Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/1947
DC FieldValueLanguage
dc.contributor.authorRapantzikos, Konstantinos-
dc.contributor.authorTsapatsoulis, Nicolas-
dc.contributor.otherΤσαπατσούλης, Νικόλας-
dc.date.accessioned2009-05-25T13:41:16Zen
dc.date.accessioned2013-05-16T13:11:05Z-
dc.date.accessioned2015-12-02T09:40:52Z-
dc.date.available2009-05-25T13:41:16Zen
dc.date.available2013-05-16T13:11:05Z-
dc.date.available2015-12-02T09:40:52Z-
dc.date.issued2006-06-01-
dc.identifier.citationInternational Journal of Intelligent Systems Technologies and Applications, 2006, vol. 1, no. 3-4, pp. 346-358en_US
dc.identifier.urihttps://hdl.handle.net/20.500.14279/1947-
dc.description.abstractFeature map fusion in Visual Attention (VA) models is by definition an uncertain procedure. One of the major impediments in extending the static VA architecture proposed by Itti et al. (2000) to account for motion or other information is the lack of justification on how to integrate the various channels. We propose an innovative committee machine scheme that allows for dynamically changing the committee members (maps) and weighting them according to the confidence level of their estimation. Through this machine we handle the extensions on Itti's model; we add a motion channel and a prior knowledge channel which accounts for the conscious search performed by humans when looking for faces in a scene. The experimental results, obtained when considering face detection, show that the map fusion, through the proposed committee machine, leads to significantly better statistical results when compared with the simple skin-based face detection method.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofInternational Journal of Intelligent Systems Technologies and Applicationsen_US
dc.rights© Inderscience Enterprisesen_US
dc.subjectCommittee machinesen_US
dc.subjectMap fusionen_US
dc.subjectVisual attentionen_US
dc.subjectFace detectionen_US
dc.subjectUncertaintyen_US
dc.subjectImage processingen_US
dc.titleA committee machine scheme for feature map fusion under uncertainty: the face detection caseen_US
dc.typeArticleen_US
dc.collaborationNational Technical University Of Athensen_US
dc.collaborationUniversity of Cyprusen_US
dc.journalsSubscriptionen_US
dc.countryGreeceen_US
dc.countryCyprusen_US
dc.subject.fieldSocial Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1504/IJISTA.2006.009912en_US
dc.dept.handle123456789/54en
dc.relation.issue3-4en_US
dc.relation.volume1en_US
cut.common.academicyear2006-2007en_US
dc.identifier.spage346en_US
dc.identifier.epage358en_US
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.author.deptDepartment of Communication and Marketing-
crisitem.author.facultyFaculty of Communication and Media Studies-
crisitem.author.orcid0000-0002-6739-8602-
crisitem.author.parentorgFaculty of Communication and Media Studies-
Appears in Collections:Άρθρα/Articles
CORE Recommender
Show simple item record

SCOPUSTM   
Citations 50

2
checked on Mar 21, 2021

Page view(s) 50

548
Last Week
1
Last month
4
checked on Dec 23, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.