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Proceedings Paper

Class-associative multiple-target detection using fringe-adjusted joint transform correlation
Author(s): Mohammad S. Alam; Mohammed Moseeur Rahman
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Paper Abstract

We propose a class-associative correlation filter based technique for detecting a class of objects consisting of dissimilar patterns. The fringe-adjusted joint transform correlation algorithm is utilized to enhance the correlation performance, thus ensuring strong and equal correlation peak for each element of the selected class. For enhanced performance, an enhanced version of the fringe-adjusted filter is incorporated in the class-associative multiple target detection process. The feasibility of the proposed technique has been tested by computer simulation.

Paper Details

Date Published: 6 March 2002
PDF: 9 pages
Proc. SPIE 4734, Optical Pattern Recognition XIII, (6 March 2002); doi: 10.1117/12.458406
Show Author Affiliations
Mohammad S. Alam, Univ. of South Alabama (United States)
Mohammed Moseeur Rahman, Univ. of South Alabama (United States)


Published in SPIE Proceedings Vol. 4734:
Optical Pattern Recognition XIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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