Title :
Image segmentation using the double Markov random field, with application to land use estimation
Author :
Wilson, Simon P. ; Stefanou, Georgios
Author_Institution :
Dept. of Stat., Trinity Coll., Dublin, Ireland
fDate :
6/23/1905 12:00:00 AM
Abstract :
We describe the double Markov random field, a natural hierarchical model for a Bayesian approach to model-based textured image segmentation. The model is difficult to implement, even using Markov chain Monte Carlo (MCMC) methods, so we describe an approximation that is computationally feasible. This is applied to a satellite image. We emphasise the valuable additional information about uncertainties in the segmentation that can be gained from the use of MCMC
Keywords :
Bayes methods; Markov processes; Monte Carlo methods; agriculture; image segmentation; image texture; random processes; Bayesian approach; MCMC; Markov chain Monte Carlo methods; agricultural region; approximation; double Markov random field; hierarchical model; land use estimation; model-based textured image segmentation; pseudolikelihood approximation; satellite image; Bayesian methods; Educational institutions; Equations; Image segmentation; Information analysis; Markov random fields; Monte Carlo methods; Sampling methods; Satellites; Statistics;
Conference_Titel :
Image Processing, 2001. Proceedings. 2001 International Conference on
Conference_Location :
Thessaloniki
Print_ISBN :
0-7803-6725-1
DOI :
10.1109/ICIP.2001.959152
