Performance evaluation of the GA/SA hybrid heuristic optimum filter for optical pattern recognition

Jin Seon Yeun; Nam Kim; Jae Kyung Pan; R. S. Kim; J. U. Um; Sang Hyun Kim

doi:10.1117/12.269771

1 April 1997 Performance evaluation of the GA/SA hybrid heuristic optimum filter for optical pattern recognition

Jin Seon Yeun, Nam Kim, Jae Kyung Pan, R. S. Kim, J. U. Um, Sang Hyun Kim

Proceedings Volume 3030, Applications of Artificial Neural Networks in Image Processing II; (1997) https://doi.org/10.1117/12.269771
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

In this paper, we newly apply a genetic and simulated annealing hybrid heuristic to encode optimal filter for optical pattern recognition. Simulated annealing as a stochastic computational technique allows for finding near globally-minimum-cost solutions with cooling schedule. Using the advantages of a parallelizable genetic algorithm (GA) and a simulated annealing algorithm (SA), the optimum filters are designed and implemented. The filter having 128 multiplied by 128 pixel size consists of the stepped phase that causes the discrete phase delay. The structure of this can be divided into rectangular cells such that each cell imparts a discrete phase delay of 0 approximately equals 2 pi[rad] to the incident wave front. Eight-phase stepped filters that we designed are compared with phase only matched filter and cosine-binary phase only filter. It is deeply focused on investigating the performance of the optimum filter in terms of recognition characteristics on the translation, scale and rotation variations of the image, and discrimination properties against similar images. By GA/SA hybrid heuristic, the optimum filter is realized for high efficiency optical reconstruction in spite of decreasing iteration number needed to encode it by respective algorithms.

Citation Download Citation

Jin Seon Yeun, Nam Kim, Jae Kyung Pan, R. S. Kim, J. U. Um, and Sang Hyun Kim "Performance evaluation of the GA/SA hybrid heuristic optimum filter for optical pattern recognition", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); https://doi.org/10.1117/12.269771

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