Open Access System for Information Sharing

Login Library

 

Article
Cited 32 time in webofscience Cited 43 time in scopus
Metadata Downloads
Full metadata record
Files in This Item:
There are no files associated with this item.
DC FieldValueLanguage
dc.contributor.authorOh, G-
dc.contributor.authorLee, S-
dc.contributor.authorShin, SY-
dc.date.accessioned2016-03-31T13:44:30Z-
dc.date.available2016-03-31T13:44:30Z-
dc.date.created2009-03-19-
dc.date.issued1999-02-
dc.identifier.issn0167-8655-
dc.identifier.other1999-OAK-0000000611-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/20500-
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.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherELSEVIER SCIENCE BV-
dc.relation.isPartOfPATTERN RECOGNITION LETTERS-
dc.subjecttexture-
dc.subjectpatterns-
dc.subjectperiodicity-
dc.subjectinertia-
dc.subjectco-occurrence matrix-
dc.subjectdistance matching function-
dc.subjectCOOCCURRENCE MATRIX-
dc.titleFast determination of textural periodicity using distance matching function-
dc.typeArticle-
dc.contributor.college컴퓨터공학과-
dc.identifier.doi10.1016/S0167-8655(98)00140-8-
dc.author.googleOh, G-
dc.author.googleLee, S-
dc.author.googleShin, SY-
dc.relation.volume20-
dc.relation.issue2-
dc.relation.startpage191-
dc.relation.lastpage197-
dc.contributor.id10057010-
dc.relation.journalPATTERN RECOGNITION LETTERS-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCIE-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationPATTERN RECOGNITION LETTERS, v.20, no.2, pp.191 - 197-
dc.identifier.wosid000078622100008-
dc.date.tcdate2019-01-01-
dc.citation.endPage197-
dc.citation.number2-
dc.citation.startPage191-
dc.citation.titlePATTERN RECOGNITION LETTERS-
dc.citation.volume20-
dc.contributor.affiliatedAuthorLee, S-
dc.identifier.scopusid2-s2.0-0033076119-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc21-
dc.type.docTypeArticle-
dc.subject.keywordAuthortexture-
dc.subject.keywordAuthorpatterns-
dc.subject.keywordAuthorperiodicity-
dc.subject.keywordAuthorinertia-
dc.subject.keywordAuthorco-occurrence matrix-
dc.subject.keywordAuthordistance matching function-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher

이승용LEE, SEUNGYONG
Dept of Computer Science & Enginrg
Read more

Views & Downloads

Browse