Parallel and deterministic algorithms from MRFs: surface reconstruction

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 13; no. 5; pp. 401 - 412
• Main Authors: Geiger, D., Girosi, F.
• Format: Journal Article
• Language: English
• Published: Los Alamitos, CA IEEE 01-05-1991; IEEE Computer Society
• Subjects: Applied sciences; Artificial intelligence; Bayesian methods; Color; Computational modeling; Computer science; control theory; systems; Equations; Exact sciences and technology; Image reconstruction; Layout; Markov random fields; Parameter estimation; Pattern recognition. Digital image processing. Computational geometry; Probability distribution; Surface reconstruction; Parallel algorithm; Segmentation; Image processing; Statistical model; Image reconstruction; Image enhancement
• ISSN: 0162-8828; 1939-3539
• DOI: 10.1109/34.134040

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.< >