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Proceedings Paper

Aided infrared target classifier pre-processing by adaptive local contrast enhancement
Author(s): Ming Kai Hsu; Harold Szu; Ting N. Lee
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Paper Abstract

AiTR is well developed field of R/D. Nonetheless. a relatively poor under-sampled infrared video may achieve a sharper imagery by smart pre-processing, similar to super-resolution attempts; the difference is in the details. We took a local adaptive contrast enhancement to exploit the pixel intensity correlation, such as smoothness, contrast, and continuity, among neighborhood pixels of variable region sizes, so-called adaptive local contrast enhancement. The final success or failure rate of AiTR will depend on the choice of cost function, such as LMS, etc. In that, we found that a sparse samples do not satisfy the usual underlying Gaussian assumption, of which the Maximum Likelihood, the Bayesian, the Fisher Rao criteria, etc. are usually depending on a priori assumption of dense sampling approaching the Gaussian statistics. Thus, in this paper, we have developed a sparse sampling classifier, called the min-Max classifier for Aided Target Recognition (AiTR), to minimize the intra-class dispersion and at the same to maximize the inter-class separation to select the optimum features vectors. As a standard test case, we choose Petland eigen-faces to benchmark our performance. We apply Szu's lossless divide and conquer theorem solving the NP Complete TSP solution to treat the multiple classes AiTR, in order to achieve min-Max classifier more efficiently than pair-wise SVM classifier.

Paper Details

Date Published: 19 March 2009
PDF: 20 pages
Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73431F (19 March 2009); doi: 10.1117/12.822203
Show Author Affiliations
Ming Kai Hsu, George Washington Univ. (United States)
Harold Szu, Army NVESD (United States)
Ting N. Lee, George Washington Univ. (United States)

Published in SPIE Proceedings Vol. 7343:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII
Harold H. Szu; F. Jack Agee, Editor(s)

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