATTERN RECOGNITION LETTERS, v.20, no.2, pp.191 - 197-
dc.identifier.issn0167-8655-
dc.identifier.urihttp://hdl.handle.net/10203/76949-
dc.description.abstractThe periodicity of a texture is one of its important visual characteristics. The inertias of co-occurrence matrices of the texture have been often used to detect the visual periodicity. However, it is time-consuming to explicitly construct these matrices. In this paper, we propose the distance matching function to avoid constructing the matrices due to our new interpretation of an inertia. For a texture of size m x n, the inertias of all co-occurrence matrices can be obtained in O(mn log mn) time by simultaneously evaluating the function at all displacement vectors. This is a significant improvement over the previous method using the co-occurrence matrices, that requires O(m(2)n(2)) time. (C) 1999 Elsevier Science B.V. All rights reserved.-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.subjectCOOCCURRENCE MATRIX-
dc.titleFast determination of textural periodicity using distance matching function-
dc.typeArticle-
dc.identifier.wosid000078622100008-
dc.identifier.scopusid2-s2.0-0033076119-
dc.type.rimsART-
dc.citation.volume20-
dc.citation.issue2-
dc.citation.beginningpage191-
dc.citation.endingpage197-
dc.citation.publicationnamePATTERN RECOGNITION LETTERS-
dc.contributor.localauthorShin, Sung-Yong-
dc.contributor.nonIdAuthorOh, G-
dc.contributor.nonIdAuthorLee, S-
dc.type.journalArticleArticle-
dc.subject.keywordAuthortexture-
dc.subject.keywordAuthorpatterns-
dc.subject.keywordAuthorperiodicity-
dc.subject.keywordAuthorinertia-
dc.subject.keywordAuthorco-occurrence matrix-
dc.subject.keywordAuthordistance matching function-
dc.subject.keywordPlusCOOCCURRENCE MATRIX-
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