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基于模糊形状上下文与局部向量相似性约束的配准算法

马新科 杨扬 杨昆 罗毅

马新科, 杨扬, 杨昆, 罗毅. 基于模糊形状上下文与局部向量相似性约束的配准算法. 自动化学报, 2020, 46(2): 342-357. doi: 10.16383/j.aas.c180118
引用本文: 马新科, 杨扬, 杨昆, 罗毅. 基于模糊形状上下文与局部向量相似性约束的配准算法. 自动化学报, 2020, 46(2): 342-357. doi: 10.16383/j.aas.c180118
MA Xin-Ke, YANG Yang, YANG Kun, LUO Yi. Registration Algorithm Based on Fuzzy Shape Context and Local Vector Similarity Constraint. ACTA AUTOMATICA SINICA, 2020, 46(2): 342-357. doi: 10.16383/j.aas.c180118
Citation: MA Xin-Ke, YANG Yang, YANG Kun, LUO Yi. Registration Algorithm Based on Fuzzy Shape Context and Local Vector Similarity Constraint. ACTA AUTOMATICA SINICA, 2020, 46(2): 342-357. doi: 10.16383/j.aas.c180118

基于模糊形状上下文与局部向量相似性约束的配准算法

doi: 10.16383/j.aas.c180118
基金项目: 

国家自然科学基金项目 41661080

云南省万人计划青年拔尖人才, 云南省大学生创新创业训练计划项目 61

详细信息
    作者简介:

    马新科  云南师范大学信息学院硕士研究生.主要研究方向为计算机视觉与模式识别.E-mail: maxxk@foxmail.com

    杨昆  云南师范大学信息学院教授.1998年获得澳大利亚新南威尔士大学硕士学位.主要研究方向为地理信息系统, 遥感图像处理.E-mail: kmdcynu@163.com

    罗毅  云南师范大学信息学院副教授.2014年获得哈尔滨理工大学博士学位.主要研究方向为无线传感器网络, 微弱信号拾取.E-mail: luoyi861030@163.com

    通讯作者:

    杨扬  云南师范大学信息学院教授.2007年获得日本早稻田大学硕士学位, 2013年获得新加坡国立大学NGS博士学位.主要研究方向为计算机视觉、遥感图像处理等.本文通信作者.E-mail: yyang_ynu@163.com

Registration Algorithm Based on Fuzzy Shape Context and Local Vector Similarity Constraint

Funds: 

National Natural Science Foundation of China 41661080

Yunnan Ten-thousand Talents Program, Program Project for College Students0 Innovation and Entrepreneurship Training of Yunnan Province 61

More Information
    Author Bio:

    MA Xin-Ke  Master student at the School of Information Science and Technology, Yunnan Normal University. His research interest covers computer vision and pattern recognition

    YANG Kun  Professor at the School of Information Science and Technology, Yunnan Normal University. He recieved his master degree form the University of New South Wales, Australia in 1998. His research interest covers geographic information system and remote sensing image processing

    LUO Yi  Associate professor at the School of Information Science and Technology, Yunnan Normal University. He received his Ph. D. degree from Harbin University of Science and Technology in 2014. His research interest covers wireless sensor networks, weak signal detection

    Corresponding author: YANG Yang  Professor at the School of Information Science and Technology, Yunnan Normal University. He received his master degree from Waseda University, Japan in 2007. He received his Ph. D. degree from National University of Singapore in 2013. His research interest covers computer vision and remote sensing image processing. Corresponding author of this paper
  • 摘要: 非刚性点集配准研究是模式识别领域的一项重要基础研究.本文在当前流行的非刚性点集配准算法的基础上提出了两个主要贡献: 1)模糊形状上下文(Fuzzy shape context, FSC)特征; 2)基于局部向量特征的局部空间向量相似性约束项.本文首先进行基于特征互补的对应关系评估, 在这一步骤中定义了模糊形状上下文特征, 然后基于模糊形状上下文特征差异和全局特征差异设计了特征互补的高斯混合模型.其次, 进行基于约束互补的空间变化更新.在这一步骤中, 定义了局部向量特征, 建立了局部空间向量相似性约束项.本文算法通过使用特征互补的高斯混合模型进行对应关系评估, 并将配准问题转化为可以用期望最大化(Expectation maximization, EM)算法解决的参数优化问题, 通过创建包含局部空间向量相似性约束项的能量方程优化了空间变换更新.本文首先测试了模糊形状上下文特征的检索率, 然后采用公开数据集测试了算法在点集配准与图像配准的性能.在与当前流行的十种算法的对比实验中, 本文算法均给出了精确的配准结果, 并在大部分实验中精度超过了当前流行算法.
    Recommended by Associate Editor BAI Xiang
    1)  本文责任编委 白翔
  • 图  1  p的SC特征图示

