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Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
August 1989 (vol. 11 no. 8)
pp. 799-813
An adaptive segmentation algorithm is developed which simultaneously estimates the parameters of the underlying Gibbs random field (GRF)and segments the noisy image corrupted by additive independent Gaussian noise. The algorithm, which aims at obtaining the maximum a posteriori (MAP) segmentation is a simulated annealing algorithm that is interrupted at regular intervals for estimating the GRF parameters. Maximum-likelihood (ML) estimates of the parameters based on the current segmentation are used to obtain the next segmentation. It is proven that the parameter estimates and the segmentations converge in distribution to the ML estimate of the parameters and the MAP segmentation with those parameter estimates, respectively. Due to computational difficulties, however, only an approximate version of the algorithm is implemented. The approximate algorithm is applied on several two- and four-region images with different noise levels and with first-order and second-order neighborhoods.
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Index Terms:
maximum likelihood estimates; picture processing; pattern recognition; optimisation; parameter estimation; Gibbs random fields; simulated annealing; adaptive segmentation; Gaussian noise; optimisation; parameter estimation; pattern recognition; picture processing; probability
Citation:
S. Lakshmanan, H. Derin, "Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 8, pp. 799-813, Aug. 1989, doi:10.1109/34.31443
