ARTIFICIAL NEURAL NETWORKS BASED SYMBOLIC GESTURE INTERFACE

C. Iacopino, Anna Montesanto, Paola Baldassarri, A. F. Dragoni, P. Puliti

Abstract

The purpose of the developed system is the realization of a gesture recognizer, applied to a user interface. We tried to get fast and easy software for user, without leaving out reliability and using instruments available to common user: a PC and a webcam. The gesture detection is based on well-known artificial vision techniques, as the tracking algorithm by Lucas and Kanade. The paths, opportunely selected, are recognized by a double layered architecture of multilayer perceptrons. The realized system is efficiency and has a good robustness, paying attention to an adequate learning of gesture vocabulary both for the user and for system.

References

  1. B. Lucas, T. Kanade, 1981. An Iterative Image Registration Technique with an Application to Stereo Vision. Proc 7th Intl Joint Conf on Artificial Intelligence.
  2. C. Tomasi, e T. Kanade, 1991. Detection and Tracking of Point Feature. School Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA.
  3. D. Chai, 1999. Face Segmentation Using Skin-Color Map in Videophone Applications. IEEE Transactions on circuits and systems for video technology, 1999.
  4. J. Shi e C. Tomasi, 1994. Good Feature to Track. IEEE Conference on Computer Vision and Pattern Recognition, Seattle.
  5. J. Bouguet, 2000. Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the algorithm. Intel Corporation Microprocessor Research Labs.
  6. D.E. Rumelhart and A. Ortony, 1964. The Representation of Knowledge in Memory. In R.C. Anderson, R.J. Spiro, W.E. Montague (Eds.) Schooling and the acquisition of knowledge, Hillsdale, NJ: Erlbaum.
  7. S. Runeson, 1994. Perception of Biological Motion: the KSD-Principle and the Implications of a Distal Versus Proximal Approach. In G. Jansson, W. Epstein & S. S. Bergström (Eds.), Perceiving events and objects.
  8. F. Rosenblatt, 1962. Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanism. Spartan Books, Washington D.C.
  9. D. E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning Representations by Back-propagation of Errors”, Nature, Vol.323, pp.533-536, 1986.
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Paper Citation


in Harvard Style

Iacopino C., Montesanto A., Baldassarri P., F. Dragoni A. and Puliti P. (2008). ARTIFICIAL NEURAL NETWORKS BASED SYMBOLIC GESTURE INTERFACE . In Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008) ISBN 978-989-8111-60-9, pages 364-369. DOI: 10.5220/0001932803640369


in Bibtex Style

@conference{sigmap08,
author={C. Iacopino and Anna Montesanto and Paola Baldassarri and A. F. Dragoni and P. Puliti},
title={ARTIFICIAL NEURAL NETWORKS BASED SYMBOLIC GESTURE INTERFACE},
booktitle={Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)},
year={2008},
pages={364-369},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001932803640369},
isbn={978-989-8111-60-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Signal Processing and Multimedia Applications - Volume 1: SIGMAP, (ICETE 2008)
TI - ARTIFICIAL NEURAL NETWORKS BASED SYMBOLIC GESTURE INTERFACE
SN - 978-989-8111-60-9
AU - Iacopino C.
AU - Montesanto A.
AU - Baldassarri P.
AU - F. Dragoni A.
AU - Puliti P.
PY - 2008
SP - 364
EP - 369
DO - 10.5220/0001932803640369