Synergistic arc-weight estimation for interactive image segmentation using graphs

dc.creatorde Miranda, PAV
dc.creatorFalcao, AX
dc.creatorUdupa, JK
dc.date2010
dc.dateJAN
dc.date2014-11-19T05:07:16Z
dc.date2015-11-26T17:57:41Z
dc.date2014-11-19T05:07:16Z
dc.date2015-11-26T17:57:41Z
dc.date.accessioned2018-03-29T00:41:15Z
dc.date.available2018-03-29T00:41:15Z
dc.identifierComputer Vision And Image Understanding. Academic Press Inc Elsevier Science, v. 114, n. 1, n. 85, n. 99, 2010.
dc.identifier1077-3142
dc.identifierWOS:000272651600008
dc.identifier10.1016/j.cviu.2009.08.001
dc.identifierhttp://www.repositorio.unicamp.br/jspui/handle/REPOSIP/74003
dc.identifierhttp://www.repositorio.unicamp.br/handle/REPOSIP/74003
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/74003
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1291551
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionWe introduce a framework for synergistic arc-weight estimation, where the user draws markers inside each object (including background), arc weights are estimated from image attributes and object information (pixels under the markers), and a visual feedback guides the user's next action. We demonstrate the method in several graph-based segmentation approaches as a basic step (which should be followed by some proper approach-specific adaptive procedure) and show its advantage over methods that do not exploit object information and over methods that recompute weights during delineation, which make the user to lose control over the segmentation process. We also validate the method using medical data from two imaging modalities (CT and MRI-T1). (C) 2009 Elsevier Inc. All rights reserved.
dc.description114
dc.description1
dc.description85
dc.description99
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.languageen
dc.publisherAcademic Press Inc Elsevier Science
dc.publisherSan Diego
dc.publisherEUA
dc.relationComputer Vision And Image Understanding
dc.relationComput. Vis. Image Underst.
dc.rightsfechado
dc.rightshttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.sourceWeb of Science
dc.subjectImage foresting transform
dc.subjectGraph-cut segmentation
dc.subjectRelative-fuzzy connectedness
dc.subjectWatershed transform
dc.subjectLive-wire segmentation
dc.subjectContour tracking
dc.subjectInteractive segmentation
dc.subjectGraph-search algorithms
dc.subjectkappa-Connected segmentation
dc.subjectRelative Fuzzy Connectedness
dc.subjectLive-wire
dc.subjectEnergy Minimization
dc.subjectForesting Transform
dc.subjectTheoretic Approach
dc.subjectMultiple Objects
dc.subjectAlgorithms
dc.subjectCuts
dc.subjectReconstruction
dc.subjectRetrieval
dc.titleSynergistic arc-weight estimation for interactive image segmentation using graphs
dc.typeArtículos de revistas


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