Dept. of Electr. Eng., Washington Univ., Seattle, WA.
IEEE Trans Image Process. 1995;4(12):1667-74. doi: 10.1109/83.475516.
We present a methodology for the quantitative performance evaluation of detection algorithms in computer vision. A common method is to generate a variety of input images by varying the image parameters and evaluate the performance of the algorithm, as algorithm parameters vary. Operating curves that relate the probability of misdetection and false alarm are generated for each parameter setting. Such an analysis does not integrate the performance of the numerous operating curves. We outline a methodology for summarizing many operating curves into a few performance curves. This methodology is adapted from the human psychophysics literature and is general to any detection algorithm. The central concept is to measure the effect of variables in terms of the equivalent effect of a critical signal variable, which in turn facilitates the determination of the breakdown point of the algorithm. We demonstrate the methodology by comparing the performance of two-line detection algorithms.
我们提出了一种用于计算机视觉中检测算法的定量性能评估的方法。一种常见的方法是通过改变图像参数生成各种输入图像，并随着算法参数的变化来评估算法的性能。为每个参数设置生成了相关误检概率和虚报概率的工作曲线。这样的分析并没有集成众多工作曲线的性能。我们概述了一种将许多工作曲线总结为少数性能曲线的方法。该方法源自人类心理物理学文献，适用于任何检测算法。核心概念是以关键信号变量的等效效应来衡量变量的影响，这反过来又有助于确定算法的失效点。我们通过比较双线检测算法的性能来演示该方法。
