Abstract
We address modelling of stochastic image textures by Gibbs random fields with a translation invariant structure of multiple pairwise pixel interactions. The characteristic interaction structure and strengths (Gibbs potentials) are learnt from a given training sample by analytic and stochastic approximation of the unconditional or conditional maximum likelihood estimates of the potentials. The interaction structure is revealed by a model-based interaction map showing the relative contributions of each interaction to a total Gibbs energy. Features of the interaction maps are discussed and illustrated by experiments with various natural textures.
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Gimel'farb, G. (1998). Recovering image structure by model-based interaction map. In: Amin, A., Dori, D., Pudil, P., Freeman, H. (eds) Advances in Pattern Recognition. SSPR /SPR 1998. Lecture Notes in Computer Science, vol 1451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0033244
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DOI: https://doi.org/10.1007/BFb0033244
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