English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/97514
Share/Impact:
Statistics
logo share SHARE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

DC FieldValueLanguage
dc.contributor.authorVillamizar, Michael-
dc.contributor.authorGrabner, Helmut-
dc.contributor.authorMoreno-Noguer, Francesc-
dc.contributor.authorAndrade-Cetto, Juan-
dc.contributor.authorGool, Luc van-
dc.contributor.authorSanfeliu, Alberto-
dc.date.accessioned2014-06-02T08:38:27Z-
dc.date.available2014-06-02T08:38:27Z-
dc.date.issued2011-
dc.identifierdoi: 10.5244/C.25.20-
dc.identifier.citationProceedings of the British Machine Vision Conference: 20.1-20.10 (2011)-
dc.identifier.isbn1-901725-43-X-
dc.identifier.urihttp://hdl.handle.net/10261/97514-
dc.descriptionPresentado al 22nd BMVC celebrado en University of Dundee (Escocia) en septiembre de 2011.-
dc.description.abstractWe propose an efficient method for object localization and 3D pose estimation. A two-step approach is used. In the first step, a pose estimator is evaluated in the input images in order to estimate potential object locations and poses. These candidates are then validated, in the second step, by the corresponding pose-specific classifier. The result is a detection approach that avoids the inherent and expensive cost of testing the complete set of specific classifiers over the entire image. A further speedup is achieved by feature sharing. Features are computed only once and are then used for evaluating the pose estimator and all specific classifiers. The proposed method has been validated on two public datasets for the problem of detecting of cars under several views. The results show that the proposed approach yields high detection rates while keeping efficiency.-
dc.description.sponsorshipThis work was supported by the Spanish Ministry of Science and Innovation under Projects RobTaskCoop (DPI2010-17112), PAU (DPI2008-06022), and MIPRCV (Consolider - Ingenio 2010 CSD2007-00018), and the EU CEEDS Project FP7-ICT-2009-5-95682.-
dc.publisherBritish Machine Vision Association-
dc.relationinfo:eu-repo/grantAgreement/EC/FP7/258749-
dc.relation.isversionofPublisher's version-
dc.rightsopenAccess-
dc.titleEfficient 3D object detection using multiple pose-specific classifiers-
dc.typecomunicación de congreso-
dc.identifier.doi10.5244/C.25.20-
dc.relation.publisherversionhttp://dx.doi.org/10.5244/C.25.20-
dc.date.updated2014-06-02T08:38:27Z-
dc.description.versionPeer Reviewed-
dc.language.rfc3066eng-
Appears in Collections:(IRII) Libros y partes de libros
Files in This Item:
File Description SizeFormat 
Efficient 3D Object.pdf2,26 MBUnknownView/Open
Show simple item record
 

Related articles:


WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.