International Journal of Computer Vision
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching Using Belief Propagation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
An Iterative Optimization Approach for Unified Image Segmentation and Matting
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Consistent Segmentation for Optical Flow Estimation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
On the estimation of hyperparameters in Bayesian approach of solving inverse problems
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
A comparative study of energy minimization methods for markov random fields
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Approximate maximum likelihood hyperparameter estimation for Gibbs priors
IEEE Transactions on Image Processing
ML parameter estimation for Markov random fields with applications to Bayesian tomography
IEEE Transactions on Image Processing
Iterated conditional modes for inverse dithering
Signal Processing
Reliability-driven, spatially-adaptive regularization for deformable registration
WBIR'10 Proceedings of the 4th international conference on Biomedical image registration
Adaptive regularization parameter for graph cut segmentation
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
Occlusion filling in stereo: Theory and experiments
Computer Vision and Image Understanding
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This paper presents a novel approach for estimating the parameters for MRF-based stereo algorithms. This approach is based on a new formulation of stereo as a maximum a posterior (MAP) problem in which both a disparity map and MRF parameters are estimated from the stereo pair itself. We present an iterative algorithm for the MAP estimation that alternates between estimating the parameters while fixing the disparity map and estimating the disparity map while fixing the parameters. The estimated parameters include robust truncation thresholds for both data and neighborhood terms, as well as a regularization weight. The regularization weight can be either a constant for the whole image or spatially-varying, depending on local intensity gradients. In the latter case, the weights for intensity gradients are also estimated. Our approach works as a wrapper for existing stereo algorithms based on graph cuts or belief propagation, automatically tuning their parameters to improve performance without requiring the stereo code to be modified. Experiments demonstrate that our approach moves a baseline belief propagation stereo algorithm up six slots in the Middlebury rankings.