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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