Boundary Detection Through Dynamic Polygons

Journal of the Royal Statistical Society Series B (Statistical Methodology)Published 1 September 1998Open access

Antonio Pievatolo, Peter J. Green

DOI10.1111/1467-9868.00143

Source URL

Citations43

SJR quartileQ1

SJR score3.31

SNIP2.48

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TL;DR

A method for the Bayesian restoration of noisy binary images portraying an object with constant grey level on a background with a new probabilistic model for the generation of polygons in a compact subset of R2, which is used as a prior distribution for the polygon.

Abstract

Summary 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 R2, 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.

Keywords

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