22 May 2015 Performance of peaky template matching under additive white Gaussian noise and uniform quantization
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Peaky template matching (PTM) is a special case of a general algorithm known as multinomial pattern matching originally developed for automatic target recognition of synthetic aperture radar data. The algorithm is a model- based approach that first quantizes pixel values into Nq = 2 discrete values yielding generative Beta-Bernoulli models as class-conditional templates. Here, we consider the case of classification of target chips in AWGN and develop approximations to image-to-template classification performance as a function of the noise power. We focus specifically on the case of a uniform quantization" scheme, where a fixed number of the largest pixels are quantized high as opposed to using a fixed threshold. This quantization method reduces sensitivity to the scaling of pixel intensities and quantization in general reduces sensitivity to various nuisance parameters difficult to account for a priori. Our performance expressions are verified using forward-looking infrared imagery from the Army Research Laboratory Comanche dataset.
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Matthew S. Horvath, Matthew S. Horvath, Brian D. Rigling, Brian D. Rigling, "Performance of peaky template matching under additive white Gaussian noise and uniform quantization", Proc. SPIE 9476, Automatic Target Recognition XXV, 94760L (22 May 2015); doi: 10.1117/12.2176220; https://doi.org/10.1117/12.2176220

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