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dc.contributor.authorVillamizar Vergel, Michael Alejandro
dc.contributor.authorMoreno-Noguer, Francesc
dc.contributor.authorAndrade-Cetto, Juan
dc.contributor.authorSanfeliu Cortés, Alberto
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2010-11-23T18:56:24Z
dc.date.available2010-11-23T18:56:24Z
dc.date.created2010
dc.date.issued2010
dc.identifier.citationVillamizar, M.A. [et al.]. Efficient rotation invariant object detection using boosted random Ferns. A: IEEE Conference on Computer Vision and Pattern Recognition. "IEEE Conference on Computer Vision and Pattern Recognition(2010)". San Francisco: 2010, p. 1038-1045.
dc.identifier.urihttp://hdl.handle.net/2117/10385
dc.description.abstractWe present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orientations. In contrast to other works that address this problem using multiple classifiers, each one specialized for a specific orientation, we propose a simple two-step approach with an estimation stage and a classification stage. The estimator yields an initial set of potential object poses that are then validated by the classifier. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients (HOGs), which we compute during a preprocessing step. Both the use of supervised learning and working on the gradient space makes our approach robust while being efficient at run-time. We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations, and with challenging conditions such as cluttered backgrounds, changing illumination conditions and partial occlusions.
dc.format.extent8 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
dc.subject.lcshImage processing
dc.subject.otherimage classification image recognition pattern recognition PARAULES AUTOR: object recognition
dc.subject.otherboosting
dc.titleEfficient rotation invariant object detection using boosted random Ferns
dc.typeConference report
dc.subject.lemacImatges -- Processament
dc.contributor.groupUniversitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel·ligents
dc.identifier.doi10.1109/CVPR.2010.5540104
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Pattern recognition::Image recognition::Pose estimation
dc.relation.publisherversionhttp://dx.doi.org/10.1109/CVPR.2010.5540104
dc.rights.accessOpen Access
local.identifier.drac4128300
dc.description.versionPostprint (published version)
local.citation.authorVillamizar, M.A.; Moreno, F.; Andrade-Cetto, J.; Sanfeliu, A.
local.citation.contributorIEEE Conference on Computer Vision and Pattern Recognition
local.citation.pubplaceSan Francisco
local.citation.publicationNameIEEE Conference on Computer Vision and Pattern Recognition(2010)
local.citation.startingPage1038
local.citation.endingPage1045


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