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A Bayesian/Monte Carlo Segmentation Method for Images Dominated by Gaussian Noise
September 1989 (vol. 11 no. 9)
pp. 985-990

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.

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Index Terms:
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.W. Bell, "A Bayesian/Monte Carlo Segmentation Method for Images Dominated by Gaussian Noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 9, pp. 985-990, Sept. 1989, doi:10.1109/34.35502
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