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.accessioned2023-08-07T20:09:05Z
dc.date.available2023-08-07T20:09:05Z
dc.identifierhttps://revistas.utp.ac.pa/index.php/memoutp/article/view/2013
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/7940558
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.relationhttps://revistas.utp.ac.pa/index.php/memoutp/article/view/2013/2955
dc.rightsDerechos de autor 2018 Memorias de Congresos UTPes-ES
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0es-ES
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.titleNavigation of quadruped multirobots by gesture recognition using restricted boltzmann machineses-ES
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeArtículo revisado por pareses-ES


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