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Elastic-band transform for visualization and detection

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dc.contributor.authorChoi, Guebin-
dc.contributor.authorOh, Hee-Seok-
dc.date.accessioned2023-04-17T09:20:32Z-
dc.date.available2023-04-17T09:20:32Z-
dc.date.created2023-03-21-
dc.date.created2023-03-21-
dc.date.created2023-03-21-
dc.date.created2023-03-21-
dc.date.created2023-03-21-
dc.date.issued2023-02-
dc.identifier.citationPattern Recognition Letters, Vol.166, pp.119-125-
dc.identifier.issn0167-8655-
dc.identifier.urihttps://hdl.handle.net/10371/189987-
dc.description.abstractThis paper presents a new multiscale transformation for statistical analysis of one-dimensional data such as time series under the concept of the scale-space approach. The proposed method uses regular obser-vations (eye scanning) with a range of different intervals. The new approach, termed 'elastic-band trans -form,' can be considered as a collection of observations over various intervals (length of elastic-band) of viewing. It is motivated by how people look at an object, such as a sequence of data repeatedly to overview a global structure of the object and find some specific features of it. Some measures based on the transformed elastic-bands are discussed for describing characteristics of data, and multiscale visual-izations induced by the measures are developed for a better understanding of data. Several numerical experiments are performed to demonstrate the usefulness of the proposed transform for visualization and detection.(c) 2023 Elsevier B.V. All rights reserved.-
dc.language영어-
dc.publisherElsevier BV-
dc.titleElastic-band transform for visualization and detection-
dc.typeArticle-
dc.identifier.doi10.1016/j.patrec.2023.01.010-
dc.citation.journaltitlePattern Recognition Letters-
dc.identifier.wosid000923849100001-
dc.identifier.scopusid2-s2.0-85149744330-
dc.citation.endpage125-
dc.citation.startpage119-
dc.citation.volume166-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorOh, Hee-Seok-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordAuthorDetection-
dc.subject.keywordAuthorMultiscale method-
dc.subject.keywordAuthorTransformation-
dc.subject.keywordAuthorVisualization-
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