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Boundary detection through dynamic polygons

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  • A. Pievatolo
  • P. J. Green

Abstract

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.

Suggested Citation

  • A. Pievatolo & P. J. Green, 1998. "Boundary detection through dynamic polygons," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(3), pages 609-626.
    • Handle: RePEc:bla:jorssb:v:60:y:1998:i:3:p:609-626
    DOI: 10.1111/1467-9868.00143
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    Cited by:

    1. Stoica, R.S. & Gregori, P. & Mateu, J., 2005. "Simulated annealing and object point processes: Tools for analysis of spatial patterns," Stochastic Processes and their Applications, Elsevier, vol. 115(11), pages 1860-1882, November.
    2. Peters, G.W. & Sisson, S.A. & Fan, Y., 2012. "Likelihood-free Bayesian inference for α-stable models," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3743-3756.

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