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dc.contributores-ES
dc.creatorSaraiva, Arata A.
dc.creatorSANTOS, D. B. S
dc.creatorMARQUES JUNIOR, F.C.F
dc.creatorSOUSA, JOSE VIGNO M.
dc.creatorFONSECA FERREIRA, N. M.
dc.creatorValente, Antonio
dc.date2018-09-30
dc.date.accessioned2018-12-04T14:32:13Z
dc.date.available2018-12-04T14:32:13Z
dc.identifierhttp://revistas.utp.ac.pa/index.php/memoutp/article/view/2013
dc.identifier.urihttp://ridda2.utp.ac.pa/handle/123456789/5787
dc.descriptionThis article discusses a method that performs gesture recognition, with the objective of extracting characteristics of the segmented hand, from dynamic images captured from a webcam and identifying signal patterns. With this method it is possible to manipulate simulated multirobots that perform specific movements. The method consists of the Continuously Adaptive Mean-SHIFT algorithm, followed by the Threshold segmentation algorithm and Deep Learning through Boltzmann restricted machines. As a result, an accuracy of 82.2%.es-ES
dc.formatapplication/pdf
dc.languagespa
dc.publisherUniversidad Tecnológica de Panamáes-ES
dc.relationhttp://revistas.utp.ac.pa/index.php/memoutp/article/view/2013/2955
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceMemorias de Congresos UTP; 2018: The 21st International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines - CLAWAR 2018; 431-438es-ES
dc.subjectManipulates; multirobots; restricted Boltzmann Machineses-ES
dc.titleNavigation of quadruped multirobots by gesture recognition using restricted boltzmann machineses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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