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Spectral methods in image segmentation: a combined approach

dc.contributor.authorMonteiro, Fernando C.
dc.contributor.authorCampilho, Aurélio
dc.date.accessioned2010-02-10T17:33:51Z
dc.date.available2010-02-10T17:33:51Z
dc.date.issued2005
dc.descriptionIndexado ISIpt
dc.description.abstractGrouping and segmentation of images remains a challenging problem in computer vision. Recently, a number of authors have demonstrated a good performance on this task using spectral methods that are based on the eigensolution of a similarity matrix. In this paper, we implement a variation of the existing methods that combines aspects from several of the best-known eigenvector segmentation algorithms to produce a discrete optimal solution of the relaxed continuous eigensolution.pt
dc.identifier.citationMonteiro, Fernando C.; Campilho, Aurélio (2005). Spectral methods in image segmentation: a combined approach. In 2nd Iberian Conference on Pattern Recognition and Image Analysis. Estoril, Portugal. p.191-198. ISBN 978-3-540-26154-4pt
dc.identifier.isbn978-3-540-26154-4
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/10198/1836
dc.language.isoengpt
dc.peerreviewedyes
dc.publisherSpringerpt
dc.relation.ispartofseriesLecture Notes in Computer Science;3523
dc.subjectImage segmentationpt
dc.subjectSpectral methodspt
dc.subjectNormalized cutspt
dc.titleSpectral methods in image segmentation: a combined approachpt
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceEstoril, Portugalpt
oaire.citation.endPage198pt
oaire.citation.startPage191pt
oaire.citation.title2nd Iberian Conference on Pattern Recognition and Image Analysispt
person.familyNameMonteiro
person.givenNameFernando C.
person.identifier.ciencia-id2019-BDBF-10E2
person.identifier.orcid0000-0002-1421-8006
person.identifier.ridH-9213-2016
person.identifier.scopus-author-id8986162600
rcaap.rightsopenAccesspt
rcaap.typeconferenceObjectpt
relation.isAuthorOfPublication363b6c37-282c-4cd6-bb54-3c97cc700d78
relation.isAuthorOfPublication.latestForDiscovery363b6c37-282c-4cd6-bb54-3c97cc700d78

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