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4 December 2000 Application of local discriminant bases discrimination algorithm for theater missile defense
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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.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mary L. Cassabaum, Harry A. Schmitt, Hai-Wen Chen, and Jack G. Riddle "Application of local discriminant bases discrimination algorithm for theater missile defense", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000);

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