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dc.contributor.authorPinto, Tiago W.
dc.contributor.authorCarvalho, Marco A. G. de
dc.contributor.authorPedronette, Daniel C. G. [UNESP]
dc.contributor.authorMartins, Paulo S.
dc.identifier.citation2014 Ieee Southwest Symposium On Image Analysis And Interpretation (ssiai 2014). New York: Ieee, p. 153-156, 2014.
dc.description.abstractResearch on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.en
dc.relation.ispartof2014 Ieee Southwest Symposium On Image Analysis And Interpretation (ssiai 2014)
dc.sourceWeb of Science
dc.subjectImage segmentationen
dc.subjectWatershed transformen
dc.subjectGraph partitioningen
dc.subjectNormalized cuten
dc.subjectUnsupervised distance learningen
dc.titleImage segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cuten
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual de Campinas (UNICAMP)
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.description.affiliationSchool of Technology, UNICAMP, Limeira, São Paulo, Brazil.
dc.description.affiliationUnespUniversidade Estadual Paulista, Department of Statistics, Applied Mathematics and Computing, BR-13506900 São Paulo, Brazil
dc.rights.accessRightsAcesso aberto
unesp.campusUniversidade Estadual Paulista (UNESP), Instituto de Geociências e Ciências Exatas, Rio Claropt[3]
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