A Methodology for Quantitative PerformanceEvaluation of Detection

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 mis-detection and false alarm are generated for each parameter setting. Such an analysis does not integrate the performance of the numerous operating curves. In this paper 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 eeect of variables in terms of the equivalent eeect 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.