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DC poleHodnotaJazyk
dc.contributor.authorFeitosa, Raul Queiroz
dc.contributor.authorMota, Guilherme
dc.contributor.authorPaciornik, Sidnei
dc.contributor.editorSkala, Václav
dc.date.accessioned2015-09-21T08:35:11Z
dc.date.available2015-09-21T08:35:11Z
dc.date.issued2001
dc.identifier.citationJournal of WSCG. 2001, vol. 9, no. 1-3.en
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.issn1213-6964 (online)
dc.identifier.urihttp://hdl.handle.net/11025/15787
dc.identifier.urihttp://wscg.zcu.cz/wscg2001/WSCG2001_Program.htm
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectrozpoznávání vzorůcs
dc.subjectsnížení rozměrůcs
dc.subjectcharakterizace materiálůcs
dc.titleAn alternative approach for pattern detection applied to materials characterizationen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe problem of detecting specific patterns in images of materials obtained through High Resolution Transmission Electron Microscopy is addressed. A supervised classification method is proposed using an extension of Principal Component Analysis and a new a procedure for building the training set. Experiments on two different types of images indicate that the proposed method is superior to the conventional cross-correlation approach. Moreover, using the same number of components, the new dimensionality reduction approach shows a better performance than the standard PCA method.en
dc.subject.translatedpattern recognitionen
dc.subject.translateddimensionality reductionen
dc.subject.translatedmaterials characterizationen
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 9, number 1-3 (2001)

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