Adaptive methods are defined and experimentally studied for a two-scale edge detection process that mimics human visual perception of edges and is inspired by the parvocellular (P) and magnocellular (M) physiological subsystems of natural vision. This two-channel processing consists of a high spatial acuity/coarse contrast channel (P) and a coarse acuity/fine contrast (M) channel. We perform edge detection after a very strong nonlinear image enhancement that uses smart Retinex image processing. Two conditions that arise from this enhancement demand adaptiveness in edge detection. These conditions are the presence of random noise further exacerbated by the enhancement process and the equally random occurrence of dense textural visual information. We examine how to best deal with both phenomena with an automatic adaptive computation that treats both high noise and dense textures as too much information and gracefully shifts from small-scale to medium-scale edge pattern priorities. This shift is accomplished by using different edge-enhancement schemes that correspond with the P- and M-channels of the human visual system. We also examine the case of adapting to a third image condition-namely, too little visual information-and automatically adjust edge-detection sensitivities when sparse feature information is encountered. When this methodology is applied to a sequence of images of the same scene but with varying exposures and lighting conditions, this edge-detection process produces pattern constancy that is very useful for several imaging applications that rely on image classification in variable imaging conditions.