A METHODOLOGY FOR QUANTITATIVE PERFORMANCE EVALUATION OF DETECTION ALGORITHMS

We present a methodology for the quantitative performance evaluation o f detection algorithms in computer vision. A common method is to gener ate a variety of input images by varying the image parameters and eval uate the performance of the algorithm, as algorithm parameters vary. O perating 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. In thi s paper, we outline a methodology for summarizing many operating curve s into a few performance curves. This methodology is adapted from the human psychophysics literature and is general to any detection algorit hm. 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-l ine detection algorithms.