Title :
Parallel and deterministic algorithms from MRFs: surface reconstruction
Author :
Geiger, Davi ; Girosi, Federico
Author_Institution :
Artificial Intelligence Lab., MIT, Cambridge, MA, USA
fDate :
5/1/1991 12:00:00 AM
Abstract :
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
Keywords :
Markov processes; iterative methods; parallel algorithms; picture processing; statistical analysis; Markov random field model; deterministic algorithms; iterative algorithm; mean field techniques; parallel algorithms; picture processing; statistical mechanics; surface reconstruction; Bayesian methods; Color; Computational modeling; Equations; Image reconstruction; Layout; Markov random fields; Parameter estimation; Probability distribution; Surface reconstruction;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
