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A Bayesian/Monte Carlo segmentation method for images dominated by Gaussian noise

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1 Author(s)
Z. W. Bell ; Martin Marietta Energy Syst. Inc., Oak Ridge, TN, USA

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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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:11 ,  Issue: 9 )