Image-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes

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dc.contributor.authorKim, Seminko
dc.contributor.authorLee, Seung Hoko
dc.contributor.authorRo, Yong-Manko
dc.date.accessioned2016-04-20T06:20:24Z-
dc.date.available2016-04-20T06:20:24Z-
dc.date.created2015-11-02-
dc.date.created2015-11-02-
dc.date.issued2015-10-
dc.identifier.citationJOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v.32, pp.217 - 223-
dc.identifier.issn1047-3203-
dc.identifier.urihttp://hdl.handle.net/10203/205268-
dc.description.abstractMost features of image-based coin recognition have been based on histogram information to achieve rotation-invariant property. However, discrimination of the features based on histogram information can be reduced by ignoring local spatial structure. In this paper, we propose a novel feature of image-based coin recognition that exploits a spatial structure. In order to consider the structure of a coin, rotation-and-flipping-robust region binary patterns (RFR) is adopted. The proposed method computes gradient magnitudes in a coin image, and extracts RFR using local difference magnitude transform to increase the accuracy of coin recognition. Comparative experiments with a number of state-of-the-art methods have been performed on the MUSCLE CIS-Benchmark Preview data set. The experimental results showed that the proposed method outperformed the state of the art methods in terms of recognition accuracy, smaller feature dimension, and shorter feature extraction time.-
dc.languageEnglish-
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCE-
dc.subjectMULTIRESOLUTION GRAY-SCALE-
dc.subjectTEXTURE CLASSIFICATION-
dc.titleImage-based coin recognition using rotation-invariant region binary patterns based on gradient magnitudes-
dc.typeArticle-
dc.identifier.wosid000362388300018-
dc.identifier.scopusid2-s2.0-84940975572-
dc.type.rimsART-
dc.citation.volume32-
dc.citation.beginningpage217-
dc.citation.endingpage223-
dc.citation.publicationnameJOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION-
dc.identifier.doi10.1016/j.jvcir.2015.08.011-
dc.contributor.localauthorRo, Yong-Man-
dc.contributor.nonIdAuthorKim, Semin-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorRegion binary pattern-
dc.subject.keywordAuthorRotation-invariant-
dc.subject.keywordAuthorImage-based coin recognition-
dc.subject.keywordAuthorCoin recognition-
dc.subject.keywordAuthorLocal binary pattern-
dc.subject.keywordAuthorFeature extraction-
dc.subject.keywordAuthorMUSCLE CIS-
dc.subject.keywordAuthorFeature matching-
dc.subject.keywordAuthorCoin classification-
dc.subject.keywordPlusMULTIRESOLUTION GRAY-SCALE-
dc.subject.keywordPlusTEXTURE CLASSIFICATION-
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