SUMMARY In this paper, for a degraded two-colour or binary scene, we show how the image with maximum a posteriori (MAP) probability, the MAP estimate, can be evaluated exactly using efficient variants of the Ford–Fulkerson algorithm for finding the maximum flow in a certain capacitated network. Availability of exact estimates allows an assessment of the performance of simulated annealing and of MAP estimation itself in this restricted setting. Unfortunately, the simple network flow algorithm does not extend in any obvious way to multicolour scenes. However, the results of experiments on two-colour images suggest that, in general, simulated annealing, according to practicable ‘temperature’ schedules, can produce poor approximations to the MAP estimate to which it converges.
SUMMARY We propose a new algorithm for the approximation of the maximum a posteriori (MAP) restoration of noisy images. The image restoration problem is considered in a Bayesian...
We make an analogy between images and statistical mechanics systems. Pixel gray levels and the presence and orientation of edges are viewed as states of atoms or molecules in a ...
A maximum likelihood estimator based on the coalescent for unequal migration rates and different subpopulation sizes is developed. The method uses a Markov chain Monte Carlo app...
We present an unsupervised segmentation algorithm comprising an annealing process to select the maximum a posteriori (MAP) realization of a hierarchical Markov random field (MRF...
We describe a comprehensive framework for performing rapid and automatic nonlabel-based nonlinear spatial normalizations. The approach adopted minimizes the residual squared dif...
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