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基于混合特征的非刚性点阵配准算法

汤昊林 杨扬 杨昆 罗毅 张雅莹 张芳瑜

汤昊林, 杨扬, 杨昆, 罗毅, 张雅莹, 张芳瑜. 基于混合特征的非刚性点阵配准算法. 自动化学报, 2016, 42(11): 1732-1743. doi: 10.16383/j.aas.2016.c150618
引用本文: 汤昊林, 杨扬, 杨昆, 罗毅, 张雅莹, 张芳瑜. 基于混合特征的非刚性点阵配准算法. 自动化学报, 2016, 42(11): 1732-1743. doi: 10.16383/j.aas.2016.c150618
TANG Hao-Lin, YANG Yang, YANG Kun, LUO Yi, ZHANG Ya-Ying, ZHANG Fang-Yu. Non-rigid Point Set Registration with Mixed Features. ACTA AUTOMATICA SINICA, 2016, 42(11): 1732-1743. doi: 10.16383/j.aas.2016.c150618
Citation: TANG Hao-Lin, YANG Yang, YANG Kun, LUO Yi, ZHANG Ya-Ying, ZHANG Fang-Yu. Non-rigid Point Set Registration with Mixed Features. ACTA AUTOMATICA SINICA, 2016, 42(11): 1732-1743. doi: 10.16383/j.aas.2016.c150618

基于混合特征的非刚性点阵配准算法

doi: 10.16383/j.aas.2016.c150618
基金项目: 

云南师范大学博士科研启动基金 01000205020503065

云南省教育厅科学研究项目 2015Z069

云南师范大学大学生科研训练基金 0100060502006

国家高技术研究发展计划(863计划) 2012AA121402

详细信息
    作者简介:

    汤昊林 云南师范大学信息学院本科生. 主要研究方向为图像配准以及医学图像 处理.E-mail:m18487107138@163.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软件工程系讲师.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, 视觉检测技术.E-mail:luoyi861030@163.com

    张雅莹 云南师范大学信息学院本科生.主要研究方向为非刚性点阵配准和医学图像处理.E-mail:nw_zhyaying@163.com

    张芳瑜 云南师范大学信息学院本科生.主要研究方向为非刚性点阵配准和医学图像处理.E-mail:zhfangyu_ynu@163.com

    通讯作者:

    杨扬 云南师范大学信息学院讲师.2007年获得日本早稻田大学计算机硕士学位, 2013年获得新加坡国立大学NGS博士学位.主要研究方向为医学图像处理, 图像配准, 地理空间信息技术, 人体咀嚼系统.E-mail:yyang_ynu@163.com

Non-rigid Point Set Registration with Mixed Features

Funds: 

Doctoral Scienti-c Research Founda-tion of Yunnan Normal University 01000205020503065

Key Scienti-c Research Project of Education Department of Yun- nan Province 2015Z069

College Students0 Scienti-c Research Training Project of Yunnan Nor- mal University 0100060502006

National High Technology Research and Devel-opment Program of China (863 Program) 2012AA121402

More Information
    Author Bio:

    Undergraduate student at the School of Information Science and Technology, Yunnan Nor- mal University. His research interest covers non-rigid point set registration and medical imag- ing.

    Professor at the School of Information Science and Technology, Yunnan Normal Univer- sity. He received his master degree from University of New SouthWales, Australia in 1998. His research interest covers geographic information system (GIS) and remote sensing image processing.

    Lecturer in the Depart- ment of Software Engineering, Yunnan Normal University. He received his Ph. D. degree from Harbin University of Science and Tech- nology in 2014. His research interest covers wireless sensor networks, weak signal detection, and visual detection.

    Undergraduate student at the School of Information Science and Technology, Yunnan Nor- mal University. Her research interest covers non-rigid point set registration and medical imag- ing.

    Undergraduate student at the School of Information Science and Technology, Yunnan Nor- mal University. Her research interest covers non-rigid point set registration and medical imag- ing.

