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A combined probabilistic framework for learning gestures and actions

  • Francisco Escolano
  • Miguel Cazorla
  • Domingo Gallardo
  • Faraón Llorens
  • Rosana Satorre
  • Ramón Rizo
4 Applied Artificial Intelligence and Knowledge-Based Systems in Specific Domains Human-Computer Interaction
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1416)

Abstract

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.

Keywords

Visual Inspection Gesture Recognition Learning Probabilistic Constraints Eigenmethods 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Francisco Escolano
    • 1
  • Miguel Cazorla
    • 1
  • Domingo Gallardo
    • 1
  • Faraón Llorens
    • 1
  • Rosana Satorre
    • 1
  • Ramón Rizo
    • 1
  1. 1.Grupo i3a: Informática Industrial e Inteligencia Artificial Departamento de Ciencia de la Computación e Inteligencia ArtificialUniversidad de AlicanteSan VicenteSpain

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