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- W309149368 abstract "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 N q = 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 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." @default.
- W309149368 created "2016-06-24" @default.
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- W309149368 date "2015-05-22" @default.
- W309149368 modified "2023-09-23" @default.
- W309149368 title "Performance of peaky template matching under additive white Gaussian noise and uniform quantization" @default.
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- W309149368 doi "https://doi.org/10.1117/12.2176220" @default.
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