An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences
Autor(a) principal: | |
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Data de Publicação: | 2006 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://repositorio-aberto.up.pt/handle/10216/297 |
Resumo: | In this paper we present a management model to deal with the problem of tracking missing features during long image sequences using Computational Vision. Some usual difficulties related with missing features are that they may be temporarily occluded or might even have disappeared definitively, and the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This approach may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step, and it can deal with the appearance, occlusion and disappearance of features especially useful for tracking in long image sequences. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented. |
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An Improved Management Model for Tracking Missing Features in Computer Vision Long Image SequencesEngenhariaEngineeringIn this paper we present a management model to deal with the problem of tracking missing features during long image sequences using Computational Vision. Some usual difficulties related with missing features are that they may be temporarily occluded or might even have disappeared definitively, and the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This approach may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step, and it can deal with the appearance, occlusion and disappearance of features especially useful for tracking in long image sequences. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented.20062006-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://repositorio-aberto.up.pt/handle/10216/297eng1790-0832Raquel R. PinhoJoão Manuel R. S. TavaresMiguel V. Correiainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-29T14:50:22Zoai:repositorio-aberto.up.pt:10216/297Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:09:44.061257Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences |
title |
An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences |
spellingShingle |
An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences Raquel R. Pinho Engenharia Engineering |
title_short |
An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences |
title_full |
An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences |
title_fullStr |
An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences |
title_full_unstemmed |
An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences |
title_sort |
An Improved Management Model for Tracking Missing Features in Computer Vision Long Image Sequences |
author |
Raquel R. Pinho |
author_facet |
Raquel R. Pinho João Manuel R. S. Tavares Miguel V. Correia |
author_role |
author |
author2 |
João Manuel R. S. Tavares Miguel V. Correia |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Raquel R. Pinho João Manuel R. S. Tavares Miguel V. Correia |
dc.subject.por.fl_str_mv |
Engenharia Engineering |
topic |
Engenharia Engineering |
description |
In this paper we present a management model to deal with the problem of tracking missing features during long image sequences using Computational Vision. Some usual difficulties related with missing features are that they may be temporarily occluded or might even have disappeared definitively, and the computational cost involved should always be reduced to the strictly necessary. The proposed Net Present Value (NPV) model, based on the economic Theory of Capital, considers the tracking of each missing feature as an investment. Thus, using the NPV criterion, with adequate receipt and outlay functions, each occluded feature may be kept on tracking or it may be excluded of the tracking process depending on its historical behavior. This approach may be applied to any tracking system as long as the tracking results may be evaluated in each temporal step, and it can deal with the appearance, occlusion and disappearance of features especially useful for tracking in long image sequences. Experimental results, both on synthetic and real image sequences, which validate our model, will be also presented. |
publishDate |
2006 |
dc.date.none.fl_str_mv |
2006 2006-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio-aberto.up.pt/handle/10216/297 |
url |
https://repositorio-aberto.up.pt/handle/10216/297 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1790-0832 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799136021806317568 |