Abstract
The problem of image segmentation can be formulated in the framework of Bayesian statistics. We use a Markov random field as the prior model of the spacial relationship between image pixels, and approximate an observed image by a Gaussian mixture model. In this paper, we introduce into the statistical model a hierarchical prior structure from which model parameters are regarded as drawn. This would give an efficient Gibbs sampler for exploring the joint posterior distribution of all parameters given an observed image and could make the estimation more robust.
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© 2005 Springer-Verlag Berlin Heidelberg
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Aoki, K., Nagahashi, H. (2005). Bayesian Image Segmentation Using MRF’s Combined with Hierarchical Prior Models. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds) Image Analysis. SCIA 2005. Lecture Notes in Computer Science, vol 3540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499145_8
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DOI: https://doi.org/10.1007/11499145_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26320-3
Online ISBN: 978-3-540-31566-7
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