Tuning of Adaptive Weight Depth Map Generation Algorithms Exploratory Data Analysis and Design of Computer Experiments (DOCE)

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dc.citation.epage12spa
dc.citation.issue1spa
dc.citation.journalTitleJournal of Mathematical Imaging and Visioneng
dc.citation.journalTitleJournal of Mathematical Imaging and Visionspa
dc.citation.spage3spa
dc.citation.volume47spa
dc.contributor.authorAcosta, Diego
dc.contributor.authorCongote, John
dc.contributor.authorBarandiaran, Iñigo
dc.contributor.authorRuíz, Óscar
dc.contributor.authorHoyos, Alejandro
dc.contributor.authorGraña, Manuel
dc.contributor.departmentUniversidad EAFIT. Departamento de Ingeniería Mecánicaspa
dc.contributor.researchgroupLaboratorio CAD/CAM/CAEspa
dc.date.accessioned2016-11-18T22:11:15Z
dc.date.available2016-11-18T22:11:15Z
dc.date.issued2013-09
dc.description.abstractIn depth map generation algorithms, parameters settings to yield an accurate disparity map estimation are usually chosen empirically or based on un planned experiments -- Algorithms' performance is measured based on the distance of the algorithm results vs. the Ground Truth by Middlebury's standards -- This work shows a systematic statistical approach including exploratory data analyses on over 14000 images and designs of experiments using 31 depth maps to measure the relative inf uence of the parameters and to fine-tune them based on the number of bad pixels -- The implemented methodology improves the performance of adaptive weight based dense depth map algorithms -- As a result, the algorithm improves from 16.78% to 14.48% bad pixels using a classical exploratory data analysis of over 14000 existing images, while using designs of computer experiments with 31 runs yielded an even better performance by lowering bad pixels from 16.78% to 13%eng
dc.formatapplication/pdfeng
dc.identifier.doi10.1007/s10851-012-0366-7
dc.identifier.issn0924-9907
dc.identifier.urihttp://hdl.handle.net/10784/9678
dc.language.isoengeng
dc.publisherSpringer Verlagspa
dc.relation.ispartofJournal of Mathematical Imaging and Vision, Volume 47, Issue 1, pp 3-12spa
dc.relation.urihttp://link.springer.com/article/10.1007/s10851-012-0366-7
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.localAcceso abiertospa
dc.subject.keywordParameter estimationspa
dc.subject.keywordAlgorithmsspa
dc.subject.keywordImage processingspa
dc.subject.keywordExperimental designspa
dc.subject.keywordParameter estimationeng
dc.subject.keywordAlgorithmseng
dc.subject.keywordImage processingeng
dc.subject.keywordExperimental designeng
dc.subject.keywordMapas de profundidad.keywor
dc.subject.keywordVisión estéreo.keywor
dc.subject.lembESTIMACIÓN DE PARÁMETROSspa
dc.subject.lembALGORITMOSspa
dc.subject.lembPROCESAMIENTO DE IMÁGENESspa
dc.subject.lembDISEÑO EXPERIMENTALspa
dc.titleTuning of Adaptive Weight Depth Map Generation Algorithms Exploratory Data Analysis and Design of Computer Experiments (DOCE)eng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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