Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/323
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dc.creatorpinho, rr
dc.creatortavares, jmrs
dc.creatorcorreia, mv
dc.date.accessioned2019-02-03T20:01:35Z-
dc.date.available2019-02-03T20:01:35Z-
dc.date.issued2007
dc.identifier.issn1790-0832
dc.identifier.othersigarra:54318
dc.identifier.urihttps://repositorio-aberto.up.pt/handle/10216/323-
dc.description.abstractIn 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.
dc.language.isoeng
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectEngenharia
dc.subjectEngineering
dc.titleAn improved management model for tracking missing features in computer vision long image sequences
dc.typeArtigo em Revista Científica Internacional
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.authenticusP-007-HY9
Appears in Collections:FEUP - Artigo em Revista Científica Internacional

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