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<p>A description is given of a thresholding algorithm that rapidly separates foreground objects from background clutter in images whose dominant feature is zero-mean Gaussian noise. Such images have been found to occur in digital radiography applications in which manufactured parts are imaged by a solid-state camera. The motivation behind the algorithm is discussed in terms of the requirements of an imaging system for nearly-real-time radiography in an industrial environment. The individual steps of the process are described, and the robustness of the technique with respect to signal-to-noise ratio and with respect to object size is discussed.</p>
image segmentation; Bayes method; Monte Carlo method; S/N ratio; picture processing; pattern recognition; Gaussian noise; background clutter; digital radiography; Bayes methods; Monte Carlo methods; pattern recognition; picture processing; radiography

Z. Bell, "A Bayesian/Monte Carlo Segmentation Method for Images Dominated by Gaussian Noise," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 11, no. , pp. 985-990, 1989.
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