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Proceedings Paper

Pattern recognition using Gaussian-filtered, shifted phase-encoded fringe-adjusted joint transform correlation
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Paper Abstract

Pattern recognition for real-time applications requires the detection scheme be a simple architecture, fast in operation, able to detect all the potential targets without generating any false alarms, and invariant to noise and distortion. Though several target detection algorithms have been proposed in the literature over the years, but most of them are found to be not as efficient in meeting all the above-mentioned objective requirements. A new Gaussian-filtered, shifted phase-encoded fringe-adjusted joint transform correlation technique has been developed in this paper for an optical pattern recognition system. The input noisy image is first filtered by using a Gaussian filter, which helps in overcoming the effect of background noise and distortions. Then the filtered image is correlated with the reference image using the proposed joint transform correlator, which eliminates the problems of duplicate correlation heights, false alarms and low discrimination ratio. The architecture involves optical devices including lenses and spatial light modulators, which guarantees the very fast operation required for real-time applications. Computer simulation results show that the algorithm can successfully discriminate between targets and non-targets contained in the input scene even in the presence of noise and can also make the best utilization of the correlation space.

Paper Details

Date Published: 17 March 2008
PDF: 8 pages
Proc. SPIE 6977, Optical Pattern Recognition XIX, 69770A (17 March 2008); doi: 10.1117/12.776954
Show Author Affiliations
Mohammed Nazrul Islam, Old Dominion Univ. Research Foundation (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)
K. Vijayan Asari, Old Dominion Univ. (United States)
Mohammad A. Karim, Old Dominion Univ. (United States)


Published in SPIE Proceedings Vol. 6977:
Optical Pattern Recognition XIX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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