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An edge detector based on the linear model is developed which utilizes the generalized likelihood ratio for statistical hypothesis testing. The detector is invariant to multiplicative changes in the gray-scale values of the image. Hence, thresholding based histogram segmentation is not required. The performance of this detector is analytically and experimentally compared to that of a gradient operator (Sobel) and is shown to have only a slightly poorer detection rate for a given false alarm rate.