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Because of the difficulty of obtaining ground truth for real images, the traditional technique for comparing low-level vision algorithms is to present image results, side by side, and to let the reader subjectively judge the quality. This is not a scientifically satisfactory strategy. However, human rating experiments can be done in a more rigorous manner to provide useful quantitative conclusions. We present a paradigm based on experimental psychology and statistics, in which humans rate the output of low level vision algorithms. We demonstrate the proposed experimental strategy by comparing four well-known edge detectors: Canny, Nalwa–Binford, Sarkar–Boyer, and Sobel. We answer the following questions: Is there a statistically significant difference in edge detector outputs as perceived by humans when considering an object recognition task? Do the edge detection results of an operator vary significantly with the …