THE FLY ALGORITHM REVISITED - Adaptation to CMOS Image Sensors

Emmanuel Sapin, Jean Louchet, Evelyne Lutton

Abstract

Cooperative coevolution algorithms (CCEAs) usually represent a searched solution as an aggregation of several individuals (or even as a whole population). In other terms, each individual only bears a part of the searched solution. This scheme allows to use the artificial Darwinism principles in a more economic way, and the gain in terms of robustness and efficiency is important. In the computer vision domain, this scheme has been applied to stereovision, to produce an algorithm (the fly algorithm) with asynchronism property. However, this property has not yet been fully exploited, in particular at the sensor level, where CMOS technology opens perpectives to faster reactions. We describe in this paper a new coevolution engine that allow the Fly Algorithm to better exploit the properties of CMOS image sensors.

References

  1. Bongard, J. and Lipson, H. (2005). Active coevolutionary learning of deterministic finite automata. Journal of Machine Learning Research 6, pages 1651-1678.
  2. Boumaza, A. and Louchet, J. (2001). Using real-time parisian evolution in robotics. EVOIASP2001. Lecture Notes in Computer Science, 2037:288-297.
  3. Boumaza, A. and Louchet, J. (2003). Mobile robot sensor fusion using flies. S. Cagnoni et al. (Eds.), Evoworkshops 2003. Lecture Notes in Computer Science, 2611:357-367.
  4. Bucci, A. and Pollack, J. B. (2005). On identifying global optima in cooperative coevolution. GECCO 7805: Proceedings of the 2005 conference on Genetic and evolutionary,.
  5. Cagnoni, S., Lutton, E., and Olague, G. (2008). Genetic and evolutionary computation for image processing and analysis. Genetic and Evolutionary Computation for Image Processing and Analysis.
  6. Chalimbaud, P. and Berry, F. (2004). Use of a cmos imager to design an active vision sensor. In 14me Congrs Francophone AFRIF-AFIA de Reconnaissance des Formes et Intelligence Artificielle.
  7. Collet, P., Lutton, E., Raynal, F., and Schoenauer, M. (2000). Polar ifs + parisian genetic programming = efficient ifs inverse problem solving. In In Genetic Programming and Evolvable Machines Journal, pages 339-361.
  8. Gamal, A. E. (2002). Trends in cmos image sensor technology and design. Minternational electron devices meeting, pages 805-808.
  9. Horn, B. H. (1986). Robot vision. McGraw Hill.
  10. Jong, E. D., Stanley, K., and Wiegand., R. (2007). Genetic and evolutionary computation for image processing and analysis. Introductory tutorial on coevolution, ECCO 7807.
  11. Larnaudie, F., Guardiola, N., Saint-P, O., Vignon, B., Tulet, M., and Davancens, R. (2004). Development of a 750 × 750 pixels cmos imager sensor for tracking applications. Proceedings of the 5th International Conference on Space Optics (ICSO 2004), pages 809-816.
  12. Louchet, J. (2000). From hough to darwin : an individual evolutionary strategy applied to artificial vision. Artificial Evolution 99. Lecture Notes in Computer Science, 1829:145-161.
  13. Louchet, J. (2001). Using an individual evolution strategy for stereovision. Genetic Programming and Evolvable Machines, 2(2).
  14. Louchet, J. and Sapin, E. (2009). Flyes open a door to slam. In EvoWorkshops. Lecture Notes in Computer Science, 5484:385-394.
  15. Ochoa, G., Lutton, E., and Burke, E. (2007). Cooperative royal road functions. In Evolution Artificielle, Tours, France, October 29-31, 2007.
  16. Panait, L., Luke, S., and Harrison, J. F. (2006). Archive-based cooperative coevolutionary algorithms. GECCO 7806: Proceedings of the 8th annual conference on Genetic and evolutionary computation.
  17. Tajima, K., Numata, A., and Ishii, I. (2004). Development of a high-resolution, high-speed vision system using cmos image sensor technology enhanced by intelligent pixel selection technique. Machine vision and its optomechatronic applications., 5603:215-224.
  18. Wiegand, R. and Potter, M. (2006). Robustness in cooperative coevolution. GECCO 7806: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 215-224.
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Paper Citation


in Harvard Style

Sapin E., Louchet J. and Lutton E. (2009). THE FLY ALGORITHM REVISITED - Adaptation to CMOS Image Sensors . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 224-229. DOI: 10.5220/0002319202240229


in Bibtex Style

@conference{icec09,
author={Emmanuel Sapin and Jean Louchet and Evelyne Lutton},
title={THE FLY ALGORITHM REVISITED - Adaptation to CMOS Image Sensors},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009)},
year={2009},
pages={224-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002319202240229},
isbn={978-989-674-014-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICEC, (IJCCI 2009)
TI - THE FLY ALGORITHM REVISITED - Adaptation to CMOS Image Sensors
SN - 978-989-674-014-6
AU - Sapin E.
AU - Louchet J.
AU - Lutton E.
PY - 2009
SP - 224
EP - 229
DO - 10.5220/0002319202240229