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A Bayesian Segmentation Methodology for Parametric Image Models
February 1995 (vol. 17 no. 2)
pp. 211-217

Abstract—Region-based image segmentation methods require some criterion for determining when to merge regions. This paper presents a novel approach by introducing a Bayesian probability of homogeneity in a general statistical context. Our approach does not require parameter estimation and is therefore particularly beneficial for cases in which estimation-based methods are most prone to error: when little information is contained in some of the regions and, therefore, parameter estimates are unreliable. We apply this formulation to three distinct parametric model families that have been used in past segmentation schemes: implicit polynomial surfaces, parametric polynomial surfaces, and Gaussian Markov random fields. We present results on a variety of real range and intensity images.

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Index Terms:
Statistical image segmentation, Bayesian methods, likelihoods, Bayes factor, range images, Markov random field, texture segmentation.
Citation:
Steven M. LaValle, Seth A. Hutchinson, "A Bayesian Segmentation Methodology for Parametric Image Models," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, no. 2, pp. 211-217, Feb. 1995, doi:10.1109/34.368166
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