A Proposal for a Homeostasis Based Adaptive Vision System

  • Javier Lorenzo-Navarro
  • Daniel Hernández
  • Cayetano Guerra
  • José Isern-González
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3522)

Abstract

In this work an approach to an adaptive vision system is presented. It is based on a homeostatic approach where the system state is represented as a set of artificial hormones which are affected by the environmental changes. To compensate these changes, the vision system is endowed with drives which are in charge of modifying the system parameters in order to keep the system performance as high as possible. To coordinate the drives in the system, a supervisor level based on fuzzy logic has been added. Experiments in both controlled and uncontrolled environments have been carried out to validate the proposal.

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References

  1. 1.
    Arkin, R.C., Balch, T.: AuRA: Principles and practice in review. Journal of Experimental and Theoretical Artificial Intelligence 7, 175–188 (1997)CrossRefGoogle Scholar
  2. 2.
    Hsiang, K., Kheng, W., Ang, M.: Integrated planning and control of mobile robot with self-organizing neural network. In: 18th IEEE Int. Conference on Robotics and Automation, Washington DC, pp. 3870–3875 (2002)Google Scholar
  3. 3.
    Aloimonos, J.Y.: Introduction: Active vision revisited. In: Aloimonos, J.Y. (ed.) Active Perception, Lawrence Erlbaum Assoc. Pub, New Jersey (1993)Google Scholar
  4. 4.
    D’Ambrosio, B.: Resource bounded-agents in an uncertain world. In: Proc. of the Workshop on Real-Time Artificial Intelligence Problems, Detroit, MI, USA (1989)Google Scholar
  5. 5.
    Stewart, D.B., Khosla, P.K.: Mechanisms for detecting and handling timing errors. Communications of the ACM 40, 87–93 (1997)CrossRefGoogle Scholar
  6. 6.
    Zilberstein, S.: Using anytime algorithms in intelligent systems. AI Magazine 17, 73–83 (1996)Google Scholar
  7. 7.
    Liu, J., Lin, K., Bettati, R., Hull, D., Yu, A.: Use of Imprecise Computation to Enhance Dependability of Real-Time Systems, pp. 157–182. Kluwer Academic Publishers, Dordrecht (1994)Google Scholar
  8. 8.
    Garvey, A.J., Lesser, V.: Design-to-time real-time scheduling. IEEE Trans. on Systems, Man and Cybernetics 23, 1491–1502 (1993)CrossRefGoogle Scholar
  9. 9.
    Picard, R.W.: Affective Computing. The MIT Press, Cambridge (1997)Google Scholar
  10. 10.
    Cañamero, D.: Modeling motivations and emotions as a basis for intelligent behavior. In: Lewis, J. (ed.) Proceedings of the First Int. Symposium on Autonomous Agents, pp. 148–155. ACM Press, New York (1997)CrossRefGoogle Scholar
  11. 11.
    Lorenzo, J., Castrillón, M., Hernández, M., Déniz, O.: Introduction of homeostatic regulation in face detection. In: Fred, A. (ed.) Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems, PRIS 2004, Porto (Portugal), pp. 5–14 (2004)Google Scholar
  12. 12.
    Lee, J.S., Jung, Y.Y., Kim, B.S., Sung-Jea, K.: An advanced video camera system with robust AF, AE and AWB control. IEEE Transactions on Consumer Electronics 47, 694–699 (2001)CrossRefGoogle Scholar
  13. 13.
    Nanda, H., Cutler, R.: Practical calibrations for a real-time digital onmidirectional camera. In: Proceedings of the Computer Vision and Pattern Recognition Conference, CVPR 2001 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Javier Lorenzo-Navarro
    • 1
  • Daniel Hernández
    • 1
  • Cayetano Guerra
    • 1
  • José Isern-González
    • 1
  1. 1.University of Las Palmas de Gran Canaria, Inst Univ. de Sistemas Inteligentes y Aplic. Num. en Ingeniería, Edif. Parque TecnológicoLas PalmasSpain

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