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
Adaptive Support-Weight Approach for Correspondence Search
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-based local stereo matching using orthogonal integral images
IEEE Transactions on Circuits and Systems for Video Technology
Segmentation-based adaptive support for accurate stereo correspondence
PSIVT'07 Proceedings of the 2nd Pacific Rim conference on Advances in image and video technology
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In this paper, we propose an efficient local stereo algorithm for accurate disparity estimation. First, we attain initial disparity estimates by iterating a cross-based cost aggregation process. Then, we propose a robust voting scheme to refine the initial estimates based on a piecewise smoothness prior, improving the quality in occluded regions and low-textured regions effectively. The refinement is guided by the segmentation result of input images. Unreliable initial estimates, which are detected using an efficient left-right consistency check, are rejected to increase the reliability of the voting results. Evaluated with the Middlebury stereo benchmark, our method is among the top performing local methods in accuracy. Compared to other local methods with similar accuracy, our method is faster by a factor of about two orders.