Selecting salient objects in real scenes: An oscillatory correlation model

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dc.contributor.authorQuiles, Marcos G. [UNIFESP]
dc.contributor.authorWang, DeLiang
dc.contributor.authorZhao, Liang
dc.contributor.authorRomero, Roseli A. F.
dc.contributor.authorHuang, De-Shuang
dc.contributor.institutionOhio State Univ
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionChinese Acad Sci
dc.date.accessioned2016-01-24T14:05:59Z
dc.date.available2016-01-24T14:05:59Z
dc.date.issued2011-01-01
dc.description.abstractAttention is a critical mechanism for visual scene analysis. By means of attention, it is possible to break down the analysis of a complex scene to the analysis of its parts through a selection process. Empirical studies demonstrate that attentional selection is conducted on visual objects as a whole. We present a neurocomputational model of object-based selection in the framework of oscillatory correlation. By segmenting an input scene and integrating the segments with their conspicuity obtained from a saliency map, the model selects salient objects rather than salient locations. the proposed system is composed of three modules: a saliency map providing saliency values of image locations, image segmentation for breaking the input scene into a set of objects, and object selection which allows one of the objects of the scene to be selected at a time. This object selection system has been applied to real gray-level and color images and the simulation results show the effectiveness of the system. (C) 2010 Elsevier B.V. All rights reserved.en
dc.description.affiliationOhio State Univ, Dept Comp Sci & Engn, Columbus, OH 43210 USA
dc.description.affiliationOhio State Univ, Ctr Cognit Sci, Columbus, OH 43210 USA
dc.description.affiliationFed Univ São Paulo Unifesp, Dept Sci & Technol, Sao Jose Dos Campos, SP, Brazil
dc.description.affiliationUniv São Paulo, Inst Math & Comp Sci, Dept Comp Sci, Sao Carlos, SP, Brazil
dc.description.affiliationChinese Acad Sci, Hefei Inst Intelligent Machines, Intelligent Comp Lab, Hefei 230031, Anhui, Peoples R China
dc.description.affiliationUnifespFed Univ São Paulo Unifesp, Dept Sci & Technol, Sao Jose Dos Campos, SP, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipNGI University
dc.description.sponsorshipK.C. Wong Education Foundation (Hong Kong)
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.format.extent54-64
dc.identifierhttp://dx.doi.org/10.1016/j.neunet.2010.09.002
dc.identifier.citationNeural Networks. Oxford: Pergamon-Elsevier B.V., v. 24, n. 1, p. 54-64, 2011.
dc.identifier.doi10.1016/j.neunet.2010.09.002
dc.identifier.issn0893-6080
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/33284
dc.identifier.wosWOS:000289013500006
dc.language.isoeng
dc.publisherElsevier B.V.
dc.relation.ispartofNeural Networks
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.licensehttp://www.elsevier.com/about/open-access/open-access-policies/article-posting-policy
dc.subjectObject selectionen
dc.subjectLEGIONen
dc.subjectOscillatory correlationen
dc.subjectVisual attentionen
dc.titleSelecting salient objects in real scenes: An oscillatory correlation modelen
dc.typeinfo:eu-repo/semantics/article
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