A combined probabilistic framework for learning gestures and actions
Contribuinte(s) |
Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial Robótica y Visión Tridimensional (RoViT) Laboratorio de Investigación en Visión Móvil (MVRLab) Informática Industrial e Inteligencia Artificial |
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Data(s) |
13/07/2012
13/07/2012
1998
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Resumo |
In this paper we introduce a probabilistic approach to support visual supervision and gesture recognition. Task knowledge is both of geometric and visual nature and it is encoded in parametric eigenspaces. Learning processes for compute modal subspaces (eigenspaces) are the core of tracking and recognition of gestures and tasks. We describe the overall architecture of the system and detail learning processes and gesture design. Finally we show experimental results of tracking and recognition in block-world like assembling tasks and in general human gestures. |
Identificador |
ESCOLANO, Francisco, et al. "A combined probabilistic framework for learning gestures and actions". En: Tasks and Methods in Applied Artificial Intelligence : 11th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems IEA-98-AIE Benicàssim, Castellón, Spain, June 1–4, 1998 Proceedings, Volume II / Angel Pasqual del Pobil, José Mira, Moonis Ali (Eds.). Berlin : Springer, 1998. (Lecture Notes in Computer Science; 1416). ISBN 3-540-64574-8, pp. 658-667 3-540-64574-8 0302-9743 (Print) 1611-3349 (Online) http://hdl.handle.net/10045/23397 10.1007/3-540-64574-8_452 |
Idioma(s) |
eng |
Publicador |
Springer Berlin / Heidelberg |
Relação |
http://dx.doi.org/10.1007/3-540-64574-8_452 |
Direitos |
The original publication is available at www.springerlink.com info:eu-repo/semantics/restrictedAccess |
Palavras-Chave | #Visual inspection #Gesture recognition #Learning #Probabilistic constraints #Eigenmethods #Ciencia de la Computación e Inteligencia Artificial |
Tipo |
info:eu-repo/semantics/conferenceObject |