Abstract
Development of efficient methods for automatic target recognition on the battlefield is one of the important research areas of electronic imaging. Evolutionary algorithms (EA) have been successfully used for solving a few electronic imaging problems closely related to target recognition such as pattern matching, semantic scene interpretation, and image registration.
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References
L. Zhang, W. Xu, and C. Chang. Genetic algorithm for affine point pattern matching. Pattern Recognit. Lett., 24(1–3):9–19, 2003.
C. A. Ankenbrandt, B. P. Buckles, and F. E. Petry. Scene recognition using genetic algorithms with semantic nets. Pattern Recognit. Lett., 11(4):231–304, 1990.
B. P. Buckles and F. E. Petry. Cloud Identification Using Genetic Algorithms and Massively Parallel Computation. Final Report, Grant No. NAG 5-2216, Center for Intelligent and Knowledge-Based Systems, Dept. of Computer Science, Tulane University, New Orleans, LA, June 1996.
J. M. Fitzpatrick and J. J. Grefenstette. Genetic algorithms in noisy environments. Mach. Learn., 3(2/3):101–120, 1988.
J. J. Grefenstette and J. M. Fitzpatrick. Genetic search with approximate function evaluations. In Proceedings of the First International Conference on Genetic Algorithms and Their Applications. Lawrence Erlbaum, Pittsburgh, PA, pp. 112–120, 1985.
V. R. Mandava, J. M. Fitzpatrick, and D. R. Pickens III. Adaptive search space scaling in digital image registration. IEEE Trans. Med. Imaging, 8(3):251–262, 1989.
I. V. Maslov. Reducing the cost of the hybrid evolutionary algorithm with image local response in electronic imaging. In Lecture Notes in Computer Science, vol. 3103, Springer-Verlag, Berlin, Heidelberg, pp. 1177–1188, 2004.
I. V. Maslov and I. Gertner. Object recognition with the hybrid evolutionary algorithm and response analysis in security applications. Opt. Eng., 43(10):2292–2302, 2004.
I. V. Maslov and I. Gertner. Reducing the computational cost of local search in the hybrid evolutionary algorithm with application to electronic imaging. Eng. Optim., 37(1):103–119, 2005.
W. E. Hart and R. K. Belew, Optimization with genetic algorithm hybrids that use local search. In Adaptive Individuals in Evolving Populations: Models and Algorithms: Proceedings of Santa Fe Institute Studies in the Sciences of Complexity, 26. Addison-Wesley, Reading, MA, pp. 483–496, 1996.
J. A. Joines and M. G. Kay. Utilizing hybrid genetic algorithms. In Evolutionary Optimization. Kluwer, Boston, MA, pp. 199–228, 2002.
J. A. Nelder and R. Mead. A simplex method for function minimization. Comput. J., 7(4):308–313, 1965.
M. H. Wright. Direct search methods: Once scorned, now respectable. In Numerical Analysis, 1995: Proceedings of the 1995 Dundee Biennial Conference in Numerical Analysis. Addison Wesley Longman, Harlow, UK, pp. 191–208, 1996.
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Maslov, I., Gertner, I. (2007). Multisensor Target Recognition in Image Response Space Using Evolutionary Algorithms. In: Sadjadi, F., Javidi, B. (eds) Physics of Automatic Target Recognition. Advanced Sciences and Technologies for Security Applications, vol 3. Springer, New York, NY. https://doi.org/10.1007/978-0-387-36943-3_8
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DOI: https://doi.org/10.1007/978-0-387-36943-3_8
Publisher Name: Springer, New York, NY
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