Evaluation of Robust Covariance Estimation for Object Detection

dc.citation.epage20spa
dc.citation.issue01spa
dc.citation.journalTitleCuadernos de Ingeniería Matemáticaspa
dc.citation.spage1spa
dc.citation.volume01spa
dc.contributor.affiliationUniversidad EAFIT, School of Sciences, Department of Mathematical Sciencesspa
dc.contributor.authorTamayo-Arango, Andres Felipe
dc.contributor.authorPlazas Escudero, David
dc.contributor.authorVidal-Correa, Juan Pablo
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degrees
dc.date.accessioned2021-06-10T20:29:41Z
dc.date.available2021-06-10T20:29:41Z
dc.date.issued2021-04-07
dc.description.abstractThis work presents an initial approach to the evaluation of robust covariance estimation for object detection (localization) using the “region covariance” technique from the literature. The covariance estimation is performed using the Comedian, Kendall, Spearman and Ledoit and Wolf robust approaches for covariance, and the procedure was also compared using two different matrix norms for estimating dissimilarity. The performance was measured quantitatively using linear regression and Pareto boundaries, yielding the Ledoit and Wolf estimation with best overall performance in object detection in normal and noisy images.spa
dc.formatapplication/pdfeng
dc.identifier.urihttp://hdl.handle.net/10784/29845
dc.language.isoengspa
dc.publisherUniversidad EAFITspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.localAcceso abiertospa
dc.subject.keywordRegion covariancespa
dc.subject.keywordRobust estimationen
dc.subject.keywordObject detectionen
dc.subject.keywordPareto boundariesen
dc.subject.keywordImage featuresen
dc.titleEvaluation of Robust Covariance Estimation for Object Detectionspa
dc.typeinfo:eu-repo/semantics/publishedVersionspa
dc.type.localArtículospa

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