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棋牌游戏森林舞会官网平台

吴福培, 黄耿楠, 刘宇豪, 叶玮琳, 李昇平

吴福培, 黄耿楠, 刘宇豪, 叶玮琳, 李昇平. 棋牌游戏森林舞会官网平台[J]. 中国二八杠棋牌游戏(中英文), 2024, 17(2): 278-290. doi: 10.37188/CO.2023-0082
引用本文: 吴福培, 黄耿楠, 刘宇豪, 叶玮琳, 李昇平. 棋牌游戏森林舞会官网平台[J]. 中国二八杠棋牌游戏(中英文), 2024, 17(2): 278-290. doi: 10.37188/CO.2023-0082
WU Fu-pei, HUANG Geng-nan, LIU Yu-hao, YE Wei-lin, LI Sheng-ping. 棋牌游戏森林舞会注册开户[J]. Chinese Optics, 2024, 17(2): 278-290. doi: 10.37188/CO.2023-0082
Citation: WU Fu-pei, HUANG Geng-nan, LIU Yu-hao, YE Wei-lin, LI Sheng-ping. 棋牌游戏森林舞会注册开户[J]. Chinese Optics, 2024, 17(2): 278-290. doi: 10.37188/CO.2023-0082

棋牌游戏森林舞会官网平台

doi: 10.37188/CO.2023-0082
基金项目:国家自然科学基金(No. 61573233);广东省自然科学基金(No. 2021A1515010661);广东省普通高校重点领域专项(No. 2020ZDZX2005)
详细信息
    作者简介:

    吴福培(1980—),男,广西玉林人,博士,教授,2009年于华南理工大学机械工程专业获得博士学位,现就职于汕头大学机械工程系,主要研究方向:自动二八杠棋牌游戏检测和3D测量。E-mail:[email protected]

    黄耿楠(1997—),男,广东揭阳人,硕士, 2023年于汕头大学机械工程专业获得硕士学位,现就职于比亚迪有限公司,担任高级工程师。研究方向:双目视觉与三维测量。E-mail:[email protected]

    刘宇豪(1999—),男,重庆人,硕士研究生,主要研究方向:双目视觉与三维测量。E-mail: [email protected]

    叶玮琳(1984—),女,广东潮州人,副教授,博士,主要研究方向:光电传感技术。E-mail:[email protected]

    李昇平(1966—),男,湖南永州人,教授,博士,主要研究方向:自适应控制、机器视觉。E-mail:[email protected]

  • 中图分类号:TP391.4

棋牌游戏森林舞会注册开户

Funds:Supported by National Natural Science Foundation of China (No. 6l573233); Natural Science Foundation of Guangdong Province (No. 2021A1515010661); Special Projects in Key Areas of General Universities in Guangdong Province (No. 2020ZDZX2005)
More Information
  • 摘要:

    视差不连续区域和重复纹理区域的误匹配率高一直是影响双目立体匹配测量精度的主要问题,为此,本文提出一种基于多特征融合的立体匹配算法。首先,在代价计算阶段,通过高斯加权法赋予邻域像素点的权值,从而优化绝对差之和(Sum of Absolute Differences,SAD)算法的计算精度。接着,基于Census变换改进二进制链码方式,将邻域内像素的平均灰度值与梯度图像的灰度均值相融合,进而建立左右图像对应点的判断依据并优化其编码长度。然后,构建基于十字交叉法与改进的引导滤波器相融合的聚合方法,从而实现视差值再分配,以降低误匹配率。最后,通过赢家通吃(Winner Take All,WTA)算法获取初始视差,并采用左右一致性检测方法及亚像素法提高匹配精度,从而获取最终的视差结果。实验结果表明,在Middlebury数据集的测试中,所提SAD-Census算法的平均非遮挡区域和全部区域的误匹配率为分别为2.67%和5.69%,测量200~900 mm距离的平均误差小于2%;而实际三维测量的最大误差为1.5%。实验结果检验了所提算法的有效性和可靠性。

  • 图 1 改进Census变换示意图

    Figure 1. Schematic diagram of improved Census transform

    图 2 自适应十字交叉域

    Figure 2. Adaptive crossing domain

    图 3 优化聚合路径示意图

    Figure 3. Schematic diagram of optimized aggregation path

    图 4 3种代价计算方法的视差图。(a)传统SAD-Census算法;(b)传统SAD+改进Census算法;(c)本文算法

    Figure 4. Parallax maps of three cost calculation methods. (a) Traditional SAD-Census algorithms; (b) traditional SAD + improved Census algorithm; (a) the proposed algorithm

