A GP Artificial Ant for image processing: preliminary experiments with EASEA
Inhaltsverzeichnis
Reference
Enzo Bolis and Christian Zerbi and Pierre Collet and Jean Louchet and Evelyne Lutton: A GP Artificial Ant for image processing: preliminary experiments with EASEA. Genetic Programming, Proceedings of EuroGP'2001, LNCS, Vol. 2038, pp. 246-255, Springer-Verlag, 18-20 April 2001.
DOI
Abstract
This paper describes how animat-based “food foraging” techniques may be applied to the design of low-level image processing algorithms. First, we show how we implemented the food foraging application using the EASEA software package. We then use this technique to evolve an animat and learn how to move inside images and detect high-gradient lines with a minimum explora- tion time. The resulting animats do not use standard “scanning + filtering” tech- niques but develop other image exploration strategies close to contour tracking. Experimental results on grey level images are presented.
Extended Abstract
Bibtex
Used References
[1] D. H. Ballard and C. M. Brown, Computer Vision, Prentice Hall, 1982.
[2] R. J. V. Bertin and W. A. van de Grind, The Influence of Light-Dark Adaptation and Lateral Inhibition on Phototaxic Foraging: A Hypothetical Animal Study, Pages 141-167, Adaptive Behavior, Volume 5, Number 2, Fall 1996.
[3] V. Cantoni, M. Ferretti, S. Levialdi, R. Negrini and S. Stefanelli, Progress in Image Analysis and Processing, World Scientific,1991.
[4] D. Cliff and S. Bullock, Adding “Foveal Vision” to Wilson's Animat, Pages 49-72, Adaptive Behavior, Volume 2, Number 1, Summer 1993.
[5] P. Collet, E. Lutton, M. Schoenauer, J. Louchet, Take it EASEA, Parallel Problem Solving from Nature VI, vol 1917, Springer pp 891-901, Paris, September 2000.
[6] R. J. Collins and D. R. Jefferson. Antfarm: Towards simulated evolution . In S. Rasmussen, J. Farmer, C. Langton and C. Taylor, editors, Artificial Life II, Reading, Massachusetts, Addison-Wesley, 1991.
[7] F. L. Crabbe, Michael G. Dyer, Observation and Imitation: Goal Sequence Learning in Neurally Controlled Construction Animats: VI-MAXSON, SAB 2000, Paris.
[8] EASEA (EAsy Specification of Evolutionary Algorithms) home page: http://www-rocq.inria.fr/EASEA/
[9] EO (Evolutionary Objects) home page: http://geneura.ugr.es/~jmerelo/EO.html
[10] GAlib home page: http://lancet.mit.edu/ga/
[11] P. Gaussier. Autonomous Robots interacting with an unknown world, Special Issue on Animat Approach to Control, Robotics and Autonomous Systems, 16, 1995.
[12] R. C. Gonzalez, R. E. Woods, Digital Image Processing, Wiley, 1992
[13] J. Ivins, J. Porrill, Statistical Snakes: Active Region Models, British Machine Vision Conference, York, Sep. 1994.
[14] R.C. Jain, R. Kasturi, B.G. Schunck, Machine Vision, McGraw-Hill, 1995.
[15] D. Jefferson, R. Collins, C. Cooper, M. Dyer, M. Flower, R. Korf, C. Taylor, A. Wang, Evolution as a theme in artificial life: the Genesys/Tracker system, Artificial life II, vol. X, Santa Fe Institute Studies in the Sciences of Complexity, Addison- Wesley, Feb. 1992, 549-578.
[16] M. Köppen, B. Nickolay, Design of image exploring agent using genetic pro- gramming. In Proceedings of the 4th International Conference on Soft Computing, volume 2, pages 549--552, Fukuoka, Japan, 30. Sep - 5. Oct 1996. World Scientific, Singapore.
[17] M. Köppen, B. Nickolay, Design of Image Exploring Agent using Genetic Pro- gramming. Fuzzy Sets and Systems, Special Issue on Softcomputing, 103 (1999) 303-315.
[18] J. R. Koza, Genetic Programming, MIT Press 1992.
[19] J. R. Koza, J. Roughgarden and J. P. Rice, Evolution of Food-Foraging Strategies for the Caribbean Anolis Lizard Using Genetic Programming, Pages 171-199, Adaptive Behavior, Volume 1, Number 2, Fall 1992.
[20] I. Kuscu, A genetic Constructive Induction Model. In P. J. Angeline, Z. Michalewicz, M. Schoenauer, XinYao, and A. Zalzala, editors, Proceedings of the Congress on Evolutionary Computation , volume 1, pages 212-217, Mayflower Hotel, Washington D.C., USA, 6-9 July 1999. IEEE Press.
[21] W. B. Langdon, Genetic Programming and Data Structures : Genetic Programming + Data Structures = Automatic Programming !, Kluwer, 1998.
[22] W. B. Langdon and R. Poli, Better Trained Ants for Genetic Programming, Technical Report CSRP-98-12, April 1998, http://www.cs.bham.ac.uk/wbl.
[23] W. B. Langdon and R. Poli, Why Ants are Hard, Technical Report: CSRP-98-4, January 1998, http://www.cs.bham.ac.uk/wbl.
[24] J. Lévy Vehel, introduction to the multifractal analysis of images, in Fractal image encoding and analysis, Yuval Fischer ed., Springer Verlag, 1996.
[25] J. A. Meyer, A. Guillot, From SAB90 to SAB94: Four Years of Animat Research, Proceedings of Third International Conference on Simulation of Adaptive Behavior. Brighton, England, 1994.
[26] R. Moller, D. Lambrinos, R. Pfeifer, T. Labhart, and R. Wehner, Modeling Ant Navigation with an Autonomous Agent, From Animals to Animats 5, Proc. of the 5th Int. Conf. on Simulation of Adaptive Behavior, August 17-21, 1998, Zurich, Switzerland, edited by R. Pfeifer, B. Blumberg, J.-A. Meyer and S. W. Wilson
[27] T. J. Prescott, Spatial Representation for Navigation in Animats, Adaptive Behavior, Volume 4, Number 2, Fall 1995, 85-123
[28] S. W. Wilson, Classifier systems and the animat problem, Machine Learning 2 (1987), 199-228.
[29] S. W. Wilson, (1991). The animat approach to AI . In J. Meyer & S. W. Wilson (Eds), From Animals to Animats, Proceedings of the first International Conference on Simulation of Adaptive Behavior, Cambridge, MA: MIT Press, 15-21.
[30] M. Witkowski, The Role of Behavioral Extinction in Animat Action Selection, SAB 2000, Paris, 2000.
Links
Full Text
http://evelyne.lutton.free.fr/Papers/22_EuroGPFinal.pdf
