Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24479
DC FieldValueLanguage
dc.contributorDepartment of Computing-
dc.creatorLee, RST-
dc.creatorLiu, JNK-
dc.date.accessioned2015-06-23T09:12:16Z-
dc.date.available2015-06-23T09:12:16Z-
dc.identifier.issn0218-0014-
dc.identifier.urihttp://hdl.handle.net/10397/24479-
dc.language.isoenen_US
dc.publisherWorld Scientificen_US
dc.subjectComposite neural oscillatory modelen_US
dc.subjectElastic graph dynamic link modelen_US
dc.subjectGabor filtersen_US
dc.subjectObject recognitionen_US
dc.subjectScene analysisen_US
dc.titleSCENOGRAM - Scene analysis using composite neural oscillatory-based elastic graph modelen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage215-
dc.identifier.epage237-
dc.identifier.volume16-
dc.identifier.issue2-
dc.identifier.doi10.1142/S0218001402001587-
dcterms.abstractScene analysis is so far one of the most important topics in machine vision. In this paper, we present an integrated scene analysis model, namely SCENOGRAM (Scene analysis using CompositE Neural Oscillatory-based elastic GRAph Model). Basically the proposed scene analyzer is based on the integration of the composite neural oscillatory model with our elastic graph dynamic link model. The system involves: (1) multifrequency bands feature extraction scheme using Gabor filters, (2) automatic figure-ground object segmentation using a composite neural oscillatory model, and (3) object matching using an elastic graph dynamic link model. From the implementation point of view, we introduce an intelligent agent based scene analysis and object identification solution using the SCENOGRAM technology. From the experimental point of view, a scene gallery of over 6000 color scene images is used for automatic scene segmentation testing and object identification test. An overall correct invariant facial recognition rate of over 87% is attained. It is anticipated that the implementation of the SCENOGRAM can provide an invariant and higher-order intelligent object (pattern) encoding, searching and identification solution for future intelligent e-Business.-
dcterms.bibliographicCitationInternational journal of pattern recognition and artificial intelligence, 2002, v. 16, no. 2, p. 215-237-
dcterms.isPartOfInternational journal of pattern recognition and artificial intelligence-
dcterms.issued2002-
dc.identifier.isiWOS:000175082000005-
dc.identifier.scopus2-s2.0-0036506370-
dc.identifier.eissn1793-6381-
dc.identifier.rosgroupidr05653-
dc.description.ros2001-2002 > Academic research: refereed > Publication in refereed journal-
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