Non-parametric local transforms for computing visual correspondence
ECCV '94 Proceedings of the third European conference on Computer Vision (Vol. II)
Ordinal Measures for Image Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching with Nonlinear Diffusion
International Journal of Computer Vision
International Journal of Computer Vision
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
A Cooperative Algorithm for Stereo Matching and Occlusion Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Correspondence with Compact Windows via Minimum Ratio Cycle
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical Stochastic Diffusion for Disparity Estimation
SMBV '01 Proceedings of the IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV'01)
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stereo Matching with Symmetric Cost Functions
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
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In stereo matching, homogeneous areas, depth continuity areas, and occluded areas need more attention. Many methods try to handle pixels in homogeneous areas by propagating supports. As a result, pixels in homogeneous areas get assigned disparities inferred from the disparities of neighboring pixels. However, at the same time, pixels in depth discontinuity areas get supports from different depths and/or from occluded pixels, and resultant disparity maps are easy to be blurred. To resolve this problem, we propose a non-linear diffusion-based support aggregation method. Supports are iteratively aggregated with the support-weights, while adjusting the support-weights according to disparities to prevent incorrect supports from different depths and/or occluded pixels. As a result, the proposed method yields good results not only in homogeneous areas but also in depth discontinuity areas as the iteration goes on without the critical degradation of performance.