A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Binocular Half-Occlusions: Empirical Comparisons of Five Approaches
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding the Largest Unambiguous Component of Stereo Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
A Progressive Scheme for Stereo Matching
SMILE '00 Revised Papers from Second European Workshop on 3D Structure from Multiple Images of Large-Scale Environments
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Colorization using optimization
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Unambiguous Stereo Matching Using Reliability-Based Dynamic Programming
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locally Adaptive Support-Weight Approach for Visual Correspondence Search
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
High-Quality Real-Time Stereo Using Adaptive Cost Aggregation and Dynamic Programming
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Extracting dense features for visual correspondence with graph cuts
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
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
Fast multi-labelling for stereo matching
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Efficient large-scale stereo matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Continuous markov random fields for robust stereo estimation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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We present an algorithm to reduce per-pixel search ranges for Markov Random Fields-based stereo algorithms. Our algorithm is based on the intuitions that reliably matched pixels need less regularization in the energy minimization and neighboring pixels should have similar disparity search ranges if their pixel values are similar. We propose a novel bi-labeling process to classify reliable and unreliable pixels that incorporate left-right consistency checks. We then propagate the reliable disparities into unreliable regions to form a complete disparity map and construct per-pixel search ranges based on the difference between the disparity map after propagation and the one computed from a winner-take-all method. Experimental results evaluated on the Middlebury stereo benchmark show our proposed algorithm is able to achieve 77% average reduction rate while preserving satisfactory accuracy.