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Performance of Peaky Template Matching Under Additive White Gaussian Noise and Uniform Quantization

机译：加性高斯白噪声和均匀量化下峰值模板匹配的性能

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摘要

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 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.
机译：峰值模板匹配（PTM）是被称为多项式模式匹配的通用算法的特例，该算法最初是为合成孔径雷达数据的自动目标识别而开发的。该算法是一种基于模型的方法，该方法首先将像素值量化为N_q-2个离散值，从而生成生成的Beta-Bernoulli模型作为类条件模板。在此，我们考虑了AWGN中目标芯片分类的情况，并根据噪声功率对图像到模板的分类性能进行了近似估算。我们特别关注“均匀量化”方案的情况，在这种情况下，固定数量的最大像素被量化得较高，而不是使用固定阈值。该量化方法降低了对像素强度的缩放的敏感性，并且量化通常降低了对难以解释为先验的各种有害参数的敏感性。使用来自陆军研究实验室科曼奇数据集的前瞻性红外图像验证了我们的性能表现。

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