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dc.contributorDelicado Useros, Pedro Francisco
dc.contributorMoisan, Lionel
dc.contributor.authorEspuny Pujol, Ferran
dc.date.accessioned2014-10-21T10:47:08Z
dc.date.available2015-06-12T08:07:50Z
dc.date.issued2014-06
dc.identifier.urihttp://hdl.handle.net/2099.1/23181
dc.descriptionLaboratoire MAP5 (Mathématiques appliquées Paris 5), CNRS UMR8145 Université Paris V - Paris Descartes
dc.description.abstractThe fundamental matrix is a two-view tensor playing a central role in Computer Vision geometry. We address its robust estimation given pairs of matched image features, affected by noise and outliers, which searches for a maximal subset of correct matches and the associated fundamental matrix. Overcoming the broadly used parametric RANSAC method, ORSA follows a probabilistic a contrario approach to look for the set of matches being least expected with respect to a uniform random distribution of image points. ORSA lacks performance when this assumption is clearly violated. We will propose an improvement of the ORSA method, based on its same a contrario framework and the use of a non-parametric estimate of the distribution of image features. The role and estimation of the fundamental matrix and the data SIFT matches will be carefully explained with examples. Our proposal performs significantly well for common scenarios of low inlier ratios and local feature concentrations.
dc.language.isoeng
dc.publisherUniversitat Politècnica de Catalunya
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa
dc.subject.lcshArtificial intelligence
dc.subject.otherSteriovision
dc.subject.otherFundamental matrix
dc.subject.otherRobust matching
dc.subject.otherSIFT
dc.subject.otherA contrario
dc.subject.otherStructure from motion
dc.subject.otherComputer vision
dc.titleImproving the A-Contrario computation of a fundamental matrix in computer vision
dc.typeMaster thesis
dc.subject.lemacIntel·ligència artificial
dc.subject.amsClassificació AMS::68 Computer science::68T Artificial intelligence
dc.identifier.slugFME-1055
dc.rights.accessOpen Access
dc.date.updated2014-07-09T06:38:39Z
dc.audience.educationlevelMàster
dc.audience.mediatorUniversitat Politècnica de Catalunya. Facultat de Matemàtiques i Estadística
dc.audience.degreeMÀSTER UNIVERSITARI EN ESTADÍSTICA I INVESTIGACIÓ OPERATIVA (Pla 2006)


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