A Variable Window Approach to Early Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
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 Matching with Segmentation-Based Cooperation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
What Energy Functions Can Be Minimized via Graph Cuts?
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient graph-based energy minimization methods in computer vision
Efficient graph-based energy minimization methods in computer vision
A Symmetric Patch-Based Correspondence Model for Occlusion Handling
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Belief Propagation for Early Vision
International Journal of Computer Vision
Surface Geometric Constraints for Stereo in Belief Propagation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Segment-Based Stereo Matching Using Belief Propagation and a Self-Adapting Dissimilarity Measure
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
A Performance Study on Different Cost Aggregation Approaches Used in Real-Time Stereo Matching
International Journal of Computer Vision
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Stereo for slanted surfaces: first order disparities and normal consistency
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Depth sculpturing for 2D paintings: A progressive depth map completion framework
Journal of Visual Communication and Image Representation
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In the paper, the algorithm of segment-based stereo matching using graph cuts is developed for extracting depth information from the stereo image pairs. The first step of the algorithm employs the mean-shift algorithm to segment the reference image, which ensures our method to correctly estimate in large untextured regions and precisely localize depth boundaries, followed by the use of Adaptive Support Weighted Self-Adaptation dissimilarity algorithm (ASW-SelfAd) for the estimation of initial disparity. This is followed by application of Singular Value Decomposition (SVD) in solving the robust disparity plane fitting. In order to ensure reliable pixel sets for the segment, we filter out outliers which contain occlusion region through three main rules, namely; cross-checking, judging reliable area and disparity distance measurement. Lastly, we apply improved clustering algorithm to merge the neighboring segments. The geometrical relationship of adjacent planes such as parallelism and intersection is employed for determination of whether two planes shall be merged. A new energy function is subsequently formulated with the use of graph cuts for the refinement of the disparity map. Finally, the depth information is extracted from the final disparity map. Experimental results on the Middlebury dataset demonstrate that our approach is effective in improving the state of the art.