    Fig.  1  SC feature diagram is showed

    图  2  模糊形状上下文特征与 SC 特征的配准效果对比

    Fig.  2  Comparison of the registration examples between fuzzy shape context features and SC features

    图  3  特征互补的高斯混合模型与单特征高斯混合模型的配准效果对比

    Fig.  3  Comparison of the the registration examples between Gaussian mixture model of feature complementary and single feature Gaussian mixture model

    图  4  速度场图解

    Fig.  4  Velocity field diagram field

    图  5  约束互补的空间变化方程与单约束的配准效果对比

    Fig.  5  Comparison of the registration examples between constraint complementary spatial variation equations and single constraint spatial variation equations

    图  6  MPEG-7数据集例子

    Fig.  6  Examples of MPEG-7 dataset

    图  7  “apple”轮廓点集提取

    Fig.  7  Contour point extraction of apple template

    图  8  人造轮廓点集的三种不同配准模式下, 本文算法的六种参数取值的配准结果(其中第一列至第三列分别是点集Line[18]、Fish1[18]、Chinese character[18]的配准结果; 第一行至第三行分别是: 1)旋转变化加固定级别形变处理、2)旋转变化加固定级别噪音与形变处理、3)旋转变化加固定级别冗余点与形变处理)

    Fig.  8  The results of point set registration using the six parameter settings under the three different registration patterns (The first to third columns are the registration results of the point set Line[18], Fish1[18], and Chinese character[18], respectively. The first to third columns are 1) rotations + fixed deformation, 2) rotations + fixed noise and deformation, and 3) rotations + fixed outliers and deformation, respectively)

    图  9  人造轮廓点集的四种不同配准模式下, 本文算法与TPS-RPM[18]、CPD[2021]、GMMREG[22]、RPM-L2E[23], GLMD[24]、PR-GLS[26]的比较实验结果.第一行至第四行分别是1)形变变化、2)噪音变化+ 4级固定形变、3)冗余点变化+ 4级固定形变、4)旋转变化+ 4级固定形变

    Fig.  9  Comparison results of the proposed method, TPS-RPM[18], CPD[20, 21], GMMREG[22], RPM-L2E[23], GLMD[24] and PR-GLS[26] under four different registration patterns. The first to fourth rows are 1) deformation, 2) noises + fixed 4 level deformation, 3) outliers + fixed 4 level deformation and 4) rotations + 4 level deformation, respectively

    图  10  点集Line[18]的配准实例(第一列是配准前, 第二列是配准后.第一行至第四行分别是四种非刚性配准模式: 8级形变处理、4级噪音处理+ 4级形变处理、4级冗余点处理+ 4级形变处理、13级旋转处理+ 4级形变处理.)

    Fig.  10  Registration example of Line[18] point set (The first to fourth rows are four different types of non-rigid registration patterns: 8 level deformation, 4 level noise + 4 level deformation, 4 level outliers + 4 level deformation, 13 level rotation + 4 level deformation.)

    图  11  点集Fish1[18]的配准实例(第一列是配准前, 第二列是配准后.第一行至第四行分别是四种非刚性配准模式: 8级形变处理、4级噪音处理+ 4级形变处理、4级冗余点处理+ 4级形变处理、13级旋转处理+ 4级形变处理.)

    Fig.  11  Registration example of Fish1[18] point set (The first to fourth rows are four different types of non-rigid registration patterns: 8 level deformation, 4 level noise + 4 level deformation, 4 level outliers + 4 level deformation, 13 level rotation + 4 level deformation.)

    图  12  点集Chinese character[18]的配准实例(第一列是配准前, 第二列是配准后.第一行至第四行分别是四种非刚性配准模式: 8级形变处理、4级噪音处理+ 4级形变处理、4级冗余点处理+ 4级形变处理、13级旋转处理+ 4级形变处理.)

