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Efficient object recognition method for adjacent circular-shape objects

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dc.contributor.authorEun, S.-J.-
dc.contributor.authorWhangbo, T.-K.-
dc.date.available2020-02-29T00:47:29Z-
dc.date.created2020-02-12-
dc.date.issued2013-12-
dc.identifier.issn1876-1100-
dc.identifier.urihttps://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/14898-
dc.description.abstractThe general object recognition method is based on the various area segmentation algorithms. However, there might be difficulties with segmenting the adjacent objects when their boundaries are not clear. In order to solve this problem, we propose an efficient method of dividing adjacent circular-shape objects into single object through three steps: detection of the region of interest (ROI), determination of the candidate segmentation points, and creation of a segmentation boundary. The simulation shows robust results of 6.5 % average difference ratio compared to the existing methods, even when SNR was severe. © 2013 Springer Science+Business Media.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.relation.isPartOfLecture Notes in Electrical Engineering-
dc.titleEfficient object recognition method for adjacent circular-shape objects-
dc.typeArticle-
dc.type.rimsART-
dc.description.journalClass1-
dc.identifier.doi10.1007/978-94-007-5860-5_110-
dc.identifier.bibliographicCitationLecture Notes in Electrical Engineering, v.215 LNEE, pp.911 - 917-
dc.description.isOpenAccessN-
dc.identifier.scopusid2-s2.0-84874147908-
dc.citation.endPage917-
dc.citation.startPage911-
dc.citation.titleLecture Notes in Electrical Engineering-
dc.citation.volume215 LNEE-
dc.contributor.affiliatedAuthorEun, S.-J.-
dc.contributor.affiliatedAuthorWhangbo, T.-K.-
dc.type.docTypeConference Paper-
dc.subject.keywordAuthorAdjacent circular-shape objects-
dc.subject.keywordAuthorCurve fitting-
dc.subject.keywordAuthorLocal feature-
dc.subject.keywordAuthorObject recognition-
dc.subject.keywordPlusAdjacent circular-shape objects-
dc.subject.keywordPlusAverage difference-
dc.subject.keywordPlusLocal feature-
dc.subject.keywordPlusSegmentation algorithms-
dc.subject.keywordPlusSegmentation boundaries-
dc.subject.keywordPlusSingle object-
dc.subject.keywordPlusThe region of interest (ROI)-
dc.subject.keywordPlusCurve fitting-
dc.subject.keywordPlusImage segmentation-
dc.subject.keywordPlusObject recognition-
dc.description.journalRegisteredClassscopus-
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Whangbo, Taeg Keun
College of IT Convergence (컴퓨터공학부(컴퓨터공학전공))
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