Abstract
The search for a suitable golf course is a very important issue in the travel plans of any modern manager. Modern management is also infamous for its penchant for high-tech gadgetry. Here we combine these two facets of modern management life. We aim to provide the cutting-edge manager with a method of finding golf courses from space!
In this paper, we present Genie: a hybrid evolutionary algorithm-based system that tackles the general problem of finding features of interest in multi-spectral remotely-sensed images, including, but not limited to, golf courses. Using this system we are able to successfully locate golf courses in 10-channel satellite images of several desirable US locations.
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References
Cox, L.A., Jr., Davis, L., Qiu, Y.: Dynamic anticipatory routing in circuit-switched telecommunications networks, in Handbook of Genetic Algorithms, L. Davis, ed., pp. 124–143, Van Nostrand Reinhold, New York, 1991.
Harvey, N.R., Marshall, S.: GA Optimization of Spatio-Temporal Grey-Scale Soft Morphological Filters with Applications in Archive Film Restoration. In: Poli, R., Voigt, H.-M., Cagnoni, S., Corne, D., Smith, G.D., Fogarty, T.C. (eds.): Evolutionary Image Analysis, Signal Processing and Telecommunications (1999) pp. 31–45
Dandekar, T., Argos, P.: Potential of genetic algorithms in protein folding and protein engineering simulations, Protein Engineering 5(7), pp. 637–645, 1992.
Harris, C., Buxton, B.: Evolving edge detectors, Research Note RN/96/3, University College London, Dept. of Computer Science, London, 1996.
Teller, A., Veloso, M.: A controlled experiment: Evolution for learning difficult image classification, in 7th Portuguese Conference on Artificial Intelligence, Volume 990 of Lecture Notes in Computer Science, Springer-Verlag, Berlin, 1995.
Poli, R., Cagoni, S.: Genetic programming with user-driven selection: Experiments on the evolution of algorithms for image enhancement, in Genetic Programming 1997: Proceedings of the 2nd Annual Conference, J. R. Koza, et al., editors, Morgan Kaufmann, San Francisco 1997.
Nordin, P., Banzhaf, W.: Programmatic compression of images and sound, in Genetic Programming 1997: Proceedings of the 2nd Annual Conference, J. R. Koza, et al., editors, Morgan Kaufmann, San Francisco, 1996.
Daida, J.M., Hommes, J.D., Bersano-Begey, T.F., Ross, S.J., Vesecky, J.F.: Algorithm discovery using the genetic programming paradigm: Extracting low-contrast curvilinear features from SAR images of arctic ice, in Advances in Genetic Programming 2, P. J. Angeline and K. E. Kinnear, Jr., editors, chap. 21, MIT, Cambridge, 1996.
Brumby, S.P., Theiler, J., Perkins, S.J., Harvey, N.R., Szymanski, J.J., Bloch J.J., Mitchell, M.: Investigation of Image Feature Extraction by a Genetic Algorithm in Proc. SPIE 3812, pp. 24–31, 1999.
Theiler, J., Harvey, N.R., Brumby, S.P, Szymanski, J.J., Alferink, S., Perkins, S., Porter, R., Bloch, J.J.: Evolving Retrieval Algorithms with a Genetic Programming Scheme in Proc. SPIE 3812, in Press.
Koza, J.R.: Genetic programming: On the Programming of Computers by Means of Natural Selection MIT Press, 1992
Laws, K.I.: Texture energy measures in Proc. Image Understanding Workshop, Nov. 1979, pp. 47–51.
Pietikainen, M., Rosenfeld, A., Davis, L.S.: Experiments with Texture Classification using Averages of Local Pattern Matches IEEE Trans. on Systems, Man and Cybernetics, Vol. SMC-13, No. 3, May/June 1983, pp. 421–426.
Bishop, C.M.: Neural Networks for Pattern Recognition, pp. 105–112, Oxford University Press, 1995.
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C, 2nd Edition, Cambridge University Press, 1992, pp. 402–405..
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Harvey, N.R. et al. (2000). Finding Golf Courses: The Ultra High Tech Approach. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_6
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DOI: https://doi.org/10.1007/3-540-45561-2_6
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