Abstract
Two models are given for the extraction of boundaries in digital images, one for discriminating textures and the other for discriminating objects. In both cases a Markov random field is constructed as a prior distribution over intensities (observed) and labels (unobserved); the labels are either the texture types or boundary indicators. The posterior distribution, i.e., the conditional distribution over the labels given the intensities, is then analyzed by a Monte-Carlo algorithm called stochastic relaxation. The final labeling corresponds to a local maximum of the posterior likelihood.
Research partially supported by Office of Naval Research contract N00014-86-K-0027 and National Science Foundation grant DMS-8401927.
Research partially supported by Office of Naval Research contract N00014-86-0037, Army Research Office contract DAAG29-83-K-0116, and National Science Foundation grant DMS-8352087.
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Geman, D., Geman, S., Graffigne, C. (1987). Locating Texture and Object Boundaries. In: Devijver, P.A., Kittler, J. (eds) Pattern Recognition Theory and Applications. NATO ASI Series, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-83069-3_14
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