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Texture-based segmentation using markov random field models and approximate Bayesian estimators based on trees

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Abstract

We describe segmentation based on textures using the label and image model of D. Gemanet al., “Boundary Detection by Constrained Optimization,”IEEE Trans. Pattern Analysis and Machine Intelligence, 12(7):609–628, July 1990. We replace their maximuma posteriori estimation criterion with a Bayesian estimator that minimizes the sum of the pixel misclassification probabilities. The new estimation goal allows the use of a different computational algorithm, which is deterministic rather than random, based on approximating lattices by trees. An example demonstrating an accurate segmentation of a collage of Brodatz textures is included.

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This work was supported by the Purdue Research Foundation, a Whirlpool Faculty Fellowship, U. S. National Science Foundation grant MIP-9110919, and the School of Electrical Engineering, Purdue University.

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Wu, CH., Doerschuk, P.C. Texture-based segmentation using markov random field models and approximate Bayesian estimators based on trees. J Math Imaging Vis 5, 277–286 (1995). https://doi.org/10.1007/BF01250284

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