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Proceedings Paper

Optimization of OT-MACH filter generation for target recognition
Author(s): Oliver C. Johnson; Weston Edens; Thomas T. Lu; Tien-Hsin Chao
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Paper Abstract

An automatic Optimum Trade-off Maximum Average Correlation Height (OT-MACH) filter generator for use in a gray-scale optical correlator (GOC) has been developed for improved target detection at JPL. While the OT-MACH filter has been shown to be an optimal filter for target detection, actually solving for the optimum is too computationally intensive for multiple targets. Instead, an adaptive step gradient descent method was tested to iteratively optimize the three OT-MACH parameters, α, β, and γ. The feedback for the gradient descent method was a composite of the performance measures, correlation peak height and peak to side lobe ratio. The automated method generated and tested multiple filters in order to approach the optimal filter quicker and more reliably than the current manual method. Initial usage and testing has shown preliminary success at finding an approximation of the optimal filter, in terms of α, β, γ values. This corresponded to a substantial improvement in detection performance where the true positive rate increased for the same average false positives per image.

Paper Details

Date Published: 13 April 2009
PDF: 9 pages
Proc. SPIE 7340, Optical Pattern Recognition XX, 734008 (13 April 2009); doi: 10.1117/12.820950
Show Author Affiliations
Oliver C. Johnson, Harvey Mudd College (United States)
Weston Edens, Butler Univ., Purdue Univ. (United States)
Thomas T. Lu, Jet Propulsion Lab. (United States)
Tien-Hsin Chao, Jet Propulsion Lab. (United States)


Published in SPIE Proceedings Vol. 7340:
Optical Pattern Recognition XX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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