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Zoltan Kato:
Markov random fields in image segmentation
Regions in real images are often homogeneous, neighboring pixels usually have similar properties (intensity, color, texture, ...). Markov Random Fields (MRF) are often used to capture such contextual constraints in a probabilistic framework. MRFs are well studied with a strong theoretical background hence providing a tool for rigorous and concise image modeling. Furthermore, they allow Markov Chain Monte Carlo (MCMC) sampling of the (hidden) underlying structure which greatly simplifies inference and parameter estimation. In this talk, we will give a short yet complete introduction to MRF image modelization by explaining how to construct a minimalistic model, how to estimate model parameters, and then how to infer the most likely segmentation of an image.

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July 8, 2011 12:22 PM

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