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A novel method for determining target detection thresholds

机译:确定目标检测阈值的新方法

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摘要

Target detection is the act of isolating objects of interest from the surrounding clutter, generally using some form of test to include objects in the found class. However, the method of determining the threshold is overlooked relying on manual determination either through empirical observation or guesswork. The question remains: how does an analyst identify the detection threshold that will produce the optimum results? This work proposes the concept of a target detection sweet spot where the missed detection probability curve crosses the false detection curve; this represents the point at which missed detects are traded for false detects in order to effect positive or negative changes in the detection probability. ROC curves are used to characterize detection probabilities and false alarm rates based on empirically derived data. It identifies the relationship between the empirically derived results and the first moment statistic of the histogram of the pixel target value data and then proposes a new method of applying the histogram results in an automated fashion to predict the target detection sweet spot at which to begin automated target detection.
机译:目标检测是将感兴趣的对象与周围的杂波隔离的动作,通常使用某种形式的测试将对象包括在找到的类中。但是,根据经验观察或猜测,依靠手动确定来忽略确定阈值的方法。问题仍然存在:分析师如何确定将产生最佳结果的检测阈值?这项工作提出了目标检测最佳点的概念,其中错过的检测概率曲线与错误的检测曲线相交。这表示为了进行检测概率的正或负变化,将错过的检测换为错误检测的时间点。 ROC曲线用于根据经验得出的数据来表征检测概率和误报率。它确定了经验得出的结果与像素目标值数据直方图的第一时刻统计量之间的关系,然后提出了一种以自动方式应用直方图结果来预测目标检测最佳点的新方法,从该点开始自动目标检测。

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