Abstract
Edge location in very high-resolution images may be affected by the statistical distribution of discrete grains that collectively form the opaque regions. An ideal case is considered in which a one-dimensional edge, represented by a step probability density function, is characterized by the location and contrast (magnitude relative to a background level) of the step discontinuity. This function is randomly sampled at a number of points, and a maximum likelihood criterion and a Monte Carlo technique are used to estimate the edge location from the samples. The root mean square deviation of this estimate from the actual edge location is obtained as a function of the edge contrast, the actual edge location, and the number of samples. This simulation is applicable to a class of single straight-edge images for which the number of samples is interpreted as the number of grains in the image and for which the edge orientation is known. 4-reNAyin -inn
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