A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to Variational Methods for Graphical Models
Machine Learning
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
The Journal of Machine Learning Research
Symmetric Stereo Matching for Occlusion Handling
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
International Journal of Computer Vision
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Using Conditional Random Fields for Decision-Theoretic Planning
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Structured Learning and Prediction in Computer Vision
Foundations and Trends® in Computer Graphics and Vision
On Learning Conditional Random Fields for Stereo
International Journal of Computer Vision
Machine Vision and Applications
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As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate parameters in Markov Random Field (MRF) based stereo formulations, one usually needs to perform approximate probabilistic inference. Message passing algorithms based on variational methods and belief propagation are widely used for approximate inference in MRFs. Conditional Random Fields (CRFs) are discriminative versions of traditional MRFs and have recently been applied to the problem of stereo vision. However, CRF parameter training typically requires expensive inference steps for each iteration of optimization. Inference is particularly slow when there are many discrete disparity levels, due to high state space cardinality. We present a novel CRF for stereo matching with an explicit occlusion model and propose sparse message passing to dramatically accelerate the approximate inference needed for parameter optimization. We show that sparse variational message passing iteratively minimizes the KL divergence between the approximation and model distributions by optimizing a lower bound on the partition function. Our experimental results show reductions in inference time of one order of magnitude with no loss in approximation quality. Learning using sparse variational message passing improves results over prior work using graph cuts.