Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-Tree Based Stereo Using Dynamic Programming Optimization
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Local stereo matching with adaptive support-weight, rank transform and disparity calibration
Pattern Recognition Letters
Fast Stereo Matching Algorithm Using Adaptive Window
ISIP '08 Proceedings of the 2008 International Symposiums on Information Processing
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense Two-Frame Stereo Correspondence by Self-organizing Neural Network
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Stereo matching using hierarchical belief propagation along ambiguity gradient
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Reliability analysis of the rank transform for stereo matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Window selection is the main challenge for local stereo matching methods based on the rank transform and it involves two aspects : the rank window selection and the match window selection. Most recent methods only focus on how to select the match window but pay little attention to the selection of the rank window. In this paper, we propose a novel matching method based on adaptive rank transform. Differing with the existing rank-based matching methods, the proposed method can deal with the rank and match window selection at the same time. The experimental results are evaluated on the Middlebury dataset as well as real images, showing that our method performs better than the recent rank-based stereo matching methods.