Abstract
A prior probability model is adapted to a class of images by identification, or parameter estimation from training data. We propose a new and accurate analytical identification of a generic Markov-Gibbs random field (MGRF) model with multiple pairwise interaction and use it for structural analysis and synthesis of textures.
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Gimel’farb, G., Zhou, D. (2007). Accurate Identification of a Markov-Gibbs Model for Texture Synthesis by Bunch Sampling. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_121
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DOI: https://doi.org/10.1007/978-3-540-74272-2_121
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