Navigation of quadruped multirobots by gesture recognition using restricted boltzmann machines
This 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 spec...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | Panamá |
| Institución: | Universidad Tecnológica de Panamá |
| Repositorio: | Repositorio Institucional de documento digitales de acceso abierto de la UTP |
| Idioma: | español |
| OAI Identifier: | oai:ridda2.utp.ac.pa:123456789/5787 |
| Acceso en línea: | http://revistas.utp.ac.pa/index.php/memoutp/article/view/2013 http://ridda2.utp.ac.pa/handle/123456789/5787 |
| Access Level: | acceso abierto |
| Palabra clave: | Manipulates; multirobots; restricted Boltzmann Machines |
| Sumario: | This 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%. |
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