A Bayesian approach to binocular stereopsis
International Journal of Computer Vision
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic quantization for belief propagation in sparse spaces
Computer Vision and Image Understanding
Component Optimization for Image Understanding: A Bayesian Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guest Editorial: Generative model based vision
Computer Vision and Image Understanding
Dynamic quantization for belief propagation in sparse spaces
Computer Vision and Image Understanding
On the computational rationale for generative models
Computer Vision and Image Understanding
Efficient large-scale stereo matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
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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. 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.