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Proceedings Paper

Use of shifted phase-encoded joint transform correlation for class-associative color pattern recognition
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Paper Abstract

Color pattern recognition techniques involve the separation of basic color components, red, green and blue, by using color filters. Although several joint transform correlation architectures have been proposed in literature for color pattern recognition, however, these algorithms are suitable for single color target detection only and most of them are sensitive to noise and do not efficiently utilizes the space bandwidth product. A new shifted phase-encoded fringe-adjusted joint transform correlation (SPJTC) technique has been proposed in this paper for class-associative color pattern recognition. The color images are first split into three fundamental color components and the individual components are then processed simultaneously through three different channels. The SPJTC technique for each color component again involves two channels, one with the reference image and the other with 180° phase-shifted reference image. Both are phase masked using a random phase and then used with the input scene. The joint power spectra (JPS) are again phase masked and subtracted one from the other. The resultant JPS yields the desired correlation after inverse Fourier transformation. A modified class-associative color fringe adjusted filter is developed for providing single and sharp correlation peak per target while satisfying the equal correlation peak criterion for each class member. The salient feature of the proposed scheme is that the number of channels and processing steps remains constant irrespective of the number of members in the class. Computer simulation verifies the effectiveness of the proposed technique for color images both in binary and gray levels even in presence of noise.

Paper Details

Date Published: 17 April 2006
PDF: 8 pages
Proc. SPIE 6245, Optical Pattern Recognition XVII, 62450F (17 April 2006); doi: 10.1117/12.666228
Show Author Affiliations
Mohammed Nazrul Islam, Univ. of South Alabama (United States)
Mohammad S. Alam, Univ. of South Alabama (United States)

Published in SPIE Proceedings Vol. 6245:
Optical Pattern Recognition XVII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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