Interpreting images by propagating Bayesian Beliefs

Yair Weiss*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Scopus citations


A central theme of computational vision research has been the realization that reliable estimation of local scene properties requires propagating measurements across the image. Many authors have therefore suggested solving vision problems using architectures of locally connected units updating their activity in parallel. Unfortunately, the convergence of traditional relaxation methods on such architectures has proven to be excruciatingly slow and in general they do not guarantee that the stable point will be a global minimum. In this paper we show that an architecture in which Bayesian Beliefs about image properties are propagated between neighboring units yields convergence times which are several orders of magnitude faster than traditional methods and avoids local minima. In particular our architecture is non-iterative in the sense of Marr [5] : at every time step, the local estimates at a given location are optimal given the information which has already been propagated to that location. We illustrate the algorithm's performance on real images and compare it to several existing methods.

Original languageAmerican English
Title of host publicationAdvances in Neural Information Processing Systems 9 - Proceedings of the 1996 Conference, NIPS 1996
PublisherNeural information processing systems foundation
Number of pages7
ISBN (Print)0262100657, 9780262100656
StatePublished - 1997
Externally publishedYes
Event10th Annual Conference on Neural Information Processing Systems, NIPS 1996 - Denver, CO, United States
Duration: 2 Dec 19965 Dec 1996

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference10th Annual Conference on Neural Information Processing Systems, NIPS 1996
Country/TerritoryUnited States
CityDenver, CO


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