A Pixel Dissimilarity Measure That Is Insensitive to Image Sampling
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Match Propogation for Image-Based Modeling and Rendering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Stereo Matching Algorithm with an Adaptive Window: Theory and Experiment
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
Efficient Stereo with Multiple Windowing
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local stereo matching using geodesic support weights
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Real-time spatiotemporal stereo matching using the dual-cross-bilateral grid
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Fast variable window for stereo correspondence using integral images
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Fast cost-volume filtering for visual correspondence and beyond
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Global stereo matching leveraged by sparse ground control points
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Object stereo -- Joint stereo matching and object segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Secrets of adaptive support weight techniques for local stereo matching
Computer Vision and Image Understanding
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Stereo matching, the key problem in the field of computer vision has long been researched for decades. However, constructing an accurate dense disparity map is still very challenging for both local and global algorithms, especially when dealing with the occlusions and disparity discontinuities. In this paper, by exploring the characteristics of the color edges, a novel constraint named the global edge constraint (GEC) is proposed to discriminate the locations of potential occlusions and disparity discontinuities. The initial disparity map is estimated by using a local algorithm, in which the GEC could guarantee that the optimal support windows would not cross the occlusions. Then a global optimization framework is adopted to improve the accuracy of the disparity map. The data term of the energy function is constructed by using the reliable correspondences selected from the initial disparity map; and the smooth term incorporates the GEC as a soft constraint to handle the disparity discontinuities. Optimal solution can be approximated via existing energy minimization approaches such as Graph cuts used in this paper. Experimental results using the Middlebury Stereo test bed demonstrate the superior performance of the proposed approach.