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Image segmentation by graph partitioning

dc.contributor.authorTorres, Ana Sofia
dc.contributor.authorMonteiro, Fernando C.
dc.date.accessioned2014-09-24T10:24:24Z
dc.date.available2014-09-24T10:24:24Z
dc.date.issued2012
dc.description.abstractIn this paper we propose an hybrid method for the image segmentation which combines the edge-based, region-based and the morphological techniques in conjunction through the spectral based clustering approach. An initial partitioning of the image into atomic regions is set by applying a watershed method to the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process which produces the final segmentation. We have applied our approach on several images of the Berkeley Segmentation Dataset. The results reveal the accuracy of the propose method.por
dc.identifier.citationTorres, Ana Sofia; Monteiro, Fernando C. (2012). Image segmentation by graph partitioning. In International Conference of Numerical Analysis and Applied Mathematics, ICNAAM 2012. Kos - Greece. 1479, p.802-805. ISBN 978-073541091-6por
dc.identifier.doi10.1063/1.4756259
dc.identifier.isbn978-073541091-6
dc.identifier.urihttp://hdl.handle.net/10198/10559
dc.language.isoengpor
dc.peerreviewedyespor
dc.subjectGraph partitioningpor
dc.subjectImage segmentationpor
dc.subjectNormalized cutpor
dc.subjectWatershed transformpor
dc.titleImage segmentation by graph partitioningpor
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferencePlaceKos - Greecepor
oaire.citation.endPage805por
oaire.citation.startPage802por
oaire.citation.titleInternational Conference of Numerical Analysis and Applied Mathematics, ICNAAM 2012por
oaire.citation.volume1479por
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.rightsopenAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication363b6c37-282c-4cd6-bb54-3c97cc700d78
relation.isAuthorOfPublication.latestForDiscovery363b6c37-282c-4cd6-bb54-3c97cc700d78

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