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
Summed-area tables for texture mapping
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Fast normalized cross correlation for defect detection
Pattern Recognition Letters
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
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Normalized Cross-Correlation (NCC) is a common matching technique to tolerate radiometric differences between stereo images. However, traditional rectangle-based NCC tends to blur the depth discontinuities. This paper proposes an efficient stereo algorithm with NCC over shape-adaptive matching regions, producing depth-discontinuity preserving disparity maps while remaining the advantage of robustness to radiometric differences. To alleviate the computational intensity, we propose an acceleration algorithm using an orthogonal integral image technique, achieving a speedup factor of 10-27. In addition, a voting scheme on reliable estimates is applied to refine the initial estimates. Experiments show that, besides the robustness, the proposed method obtains accurate disparity maps at fast speed. Our method highly ranks among the local approaches in the Middlebury stereo benchmark.