Abstract
The Bayesian approach to image processing based on Markov random fields is adapted to image analysis problems such as object recognition and edge detection. Here the input is a grey-scale or binary image and the desired output is a graphical pattern in continuous space, such as a list of geometric objects or a line drawing. The natural prior models are Markov point processes and random sets. We develop analogues of Besag’s ICM algorithm and present relationships with existing techniques like the Hough transform and the erosion operator.
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