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dc.contributor.authorMolina-Cabello, Miguel A.
dc.contributor.authorElizondo, David A.
dc.contributor.authorLuque Baena, Rafael Marcos
dc.contributor.authorLópez-Rubio, Ezequiel 
dc.date.accessioned2019-09-13T11:54:33Z
dc.date.available2019-09-13T11:54:33Z
dc.date.created2019
dc.date.issued2019-09-13
dc.identifier.urihttps://hdl.handle.net/10630/18345
dc.description.abstractMost classic approaches to homography estimation are based on the filtering of outliers by means of the RANSAC method. New proposals include deep convolutional neural networks. Here a new method for homography estimation is presented, which supplies a deep neural homography estimator with color perturbated versions of the original image pair. The obtained outputs are combined in order to obtain a more robust estimation of the underlying homography. Experimental results are shown, which demonstrate the adequate performance of our approach, both in quantitative and qualitative terms.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectRedes neuronalesen_US
dc.subject.otherconvolutional neural networksen_US
dc.subject.othercomputer visionen_US
dc.subject.othercolor transformationsen_US
dc.titleHomography estimation with deep convolutional neural networks by random color transformationsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitle30th British Machine Vision Conferenceen_US
dc.relation.eventplaceCardiff, Gales, Reino Unidoen_US
dc.relation.eventdateSeptiembre de 2019en_US


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