Xinjun Peng, Jun Han, Yong Tang, Yuzhi Shang, Yujin Yu. Anti-Noise Stereo Matching Algorithm Based on Improved Census Transform and Outlier Elimination[J]. Acta Optica Sinica, 2017, 37(11): 1115004
- Acta Optica Sinica
- Vol. 37, Issue 11, 1115004 (2017)
Fig. 1. (a) Adaptive window with noise; (b) part of the Teddy map added with 0.05% salt and pepper noise; (c) disparity map computed by traditional cross-based cost aggregation algorithm; (d) disparity map computed by proposed cost aggregation algorithm
Fig. 2. (a) Arm length of pixel p in different directions; (b) aggregation region S of the pixel p
Fig. 3. Influence of α and β on error matching rate in non-occlusion region. (a) α values; (b) β values
Fig. 4. Error matching rate of different algorithms under different noises. (a) Salt and pepper noise; (b) Gaussian noise
Fig. 5. Experimental results of the benchmark images. (a) Middlebury benchmark images; (b) Middlebury benchmark disparity maps; (c) disparity maps with proposed algorithm; (d) error maps with proposed algorithm
Fig. 6. Experimental results of the benchmark images. (a) Middlebury benchmark images; (b) Middlebury benchmark disparity maps; (c) disparity maps with proposed algorithm
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Table 1. Average error matching rate in non-occlusion region%
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Table 2. Average error matching rate of different algorithms in non-occlusion region%
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Table 3. Average matching accuracy with different PSNR values in non-occlusion area%
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Table 4. Error matching rate of different algorithms when the threshold is 1%
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