Generating Disparity Image of Standing Trees Based on Improved SGM

Ping Yin; Aijun Xu; Jianxin Yin

doi:10.3788/LOP202259.1815017

Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1815017(2022)

Generating Disparity Image of Standing Trees Based on Improved SGM

Ping Yin^1,2,3, Aijun Xu^1,2,3, and Jianxin Yin^1,2,3、*

Author Affiliations

¹School of Mathematics and Computer Science, Zhejiang A & F University, Hangzhou 311300, Zhejiang , China

²Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology, Hangzhou 311300, Zhejiang , China

³Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Engineering, Zhejiang A & F University, Hangzhou 311300, Zhejiang , China

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Figures & Tables(13)

Fig. 1. Five species of tree

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Fig. 2. Flowchart of tree disparity image generation method

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Fig. 3. Median calculation

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Fig. 4. Cost aggregation. (a) Minimum path cost; (b) 8-path aggregation

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Fig. 5. Schematic of winner-take-all algorithm

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Fig. 6. Correction results of tree image. (a) Before correction; (b) after correction

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Fig. 7. Histogram equalization results before and after preprocessing. (a) Tree left image; (b) tree right image; (c) tree left image after preprocessing; (d) tree right image after preprocessing

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Fig. 8. Disparity images generated by different algorithms. (a) SGBM algorithm; (b) BM algorithm; (c) SGM algorithm; (d) proposed algorithm

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Fig. 9. Comparison between the disparity image generated by the proposed algorithm and the real disparity image. (a) Original left image; (b) real disparity image; (c) disparity image generated by the proposed algorithm

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Table 1. Algorithm parameters in this paper
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Table 1. Algorithm parameters in this paper
Parameter Census size P₁ P₂ r Maximum parallax value µ₁ µ₂ δ_d
Value 3×3 10 150 8 64 0.8 1 1

Table 2. Calibration results of binocular camera

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Table 2. Calibration results of binocular camera

Camera parameter	Left camera	Right camera
Focal length（f_x，f_y）	（1139.78060，1140.06362）	（1139.24564，1139.93787）
Optical center position coordinate	（679.74223，377.37711）	（683.14170，380.91245）
Distortion coefficient	（0.08655，-0.22816，0.00233，-0.00617，0）	（0.08655，-0.22816，0.00223，-0.00617，0）
R	［-0.01300，-0.00469，-0.00061］
T	［-60.73141，0.16710，0.42228］

Table 3. Matching cost test result
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Table 3. Matching cost test result
Algorithm False match rate /% Time /s
Traditional Census 14.19 4.7
Improve Census 11.23 4.9

Table 4. False match rate of different algorithms on Middlebury standard dataset

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Table 4. False match rate of different algorithms on Middlebury standard dataset

Algorithm	Tuskuba	Teddy	Cones	Venus	Average
SGBM	12.16	22.43	14.00	10.22	14.70
BM	14.23	21.45	10.93	11.69	14.575
SGM	12.78	19.46	15.61	8.92	14.19
Method in Ref.［11］	6.61	6.24	4.21	5.45	5.6275
Method in Ref.［16］	1.79	11.51	8.51	0.43	5.56
Method in Ref.［35］	3.25	15.7	9.76	2.83	7.89
Proposed algorithm	8.13	6.01	4.06	2.71	5.23

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Ping Yin, Aijun Xu, Jianxin Yin. Generating Disparity Image of Standing Trees Based on Improved SGM[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815017

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