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Proceedings Paper

Application of local discriminant bases discrimination algorithm for theater missile defense
Author(s): Mary L. Cassabaum; Harry A. Schmitt; Hai-Wen Chen; Jack G. Riddle
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Paper Abstract

The local discriminant bases (LDB) method is a powerful algorithmic framework that was originally developed by Coifman and Saito in 1994 as a technique for analyzing object classification problems. LDB is a feature extraction algorithm which selects a best-basis from a library of orthogonal bases based on relative entropy or a similar metric. The localized nature of these orthogonal basis functions often results in features that are easier to interpret and more intuitive than those obtained form more conventional methods. An evaluation of the best-basis technique using LDB was conducted with IR sensor data. In particular, our data set consisted of the intensity fluctuations of subpixel targets collected don a focal plane array. This 1D dat set provides a useful benchmark against current feature estimation/extraction algorithms as well as preparation for the much more difficult 2D problem. Significantly, LDB is an automated procedure. This has a number of potential advantages, including the ability to: (1) easily handle an increased threat set; and (2) significantly improve the productivity of the feature estimation 'expert' by removing them from the mechanics of the classification process.

Paper Details

Date Published: 4 December 2000
PDF: 8 pages
Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); doi: 10.1117/12.408572
Show Author Affiliations
Mary L. Cassabaum, Raytheon Systems Co. (United States)
Harry A. Schmitt, Raytheon Systems Co. (United States)
Hai-Wen Chen, Raytheon Systems Co. (United States)
Jack G. Riddle, Raytheon Systems Co. (United States)

Published in SPIE Proceedings Vol. 4119:
Wavelet Applications in Signal and Image Processing VIII
Akram Aldroubi; Andrew F. Laine; Michael A. Unser, Editor(s)

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