DC Field | Value | Language |
---|---|---|
dc.contributor.author | Oh, G | - |
dc.contributor.author | Lee, S | - |
dc.contributor.author | Shin, SY | - |
dc.date.accessioned | 2016-03-31T13:44:30Z | - |
dc.date.available | 2016-03-31T13:44:30Z | - |
dc.date.created | 2009-03-19 | - |
dc.date.issued | 1999-02 | - |
dc.identifier.issn | 0167-8655 | - |
dc.identifier.other | 1999-OAK-0000000611 | - |
dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/20500 | - |
dc.description.abstract | The 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.statementofresponsibility | X | - |
dc.language | English | - |
dc.publisher | ELSEVIER SCIENCE BV | - |
dc.relation.isPartOf | PATTERN RECOGNITION LETTERS | - |
dc.subject | texture | - |
dc.subject | patterns | - |
dc.subject | periodicity | - |
dc.subject | inertia | - |
dc.subject | co-occurrence matrix | - |
dc.subject | distance matching function | - |
dc.subject | COOCCURRENCE MATRIX | - |
dc.title | Fast determination of textural periodicity using distance matching function | - |
dc.type | Article | - |
dc.contributor.college | 컴퓨터공학과 | - |
dc.identifier.doi | 10.1016/S0167-8655(98)00140-8 | - |
dc.author.google | Oh, G | - |
dc.author.google | Lee, S | - |
dc.author.google | Shin, SY | - |
dc.relation.volume | 20 | - |
dc.relation.issue | 2 | - |
dc.relation.startpage | 191 | - |
dc.relation.lastpage | 197 | - |
dc.contributor.id | 10057010 | - |
dc.relation.journal | PATTERN RECOGNITION LETTERS | - |
dc.relation.index | SCI급, SCOPUS 등재논문 | - |
dc.relation.sci | SCIE | - |
dc.collections.name | Journal Papers | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | PATTERN RECOGNITION LETTERS, v.20, no.2, pp.191 - 197 | - |
dc.identifier.wosid | 000078622100008 | - |
dc.date.tcdate | 2019-01-01 | - |
dc.citation.endPage | 197 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 191 | - |
dc.citation.title | PATTERN RECOGNITION LETTERS | - |
dc.citation.volume | 20 | - |
dc.contributor.affiliatedAuthor | Lee, S | - |
dc.identifier.scopusid | 2-s2.0-0033076119 | - |
dc.description.journalClass | 1 | - |
dc.description.journalClass | 1 | - |
dc.description.wostc | 21 | - |
dc.type.docType | Article | - |
dc.subject.keywordAuthor | texture | - |
dc.subject.keywordAuthor | patterns | - |
dc.subject.keywordAuthor | periodicity | - |
dc.subject.keywordAuthor | inertia | - |
dc.subject.keywordAuthor | co-occurrence matrix | - |
dc.subject.keywordAuthor | distance matching function | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
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