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Parallel and deterministic algorithms from MRFs: surface reconstruction

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2 Author(s)
D. Geiger ; Artificial Intelligence Lab., MIT, Cambridge, MA, USA ; F. Girosi

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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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:13 ,  Issue: 5 )