The variational approach to shape from shading
Computer Vision, Graphics, and Image Processing
Visual reconstruction
Parallel and Deterministic Algorithms from MRFs: Surface Reconstruction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Occlusions and binocular stereo
International Journal of Computer Vision
A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
A Bayesian approach to binocular stereopsis
International Journal of Computer Vision
International Journal of Computer Vision
A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
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
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
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
Occlusions, Discontinuities, and Epipolar Lines in Stereo
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Robust analysis of feature spaces: color image segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Improvements in Real-Time Correlation-Based Stereo Vision
SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Advances in Computational Stereo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image completion with structure propagation
ACM SIGGRAPH 2005 Papers
Efficient Dense Stereo with Occlusions for New View-Synthesis by Four-State Dynamic Programming
International Journal of Computer Vision
Approximate Bayesian Multibody Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating Optimal Parameters for MRF Stereo from a Single Image Pair
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic quantization for belief propagation in sparse spaces
Computer Vision and Image Understanding
Linear Programming Relaxations and Belief Propagation -- An Empirical Study
The Journal of Machine Learning Research
Multiview Stereo via Volumetric Graph-Cuts and Occlusion Robust Photo-Consistency
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian object extraction from uncalibrated image pairs
Image Communication
Reconstructing relief surfaces
Image and Vision Computing
Learning Probabilistic Models for Contour Completion in Natural Images
International Journal of Computer Vision
A Geometric Approach for Regularization of the Data Term in Stereo-Vision
Journal of Mathematical Imaging and Vision
Figure-ground segmentation using factor graphs
Image and Vision Computing
Bilayer representation for three dimensional visual communication
Journal of Visual Communication and Image Representation
Bisection approach for pixel labelling problem
Pattern Recognition
An energy minimisation approach to attributed graph regularisation
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
MRF-based stereo correspondence and virtual view interpolation
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
A globally optimal approach for 3D elastic motion estimation from stereo sequences
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Surfaces with occlusions from layered stereo
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Mixed color/level lines and their stereo- matching with a modified Hausdorff distance
Integrated Computer-Aided Engineering
Resource Allocation via Message Passing
INFORMS Journal on Computing
Dense motion and disparity estimation via loopy belief propagation
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Complex correlation statistic for dense stereoscopic matching
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Recovering depth map from video with moving objects
PSIVT'11 Proceedings of the 5th Pacific Rim conference on Advances in Image and Video Technology - Volume Part II
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
A pointwise smooth surface stereo reconstruction algorithm without correspondences
Image and Vision Computing
Resource Allocation via Message Passing
INFORMS Journal on Computing
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In this paper, we formulate the stereo matching problem as a Markov network consisting of three coupled Markov random fields (MRF's). These three MRF's model a smooth field for depth/disparity, a line process for depth discontinuity and a binary process for occlusion, respectively. After eliminating the line process and the binary process by introducing two robust functions, we obtain the maximum a posteriori (MAP) estimation in the Markov network by applying a Bayesian belief propagation (BP) algorithm. Furthermore, we extend our basic stereo model to incorporate other visual cues (e.g., image segmentation) that are not modeled in the three MRF's, and again obtain the MAP solution. Experimental results demonstrate that our method outperforms the state-of-art stereo algorithms for most test cases.