DC FieldValueLanguage
dc.contributor.authorNtalianis, Klimis-
dc.contributor.authorDoulamis, Anastasios-
dc.contributor.authorKollias, Stefanos-
dc.contributor.authorDoulamis, Nikolaos-
dc.date.accessioned2024-10-31T14:54:43Z-
dc.date.available2024-10-31T14:54:43Z-
dc.date.issued2002-01-01-
dc.identifierscopus-70450198494-
dc.identifier.isbn978-3-540-46084-8-
dc.identifier.isbn978-3-540-44074-1-
dc.identifier.issn1611-3349-
dc.identifier.other70450198494-
dc.identifier.urihttps://uniwacris.uniwa.gr/handle/3000/2853-
dc.description.abstractIn this paper efficient performance generalization of neural network classifiers is accomplished, for unsupervised video object segmentation in videoconference/videophone sequences. Each time conditions change, a retraining phase is activated and the neural network classifier is adapted to the new environment. During retraining both the former and current knowledge are utilized so that good network generalization is achieved. The retraining algorithm results in the minimization of a convex function subject to linear constraints, leading to very fast network weight adaptation. Current knowledge is unsupervisedly extracted using a face-body detector, based on Gaussian p.d.f models. A binary template matching technique is also incorporated, which imposes shape constraints to candidate face regions. Finally the retrained network performs video object segmentation to the new environment. Several experiments on real sequences indicate the promising performance of the proposed adaptive neural network as efficient video object segmentation tool.en_US
dc.language.isoenen_US
dc.relation.ispartofArtificial Neural Networks - ICANN 2002en_US
dc.relation.ispartofseriesLecture Notes in Computer Scienceen_US
dc.titleNeural networks retraining for unsupervised video object segmentation of videoconference sequencesen_US
dc.typeConference Paperen_US
dc.relation.conferenceInternational Conference on Artificial Neural Networks (ICANN 2002), 28-30 August 2002, Madrid, Spainen_US
dc.identifier.doi10.1007/3-540-46084-5_212en_US
dc.identifier.scopus2-s2.0-70450198494-
dcterms.accessRights0en_US
dc.relation.deptDepartment of Business Administrationen_US
dc.relation.facultySchool of Administrative, Economics and Social Sciencesen_US
dc.identifier.spage1312en_US
dc.identifier.epage1318en_US
dc.collaborationUniversity of West Attica (UNIWA)en_US
dc.journalsOpen Accessen_US
dc.publicationPeer Revieweden_US
dc.countryGreeceen_US
local.metadatastatusnot verifieden_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairetypeConference Paper-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptDepartment of Business Administration-
crisitem.author.facultySchool of Administrative, Economics and Social Sciences-
crisitem.author.parentorgSchool of Administrative, Economics and Social Sciences-
Appears in Collections:Book Chapter / Κεφάλαιο Βιβλίου
CORE Recommender
Show simple item record

SCOPUSTM   
Citations

2
checked on Nov 16, 2024

Page view(s)

8
checked on Nov 21, 2024

Google ScholarTM

Check

Altmetric

Altmetric


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