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Boundary detection through dynamic polygons
Author(s) -
Pievatolo A.,
Green P. J.
Publication year - 1998
Publication title -
journal of the royal statistical society: series b (statistical methodology)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.523
H-Index - 137
eISSN - 1467-9868
pISSN - 1369-7412
DOI - 10.1111/1467-9868.00143
Subject(s) - polygon (computer graphics) , boundary (topology) , markov chain monte carlo , computer science , maximum a posteriori estimation , markov chain , probabilistic logic , reversible jump markov chain monte carlo , algorithm , convergence (economics) , monte carlo method , object (grammar) , constant (computer programming) , a priori and a posteriori , pixel , computation , bayesian probability , mathematics , artificial intelligence , machine learning , statistics , frame (networking) , maximum likelihood , mathematical analysis , telecommunications , philosophy , epistemology , economics , programming language , economic growth
A method for the Bayesian restoration of noisy binary images portraying an object with constant grey level on a background is presented. The restoration, performed by fitting a polygon with any number of sides to the object's outline, is driven by a new probabilistic model for the generation of polygons in a compact subset of R 2 , which is used as a prior distribution for the polygon. Some measurability issues raised by the correct specification of the model are addressed. The simulation from the prior and the calculation of the a posteriori mean of grey levels are carried out through reversible jump Markov chain Monte Carlo computation, whose implementation and convergence properties are also discussed. One example of restoration of a synthetic image is presented and compared with existing pixel‐based methods.