A Model for Segmentation and Analysis of Noisy Images

Valen Johnson
Duke University

Nov 30 1990

I propose a statistical model for image generation that provides automatic segmentation of images into intensity-differentiated regions and facilitates the quantitative assessment of uncertainty associated with identified image features. The model is specified hierarchically within the Bayesian paradigm and at the lowest level in the hierarchy a Gibbs distribution is employed to specify a probability distribution on the space of all possible partitions of the discretized image scene. An important and novel feature of this distribution is that the number of partitioning element, or image regions, is not specified a priori. At higher levels in the hierarchical specification, random variables representing emission intensities are associated with regions and pixels. Observations are assumed generated from exponential family models centered about these values.


Bayesian inference, Gibbs distributions, image restoration, hierarchical models, random partitions, emission computed tomography, hexagonal arrays


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