Document details for 'Reversible jump MCMC methods and segmentation algorithms in hidden Markov models'

Authors Paroli, R. and Spezia, L.
Publication details Australian & New Zealand Journal of Statistics 52, 151-166.
Keywords Clustering; digital images; label switching; Peano-Hilbert scan; post-processing
Abstract We consider hidden Markov Models with an unknown number of regimes for the segmentation of the pixel intensities of digital images that consist of a small set of colours. New reversible jump Markov chain Monte Carlo algorithms to estimate both the dimension and the unknown parameters of the model are introduced. Parameters are updated by random walk Metropolis-Hastings moves, without updating the sequence of the hidden Markov chain. The segmentation, i.e. the estimation of the hidden regimes, is another aim and it is performed by means of some competing algorithms. We apply our Bayesian inference and segmentation tools to digital images, which are linearized through the Peano-Hilbert scan, and make experiments and comparisons on both synthetic images and a real brain magnetic resonance image.
Last updated 2010-06-17

Unless explicitly stated otherwise, all material is copyright © Biomathematics and Statistics Scotland.

Biomathematics and Statistics Scotland (BioSS) is formally part of The James Hutton Institute (JHI), a registered Scottish charity No. SC041796 and a company limited by guarantee No. SC374831. Registered Office: JHI, Invergowrie, Dundee, DD2 5DA, Scotland