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dc.contributor.authorVillamizar Vergel, Michael Alejandro
dc.contributor.authorGarrell Zulueta, Anais
dc.contributor.authorSanfeliu Cortés, Alberto
dc.contributor.authorMoreno-Noguer, Francesc
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.accessioned2017-03-27T09:48:52Z
dc.date.available2018-08-01T00:30:12Z
dc.date.issued2016-08
dc.identifier.citationVillamizar, M.A., Garrell, A., Sanfeliu, A., Moreno-Noguer, F. Interactive multiple object learning with scanty human supervision. "Computer vision and image understanding", Agost 2016, vol. 149, p. 51-64.
dc.identifier.issn1077-3142
dc.identifier.urihttp://hdl.handle.net/2117/102900
dc.description© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.abstractWe present a fast and online human-robot interaction approach that progressively learns multiple object classifiers using scanty human supervision. Given an input video stream recorded during the human robot interaction, the user just needs to annotate a small fraction of frames to compute object specific classifiers based on random ferns which share the same features. The resulting methodology is fast (in a few seconds, complex object appearances can be learned), versatile (it can be applied to unconstrained scenarios), scalable (real experiments show we can model up to 30 different object classes), and minimizes the amount of human intervention by leveraging the uncertainty measures associated to each classifier.; We thoroughly validate the approach on synthetic data and on real sequences acquired with a mobile platform in indoor and outdoor scenarios containing a multitude of different objects. We show that with little human assistance, we are able to build object classifiers robust to viewpoint changes, partial occlusions, varying lighting and cluttered backgrounds. (C) 2016 Elsevier Inc. All rights reserved.
dc.format.extent14 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::Informàtica::Robòtica
dc.subject.otherObject recognition
dc.subject.otherInteractive learning
dc.subject.otherOnline classifier
dc.subject.otherHuman-robot interaction
dc.subject.otherhuman-robot interaction
dc.subject.otherrecognition
dc.titleInteractive multiple object learning with scanty human supervision
dc.typeArticle
dc.contributor.groupUniversitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel.ligents
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.identifier.doi10.1016/j.cviu.2016.03.010
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Pattern recognition
dc.subject.inspecClassificació INSPEC::Automation::Robots
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S1077314216300042
dc.rights.accessOpen Access
drac.iddocument18770964
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/MINECO/1PE/DPI2013-42458-P
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/644271/EU/AErial RObotic system integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance/AEROARMS
upcommons.citation.authorVillamizar, M.A., Garrell, A., Sanfeliu, A., Moreno-Noguer, F.
upcommons.citation.publishedtrue
upcommons.citation.publicationNameComputer vision and image understanding
upcommons.citation.volume149
upcommons.citation.startingPage51
upcommons.citation.endingPage64


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Except where otherwise noted, content on this work is licensed under a Creative Commons license: Attribution-NonCommercial-NoDerivs 3.0 Spain