    Fig.  12  Registration example of Chinese character[18] point set (The first to fourth rows are four different types of non-rigid registration patterns: 8 level deformation, 4 level noise + 4 level deformation, 4 level outliers + 4 level deformation, 13 level rotation + 4 level deformation.)

    图  13  汽车真实图像配准实例(取自Pascal 2007 challenge数据库[45]的第18对汽车图像, 图中连线代表特征点之间的对应关系.)

    Fig.  13  Registration example of automotive real image (The 18th pair of car images are selected from the Pascal 2007 challenge database[45]. The lines represent the correspondence of the feature points in the figure.)

    图  14  摩托车真实图像配准实例(取自Pascal 2007 challenge数据库[45]的第4对摩托车图像, 图中连线代表特征点之间的对应关系.)

    Fig.  14  Registration example of motorbike real image (The 4th pair of car images are selected from the Pascal 2007 challenge database[45]. The lines represent the correspondence of feature points in the figure.)

    图  15  CMU hotel序列图像配准实例(CMU hotel序列图像的第1张与第100张的配准结果, 图中连线代表特征点之间的对应关系.)

    Fig.  15  Registration example of CMU hotel sequence image (Registration results of CMU hotel sequence images of 1st and 100th, the lines represent the correspondence of feature points in the figure.)

    图  16  CMU house序列图像配准实例(CMU house序列图像的第1张与第111张的配准结果, 图中连线代表特征点之间的对应关系.)

    Fig.  16  Registration example of CMU house sequence image (Registration results of CMU house sequence images of 1st and 100th, the lines represent the correspondence of feature points in the figure.)

    图  17  本文算法与CPD[21]、TPS-RPM[18]、GMMREG[22]、PR-GLS[26]、RPM-L2E[23]、GLMD[24]六种算法的图像配准实例

    Fig.  17  Comparison examples of our method, CPD[21], TPS-RPM[18], GMMREG[22], PR-GLS[26], RPM-L2E[23] and GLMD[24]

    表  1  SC、Latecki等、Mokhtarian等和FSC的检索率

    Table  1  Retrieval rate of shape context, Latecki et al., Mokhtarianet et al. and fuzzy shape

    SC [32] Latecki等[50] Mokhtarian等[51-52] FSC
    76.51 % 76.45 % 75.44 % 78.93 %
    下载: 导出CSV

    表  2  FGM[49]、Leordeanu等[45]、GLMD[24]、本文算法在汽车与摩托车真实图像中的平均配准

    Table  2  Matching rates on cars and motorbikes for our method, FGM[49], Leordeanu et al.[45] and GLMD[24]

    FGM Leordeanu等 GLMD 本文算法
    80 % 80 % 93 % 97 %
    下载: 导出CSV

    表  3  CMU hotel和CMU house序列图像的平均配准率

    Table  3  The average registration rate of CMU hotel and CMU house sequence images

    算法名称 CMU hotel CMU house
    GMMREG [22] 97.1 % 99.5 %
    Torresani等[48] 100 %
    FGM [49] 100 %
    GLMD [24] 100 %
    Leordeanu等[45] 94.8 % 99.8 %
    Caetano等[47] <90 % <96 %
    本文算法 99.6 % 100 %
    下载: 导出CSV

    表  4  “boat”图像配准结果的误差展示

    Table  4  Registration error of boat image

    算法 RMSE MAE MEE
    CPD 1.4513 1.8151 0.2486
    GMMREG 12.0899 4.2977 16.513
    RPM-L2E 1.9918 0.8515 2.4792
    TPS-RPM 42.3288 14.9059 53.4349
    GLMD 1.6392 2.0807 0.495
    PR-GLS 1.9291 0.8609 2.0807
    Ours 1.0799 0.8523 0.134
    下载: 导出CSV

    表  5  “graf”图像配准结果的误差展示

    Table  5  Registration error of graf image

    算法 RMSE MAE MEE
    CPD 74.9729 94.3400 24.8849
    GMMREG 41.6075 53.4556 17.0273
    RPM-L2E 47.7672 64.1356 22.5171
    TPS-RPM 97.4394 125.3333 21.2458
    GLMD 38.2704 48.5050 17.4508
    PR-GLS 75.0847 90.4167 46.5821
    Ours 1.5312 1.7801 0.5905
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-03-02
  • 录用日期:  2018-07-23
  • 刊出日期:  2020-03-06

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