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30 April 1992 Iterative Bayesian method for segmenting images that have undergone a gray-level degradation
Kurt R. Smith, Michael I. Miller
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Proceedings Volume 1611, Sensor Fusion IV: Control Paradigms and Data Structures; (1992) https://doi.org/10.1117/12.57964
Event: Robotics '91, 1991, Boston, MA, United States
Abstract
We extend the MRF image model commonly employed in the Bayesian development of image segmentation procedures to include a degradation channel resulting in a 2D hidden Markov model as the basis for the segmentation problem. We solve the segmentation problem by deriving the expectation-maximization algorithm for the case where the 'hidden' Markov source is the 2-D MRF that generates a true scene and the degradation channel is an additive, memoryless, grey-level degradation process that produces the observed scene.
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Kurt R. Smith and Michael I. Miller "Iterative Bayesian method for segmenting images that have undergone a gray-level degradation", Proc. SPIE 1611, Sensor Fusion IV: Control Paradigms and Data Structures, (30 April 1992); https://doi.org/10.1117/12.57964
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KEYWORDS
Image segmentation
Image processing
Expectation maximization algorithms
Image fusion
Composites
Sensor fusion
Algorithm development
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