Bayesian stereo matching

Li Cheng; Terry Caelli

doi:10.1016/j.cviu.2005.09.009

Bayesian stereo matching

Li Cheng^*, Terry Caelli

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

Abstract

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. 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.

Original language	English
Pages (from-to)	85-96
Number of pages	12
Journal	Computer Vision and Image Understanding
Volume	106
Issue number	1
DOIs	https://doi.org/10.1016/j.cviu.2005.09.009
Publication status	Published - Apr 2007

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10.1016/j.cviu.2005.09.009

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keywords = "Bayesian analysis, Generative model, Markov random field, Monte Carlo sampling, Stereo vision",

author = "Li Cheng and Terry Caelli",

year = "2007",

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AB - 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. 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.

KW - Bayesian analysis

KW - Generative model

KW - Markov random field

KW - Monte Carlo sampling

KW - Stereo vision

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Bayesian stereo matching

Abstract

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