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STOCHASTIC ANNEALING FOR NEAREST-NEIGHBOR POINT-PROCESSES WITH APPLICATION TO OBJECT RECOGNITION
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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
Full text not available from this repository.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.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > QA Mathematics |
| Journal or Publication Title: | ADVANCES IN APPLIED PROBABILITY |
| Publisher: | APPLIED PROBABILITY TRUST |
| ISSN: | 0001-8678 |
| Date: | June 1994 |
| Volume: | 26 |
| Number: | 2 |
| Number of Pages: | 20 |
| Page Range: | pp. 281-300 |
| Publication Status: | Published |
| URI: | http://wrap.warwick.ac.uk/id/eprint/20610 |
Data sourced from Thomson Reuters' Web of Knowledge
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