Reconstruction of polygonal images
作者:
Peter Clifford,
R. D. Middleton,
期刊:
Journal of Applied Statistics
(Taylor Available online 1989)
卷期:
Volume 16,
issue 3
页码: 409-422
ISSN:0266-4763
年代: 1989
DOI:10.1080/02664768900000050
出版商: Carfax Publishing Company
数据来源: Taylor
摘要:
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.
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