DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ntalianis, Klimis | - |
dc.contributor.author | Doulamis, Nikolaos | - |
dc.contributor.author | Doulamis, Anastasios | - |
dc.contributor.author | Kollias, Stefanos | - |
dc.date.accessioned | 2024-07-19T07:44:53Z | - |
dc.date.available | 2024-07-19T07:44:53Z | - |
dc.date.issued | 2001-08 | - |
dc.identifier | google_scholar-vd7COBsAAAAJ:yFnVuubrUp4C | - |
dc.identifier.isbn | 3-540-42486-5 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.other | vd7COBsAAAAJ:yFnVuubrUp4C | - |
dc.identifier.uri | https://uniwacris.uniwa.gr/handle/3000/2748 | - |
dc.description.abstract | In this paper, an adaptive neural network architecture is proposed for efficient video object segmentation and tracking of stereoscopic video sequences. Object extraction is a very important issue, addressed by the emerging multimedia applications, since it provides a meaningful description of the visual content. The scheme includes:(A) A retraining algorithm that optimally adapts the network weights to the current conditions and simultaneously minimally degrades the previous knowledge.(B) A semantically meaningful object extraction module for constructing the retraining set of the current conditions and (C) a decision mechanism, which detects the time instances when network retraining is required. The algorithm results in the minimization of a convex function subject to linear constraints. Furthermore description of the current conditions is achieved by appropriate combination of color and depth information. Experimental results on real life video sequences indicate the promising performance of the proposed adaptive neural network-based scheme. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | Artificial Neural Networks - ICANN 2001 | en_US |
dc.relation.ispartofseries | Lecture Notes in Computer Science | en_US |
dc.source | Proc. of International Conference on Artificial Neural Networks, 0 | - |
dc.title | Adaptable neural networks for unsupervised video object segmentation of stereoscopic sequences | en_US |
dc.type | Conference Paper | en_US |
dc.relation.conference | International Conference on Artificial Neural Networks (ICANN 2001), 21-25 August 2001, Vienna, Austria | en_US |
dc.identifier.doi | 10.1007/3-540-44668-0_147 | en_US |
dc.relation.dept | Department of Business Administration | en_US |
dc.relation.faculty | School of Administrative, Economics and Social Sciences | en_US |
dc.identifier.spage | 1060 | en_US |
dc.identifier.epage | 1066 | en_US |
dc.collaboration | University of West Attica (UNIWA) | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.journals | Open Access | en_US |
dc.publication | Peer Reviewed | en_US |
dc.country | Greece | en_US |
local.metadatastatus | not verified | en_US |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.openairetype | Conference Paper | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | Department of Business Administration | - |
crisitem.author.faculty | School of Administrative, Economics and Social Sciences | - |
crisitem.author.parentorg | School of Administrative, Economics and Social Sciences | - |
Appears in Collections: | Book Chapter / Κεφάλαιο Βιβλίου |
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