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Proceedings Paper

Target detection using texture operators
Author(s): James M. Coggins
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Paper Abstract

This study is an initial investigation into the efficacy of texture operators for detection of military vehicle targets-in-the-clear in SAR imagery. The specific study is a very simple problem that aims to evaluate a particular feature set that arises in an approach to computer vision called spatial spectroscopy. Spatial spectroscopy begins by partitioning the image's spatial (Fourier) spectrum using a bank of filters. The filters compute a multiscale, truncated Taylor Series expansion at each pixel. Suitably extended on generic images, this feature space is capable of producing a unique pattern describing each pixel. The objective, of course, is not to uniquely distinguish each pixel but to form groups of pixels corresponding to targets in SAR that are distinct from background pixels. Thus, nonlinear operators are required to fold, twist, and bend the feature space in ways that cause pixels that make up targets to group together. The particular nonlinear operators for a study depend on the invariances and equivariances of the problem. In the present case, a large suite of operators is applied to the image data and principal discriminant analysis is used to select the most relevant features. Texture operators are found to be effective at discriminating targets from background.

Paper Details

Date Published: 18 May 2006
PDF: 8 pages
Proc. SPIE 6234, Automatic Target Recognition XVI, 62340G (18 May 2006); doi: 10.1117/12.665798
Show Author Affiliations
James M. Coggins, BAE Systems (United States)

Published in SPIE Proceedings Vol. 6234:
Automatic Target Recognition XVI
Firooz A. Sadjadi, Editor(s)

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