    图 5 4种算法的实验结果图

    Figure 5. Experimental results of 4 kinds of algorithms

    图 6 不同算法的误匹配率

    Figure 6. Mismatch rate of different algorithms

    图 7 不同亮度下几种算法的实验结果

    Figure 7. Experimental results of 4 kinds of algorithms at different brightness levels

    图 8 噪声干扰下各算法的实验结果

    Figure 8. Experimental results of different algorithms under noise interference

    图 9 测距实验对比结果

    Figure 9. Comparison results of distance measuring experiments

    图 10 样本视差结果图的边缘提取

    Figure 10. Edge extraction of sample parallax result maps

    图 11 量块视差图

    Figure 11. Gauge block parallax map

    表  1 实验参数设置

    Table  1. Experimental parameter setting

    Parameter Value Parameter Value
    б 2 λ 0.003
    τ 1 16 ε 0.001
    τ 2 4 ω 9×7
    L 1 30 β 1 0.6
    L 2 15 β 2 0.4
    δ 0 1
    下载: 导出CSV

    表  2 不同算法在非遮挡区域和全部区域的误匹配率

    Table  2. Mismatch rate of different algorithms in the non-occluded region and all regions (%)

    Algorithm
    Tsukuba

    Venus

    Teddy

    Cones
    Avg
    Non all Disc Non all Disc Non all Disc Non all Disc Non all Disc
    GC-occ 1.19 2.01 6.24 1.64 2.19 6.75 11.20 17.40 19.80 5.36 12.40 13.00 4.84 8.50 11.45
    SemiGlob 3.26 3.96 12.80 1.00 1.57 11.30 6.02 12.20 16.30 3.06 9.75 8.90 3.34 6.87 12.32
    RTCensus 5.08 6.25 19.20 1.58 2.42 14.20 7.96 13.8 20.30 4.10 9.54 12.20 4.68 8.01 16.48
    HCA 1.31 2.47 0.94 0.11 0.23 0.24 4.23 9.45 3.67 5.64 11.59 4.30 2.83 5.94 2.29
    Cross-ScaleGF 2.38 2.85 8.40 1.13 1.98 9.26 7.05 14.9 16.80 3.32 11.00 7.99 3.47 7.68 10.61
    GradAdaptWgt 2.26 2.63 8.99 0.99 1.39 4.92 8.00 13.10 18.60 2.61 7.67 7.43 3.46 6.19 9.99
    Proposed algorithm 1.25 2.73 0.90 0.15 0.31 0.31 4.62 8.73 2.98 4.69 10.98 4.11 2.67 5.69 2.08
    下载: 导出CSV

    表  3 三维测距实验结果

    Table  3. Experimental results of three-dimensional distance measurement

    序号 实际距离/mm 测量距离/mm 距离误差/mm 距离误差率/%
    1 200 198.42 −1.58 0.79
    2 300 302.55 2.55 0.85
    3 400 403.76 3.76 0.94
    4 500 495.05 −4.95 0.99
    5 600 606.06 6.06 1.01
    6 700 707.56 7.56 1.08
    7 800 809.52 9.52 1.19
    8 900 912.24 12.24 1.36
    下载: 导出CSV

    表  4 测量样本实验结果

    Table  4. Sample experimental results

    样本序号 实际长度/mm 测量长度/mm 误差/mm 误差率/%
    1 20 19.9575 −0.0425 −0.21
    35 34.6144 −0.3856 −1.1
    2 35 35.1649 0.1649 −0.47
    50 49.9672 −0.0328 −0.06
    3 5 4.9439 −0.0561 −1.12
    15 14.7565 −0.2435 −1.62
    4 20 20.3541 0.3541 0.71
    30 29.5784 −0.4216 −1.41
    下载: 导出CSV

    表  5 厚度测量结果

    Table  5. Thickness measurement results

    序号 实际厚度/mm 测量厚度/mm 测量误差/mm 绝对误差率/%
    1 1.00 1.0105 0.0105 1.05
    2 1.10 1.1235 0.0235 2.13
    3 1.20 1.2335 0.0335 2.79
    4 1.50 1.5563 0.0563 3.75
    5 2.00 1.9346 −0.0654 3.27
    下载: 导出CSV
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  • 收稿日期: 2023-05-08
  • 修回日期: 2023-06-02
  • 网络出版日期: 2023-09-15

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