Optimal Likelihood Generators for Edge Detection under Gaussian Additive Noise.
ROCHESTER UNIV NY DEPT OF COMPUTER SCIENCE
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A technique is presented for determining the probability of an edge at a point in an image that is convolve with a linear blurring function and also with uncorrelated Gaussian additive noise. The ideal image is modeled by a set of templates for local neighborhoods. Every neighborhood in the ideal image is assumed to fit one of the templates with height probability. A computationally feasible scheme to compute the probability of edges is given. The output of several of the likelihood generators based on this model can be combined to form a more robust likelihood generator. Keywords Edge detection Template Likelihood Bayesian reasoning.