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Bayesian Stereo Matching

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Cheng , Li; Caelli, Terry

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A Bayesian framework is proposed for stereo vision where solutions to both the model parameters and the disparity map are posed in terms of predictions of latent variables, given the observed stereo images. A mixed sampling and deterministic strategy is adopted to balance between effectiveness and efficiency: the parameters are estimated via Markov Chain Monte Carlo sampling techniques and the Maximum A Posteriori (MAP) disparity map is inferred by a deterministic approximation algorithm....[Show more]

dc.contributor.authorCheng , Li
dc.contributor.authorCaelli, Terry
dc.date.accessioned2015-12-08T22:08:22Z
dc.identifier.issn1077-3142
dc.identifier.urihttp://hdl.handle.net/1885/28589
dc.description.abstractA Bayesian framework is proposed for stereo vision where solutions to both the model parameters and the disparity map are posed in terms of predictions of latent variables, given the observed stereo images. A mixed sampling and deterministic strategy is adopted to balance between effectiveness and efficiency: the parameters are estimated via Markov Chain Monte Carlo sampling techniques and the Maximum A Posteriori (MAP) disparity map is inferred by a deterministic approximation algorithm. Additionally, a new method is provided to evaluate the partition function of the associated Markov random field model. Encouraging results are obtained on a standard set of stereo images as well as on synthetic forest images.
dc.publisherAcademic Press
dc.sourceComputer Vision and Image Understanding
dc.subjectKeywords: Algorithms; Bayesian networks; Markov processes; Monte Carlo methods; Generative model; Maximum A Posteriori (MAP) disparity map; Stereo images; Stereo vision Bayesian analysis; Generative model; Markov random field; Monte Carlo sampling; Stereo vision
dc.titleBayesian Stereo Matching
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume106
dc.date.issued2007
local.identifier.absfor080104 - Computer Vision
local.identifier.ariespublicationu3594520xPUB59
local.type.statusPublished Version
local.contributor.affiliationCheng , Li, College of Engineering and Computer Science, ANU
local.contributor.affiliationCaelli, Terry, College of Engineering and Computer Science, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.issue1
local.bibliographicCitation.startpage85
local.bibliographicCitation.lastpage96
local.identifier.doi10.1016/j.cviu.2005.09.009
dc.date.updated2015-12-08T07:16:59Z
local.identifier.scopusID2-s2.0-34047146525
CollectionsANU Research Publications

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