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19 March 2009 Aided infrared target classifier pre-processing by adaptive local contrast enhancement
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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.
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Ming Kai Hsu, Harold Szu, and Ting N. Lee "Aided infrared target classifier pre-processing by adaptive local contrast enhancement", Proc. SPIE 7343, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering VII, 73431F (19 March 2009);

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