Parallel and deterministic algorithms from MRFs: surface reconstruction

Author

Geiger, Davi ; Girosi, Federico

Author_Institution

Artificial Intelligence Lab., MIT, Cambridge, MA, USA

Volume

13

Issue

5

fYear

1991

fDate

5/1/1991 12:00:00 AM

Firstpage

401

Lastpage

412

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;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.134040

Filename

134040