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.
This work has been partially supported by the Spanish Ministry of Education and Science and FEDER funds under research project TIN2004-07087, the Canary Islands Regional Goverment under projects PI2003/165 and PI2003/160 and the University of Las Palmas under projects UNI2003/10, UNI2004/10 and UNI2004/25.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Arkin, R.C., Balch, T.: AuRA: Principles and practice in review. Journal of Experimental and Theoretical Artificial Intelligence 7, 175–188 (1997)
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)
Aloimonos, J.Y.: Introduction: Active vision revisited. In: Aloimonos, J.Y. (ed.) Active Perception, Lawrence Erlbaum Assoc. Pub, New Jersey (1993)
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)
Stewart, D.B., Khosla, P.K.: Mechanisms for detecting and handling timing errors. Communications of the ACM 40, 87–93 (1997)
Zilberstein, S.: Using anytime algorithms in intelligent systems. AI Magazine 17, 73–83 (1996)
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)
Garvey, A.J., Lesser, V.: Design-to-time real-time scheduling. IEEE Trans. on Systems, Man and Cybernetics 23, 1491–1502 (1993)
Picard, R.W.: Affective Computing. The MIT Press, Cambridge (1997)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lorenzo-Navarro, J., Hernández, D., Guerra, C., Isern-González, J. (2005). A Proposal for a Homeostasis Based Adaptive Vision System. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3522. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492429_23
Download citation
DOI: https://doi.org/10.1007/11492429_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26153-7
Online ISBN: 978-3-540-32237-5
eBook Packages: Computer ScienceComputer Science (R0)