UNSPECIFIED. (1994) STOCHASTIC ANNEALING FOR NEAREST-NEIGHBOR POINT-PROCESSES WITH APPLICATION TO OBJECT RECOGNITION. ADVANCES IN APPLIED PROBABILITY, 26 (2). pp. 281-300. ISSN 0001-8678

Abstract

We study convergence in total variation of non-stationary Markov chains in continuous time and apply the results to the image analysis problem of object recognition. 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 found in stochastic geometry. We construct well-defined spatial birth-and-death processes that converge weakly to the posterior distribution. A simulated annealing algorithm involving a sequence of spatial birth-and-death processes is developed and shown to converge in total variation to a uniform distribution on the set of posterior mode solutions. The method is demonstrated on a tame example.

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