Reconstruction of polygonal images

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Abstract
Let T be a two-dimensional region, and let X be a surface dejined on T. The values of X on T, constitute an image, or pattern. The true value of X at any point on T cannot be directly observed, but data can be recorded which provide information about X. The aim is to reconstruct X using the prior knowledge that X will vary smoothly over most of T, but may exhibit jump discontinuities over line segments. This information can be incorporated via Bayes' theorem, using a polygonal Markov random field on T as prior distribution. Under this continuum model, X may in principle be estimated according to standard criteria. In practice, the techniques rely on simulation of the posterior distribution. A natural family of conjugate priors is identified, and a class of spatial-temporal Markov processes is constructed on the uncountable state space; simulation then proceeds by a method of analogous to the Gibbs sampler.