Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/17652
Título: Hand pose detection for vision-based gesture interfaces
Autores/as: Antón Canalís, Luis
Sánchez Nielsen, Elena
Castrillón-Santana, M. 
Clasificación UNESCO: 120304 Inteligencia artificial
Fecha de publicación: 2005
Publicación seriada: Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005
Resumen: Vision-based applications designed for humanmachine interaction require fast and accurate hand detection. However, previous works on this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects to locate. This paper presents an approach which changes the detection target without limiting the number of detected gestures. Using a cascade classifier we detect hands based on their wrists. With this approach, we introduce two main contributions: (1) a reliable segmentation, independently of the gesture being made and (2) a training phase faster than previous cascade classifier based methods. The paper includes experimental evaluations with different video streams that illustrate the efficiency and suitability for perceptual interfaces.
URI: http://hdl.handle.net/10553/17652
Fuente: Proceedings of the 9th IAPR Conference on Machine Vision Applications, MVA 2005, p. 506-509
Colección:Actas de congresos
miniatura
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