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dc.creatorPeng, Honges
dc.creatorWang, Junes
dc.creatorPérez Jiménez, Mario de Jesúses
dc.date.accessioned2021-07-21T09:22:47Z
dc.date.available2021-07-21T09:22:47Z
dc.date.issued2015
dc.identifier.citationPeng, H., Wang, J. y Pérez Jiménez, M.d.J. (2015). Optimal multi-level thresholding with membrane computing. Digital Signal Processing, 37 (February 2015), 53-64.
dc.identifier.issn1051-2004es
dc.identifier.urihttps://hdl.handle.net/11441/116326
dc.description.abstractThe conventional methods are not effective and efficient for image multi-level thresholding due to time-consuming and expensive computation cost. The multi-level thresholding problem can be posed as anoptimization problem, optimizing some thresholding criterion. In this paper, membrane computing isintroduced to propose an efficient and robust multi-level thresholding method, where a cell-like P systemwith the nested structure of three layers is designed as its computing framework. Moreover, an improvedvelocity-position model is developed to evolve the objects in membranes based on the special membranestructure and communication mechanism of objects. Under the control of evolution-communicationmechanism of objects, the cell-like P system can efficiently exploit the best multi-level thresholds for animage. Simulation experiments on nine standard images compare the proposed multi-level thresholdingmethod with several state-of-the-art multi-level thresholding methods and demonstrate its superiority.es
dc.description.sponsorshipNational Natural Science Foundation of China No. 61170030es
dc.description.sponsorshipChunhui Project Foundation of the Education Department of China No. Z2012025es
dc.description.sponsorshipChunhui Project Foundation of the Education Department of China No. Z2012031es
dc.description.sponsorshipResearch Fund of Sichuan Key Technology Research and Development Program No. 2013GZX0155es
dc.description.sponsorshipOpen Research Funds of Key Laboratory of High Performance Scientific Computing No. SZJJ2012-002es
dc.formatapplication/pdfes
dc.format.extent12es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofDigital Signal Processing, 37 (February 2015), 53-64.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMembrane Computinges
dc.subjectCell-like P systemses
dc.subjectImage segmentationes
dc.subjectMulti-level thresholdinges
dc.subjectHistogrames
dc.titleOptimal multi-level thresholding with membrane computinges
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificiales
dc.relation.projectIDNo. 61170030es
dc.relation.projectIDNo. Z2012025es
dc.relation.projectIDNo. Z2012031es
dc.relation.projectIDNo. 2013GZX0155es
dc.relation.projectIDNo. SZJJ2012-002es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1051200414003157es
dc.identifier.doi10.1016/j.dsp.2014.10.006es
dc.contributor.groupUniversidad de Sevilla. TIC193: Computación Naturales
dc.journaltitleDigital Signal Processinges
dc.publication.volumen37es
dc.publication.issueFebruary 2015es
dc.publication.initialPage53es
dc.publication.endPage64es
dc.identifier.sisius20834913es
dc.contributor.funderNational Natural Science Foundation of Chinaes
dc.contributor.funderChunhui Project Foundation of the Education Department of Chinaes
dc.contributor.funderResearch Fund of Sichuan Key Technology Research and Development Programes
dc.contributor.funderOpen Research Funds of Key Laboratory of High Performance Scientific Computing, Chinaes

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional