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Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
May 1991 (vol. 13 no. 5)
pp. 401-412

Deterministic approximations to Markov random field (MRF) models are derived. One of the models is shown to give in a natural way the graduated nonconvexity (GNC) algorithm proposed by A. Blake and A. Zisserman (1987). This model can be applied to smooth a field preserving its discontinuities. A class of more complex models is then proposed in order to deal with a variety of vision problems. All the theoretical results are obtained in the framework of statistical mechanics and mean field techniques. A parallel, iterative algorithm to solve the deterministic equations of the two models is presented, together with some experiments on synthetic and real images.

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Index Terms:
parallel algorithms; picture processing; Markov random field model; deterministic algorithms; surface reconstruction; statistical mechanics; mean field techniques; iterative algorithm; iterative methods; Markov processes; parallel algorithms; picture processing; statistical analysis
D. Geiger, F. Girosi, "Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 5, pp. 401-412, May 1991, doi:10.1109/34.134040
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