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Proceedings Paper

New subspace methods for ATR
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Paper Abstract

In ATR applications, each feature is a convolution of an image with a filter. It is important to use most discriminant features to produce compact representations. We propose two novel subspace methods for dimension reduction to address limitations associated with Fukunaga-Koontz Transform (FKT). The first method, Scatter-FKT, assumes that target is more homogeneous, while clutter can be anything other than target and anywhere. Thus, instead of estimating a clutter covariance matrix, Scatter-FKT computes a clutter scatter matrix that measures the spread of clutter from the target mean. We choose dimensions along which the difference in variation between target and clutter is most pronounced. When the target follows a Gaussian distribution, Scatter-FKT can be viewed as a generalization of FKT. The second method, Optimal Bayesian Subspace, is derived from the optimal Bayesian classifier. It selects dimensions such that the minimum Bayes error rate can be achieved. When both target and clutter follow Gaussian distributions, OBS computes optimal subspace representations. We compare our methods against FKT using character image as well as IR data.

Paper Details

Date Published: 19 May 2005
PDF: 10 pages
Proc. SPIE 5807, Automatic Target Recognition XV, (19 May 2005); doi: 10.1117/12.604135
Show Author Affiliations
Peng Zhang, Tulane Univ. (United States)
Jing Peng, Tulane Univ. (United States)
S. Richard F. Sims, U.S. Army RD&E Command (United States)


Published in SPIE Proceedings Vol. 5807:
Automatic Target Recognition XV
Firooz A. Sadjadi, Editor(s)

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