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<p><b>Abstract</b>—We present a class of models, derived from classical discrete Markov random fields, that may be used for the solution of ill-posed problems in image processing and in computational vision. They lead to reconstrucion algorithms that are flexible, computationally efficient, and biologically plausible. To illustrate their use, we present their application to the reconstruction of the dominant orientation and direction fields, to the classification of multiband images, and to image quantization and filtering.</p>
Bayes methods, estimation theory, Gaussian distributions, image classification, image segmentation, Markov processes, probability, simulated annealing.

M. Rivera, F. A. Velasco, J. L. Marroquin and M. Nakamura, "Gauss-Markov Measure Field Models for Low-Level Vision," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 23, no. , pp. 337-348, 2001.
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