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Proceedings Paper

Class-associative pattern recognition using joint transform correlation
Author(s): Mohammad S. Alam
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Paper Abstract

In this paper, we investigate the latest advancements in real time pattern recognition using the joint transform correlator (JTC) architectures and algorithms. We propose two class associative correlation filters to detect a class of objects consisting of dissimilar patterns. For enhanced performance, both phase and amplitude information is incorporated in the class detection filters. To suppress undesired crosscorrelation between selected objects a new algorithm is introduced. In addition fringe-adjusted joint transform correlation is utilized to enhance the correlation performance, thus ensuring strong and equal correlation peak for each element of the selected class. The feasibility of the proposed technique has been tested by computer simulation.

Paper Details

Date Published: 20 November 2002
PDF: 11 pages
Proc. SPIE 4803, Photorefractive Fiber and Crystal Devices: Materials, Optical Properties, and Applications VIII, (20 November 2002); doi: 10.1117/12.456550
Show Author Affiliations
Mohammad S. Alam, Univ. of South Alabama (United States)


Published in SPIE Proceedings Vol. 4803:
Photorefractive Fiber and Crystal Devices: Materials, Optical Properties, and Applications VIII
Francis T. S. Yu; Ruyan Guo, Editor(s)

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