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Automatic Binary Data Classification Using a Modified Allen-Cahn Equation

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dc.contributor.authorKim, S.-
dc.contributor.authorKim, J.-
dc.date.accessioned2021-12-03T05:21:14Z-
dc.date.available2021-12-03T05:21:14Z-
dc.date.created2021-08-31-
dc.date.issued2021-03-30-
dc.identifier.issn0218-0014-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/129040-
dc.description.abstractIn this paper, we propose an automatic binary data classification method using a modified Allen-Cahn (AC) equation. The modified AC equation was originally developed for image segmentation. The equation consists of the AC equation with a fidelity term which enforces the solution to be the given data. In the proposed method, we start from a coarse grid and refine the grid until the accuracy of the data classification reaches a given tolerance. Therefore, we can avoid a laborious trial and error procedure. For a numerical method for the modified AC equation, we use a recently developed explicit hybrid scheme. We perform several 2D and 3D computational tests to demonstrate the performance of the proposed method. The computational results confirm that the proposed algorithm is automatic. © 2021 World Scientific Publishing Company.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherWorld Scientific-
dc.subjectImage segmentation-
dc.subjectPositive ions-
dc.subjectAllen-Cahn-
dc.subjectAllen-Cahn equation-
dc.subjectBinary data-
dc.subjectCoarse grid-
dc.subjectComputational results-
dc.subjectComputational tests-
dc.subjectHybrid scheme-
dc.subjectTrial-and-error procedures-
dc.subjectNumerical methods-
dc.titleAutomatic Binary Data Classification Using a Modified Allen-Cahn Equation-
dc.typeArticle-
dc.contributor.affiliatedAuthorKim, J.-
dc.identifier.doi10.1142/S0218001421500130-
dc.identifier.scopusid2-s2.0-85094636900-
dc.identifier.wosid000639929900001-
dc.identifier.bibliographicCitationInternational Journal of Pattern Recognition and Artificial Intelligence, v.35, no.4-
dc.relation.isPartOfInternational Journal of Pattern Recognition and Artificial Intelligence-
dc.citation.titleInternational Journal of Pattern Recognition and Artificial Intelligence-
dc.citation.volume35-
dc.citation.number4-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalWebOfScienceCategoryComputer Science, Artificial Intelligence-
dc.subject.keywordPlusImage segmentation-
dc.subject.keywordPlusPositive ions-
dc.subject.keywordPlusAllen-Cahn-
dc.subject.keywordPlusAllen-Cahn equation-
dc.subject.keywordPlusBinary data-
dc.subject.keywordPlusCoarse grid-
dc.subject.keywordPlusComputational results-
dc.subject.keywordPlusComputational tests-
dc.subject.keywordPlusHybrid scheme-
dc.subject.keywordPlusTrial-and-error procedures-
dc.subject.keywordPlusNumerical methods-
dc.subject.keywordAuthorBinary data classification-
dc.subject.keywordAuthormodified Allen-Cahn equation-
dc.subject.keywordAuthoroperator splitting method-
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