    Corresponding author: YANG Yang Lecturer at the School of Information Science and Technology, Yunnan Normal University. He received his master degree from Waseda University, Japan in 2007, and Ph. D. degree from National University of Singapore, Singapore in 2013. His research interest covers medical image processing, image registration, geography information system and human masticatory system. Corresponding author of this paper.
  • 摘要: 提出一种基于混合特征的非刚性点阵配准算法.该算法包含了对应关系评估与空间变换更新两个相互交替的步骤.首先定义了两个特征描述法用于描述两个点阵之间的全局和局部几何结构特征差异,随后合并这两个特征描述法建立一个基于混合特征的能量优化方程.该能量优化方程可以利用线性分配技术进行求解,同时可以灵活地选择使用最小化全局结构特征差异或最小化局部结构特征差异来评估两个点阵之间的对应关系.为了增强前述两个步骤之间的协调性,我们利用能量权重调节在整个配准过程中控制能量优化从最小化局部结构特征差异逐步转变为最小化全局结构特征差异,同时控制用于空间变换的薄板样条函数(Thin plate spline)的更新从刚性变换逐步转变为非刚性变换.我们在二维轮廓配准、三维轮廓配准、序列图像配准和图像特征点配准下对本文算法进行了各项性能测试,同时也与当前8种流行算法进行了性能比较.本文算法展现了卓越的非刚性配准性能,并在大部分实验中超越了当前的相关算法.
  • 图  1  二维轮廓点阵配准下的性能对比(误差线表示了100次随机测试中平均误差的标准偏差值.从第1行至第4行分别为点阵Line,Fish 1,Chinese character以及Fish2的实验结果.

    Fig.  1  Comparison of our results against CPD,TPS-RPM and GMMREG on 2D contour point set registration (The error bars indicate the standard deviations of the mean errors in100 random experiments. From the top row to bottom row are: Line,Fish 1,Chinese character and Fish 2,respectively.

    图  2  本文算法的配准实例: Line

    Fig.  2  Registration examples on Line point set

    图  3  本文算法的配准实例: Fish 1

    Fig.  3  Registration examples on Fish 1 point set

    图  4  本文算法的配准实例: Chinese character

    Fig.  4  Registration examples on Chinese character point set

    图  5  本文算法的配准实例: Fish 2

    Fig.  5  Registration examples on Fish 2 point set

    图  6  三维Face轮廓点阵配准下的性能对比 (误差线表示了100次随机测试中平均误差的标准偏差值.)

    Fig.  6  Comparison of our results against CPD and GMMREG on3D face contour point set registration (The error bars indicate the standard deviations of the mean errors in 100 random experiments.)

    图  7  3D face点阵配准实例

    Fig.  7  Registration examples on 3D face point set

    图  8  CMU house与CMU hotel配准实例

    Fig.  8  Registration examples on CMU house and CMU hotel

    图  9  Pascal 2007 challenge 配准实例

    Fig.  9  Registration examples on Pascal 2007 challenge

    图  10  不同能量权重调节参数设定下的配准性能

    Fig.  10  Relationships between performances and different energy tradeoff adjustment parameter settings

    表  1  本文算法与相关算法的不同

    Table  1  Methodological differences between our method and the current methods

    算法对应关系评估空间变换更新
    使用的特征对应关系约束条件空间变换方程
    本文算法混合特征BTPS 能量方程 1TPS
    TPS-RPM高斯概率密度FTPS 能量方程 2TPS
    CPD高斯概率密度FMCC-NLLGRBF
    GMMREG高斯概率密度F最小化 L2 距离TPS
    Ma 等[29]Shape contextBL2E 评估子[30]RKHS
    Wang 等[31]MoAGF最小化 L2 距离RKHS
    注: B: 二值对应; F: 模糊对应; GRBF (Gaussian radial basis function): 高斯径向基函数; TPS: 薄板样条函数; MCC-NLL(Motion coherence constraint based negative log-likelihod):基于运动一致性的负对数似然; RKHS (Reproducing kernel Hilbert space): 再生核Hilbert空间; MoAG (Mixture of asymmetric Gaussian model): 混合非对称高斯模型; 在TPS能量方程 2中, $λ_{2}\textrm{tr}(d-I)^{\rm T}(d-I)$ 被加到了式(7) (TPS能量方程 1) 来控制仿射变换.
    下载: 导出CSV

    表  2  CMU house和CMU hotel序列图像中所有可能的图像配准结果 (%)

    Table  2  Matching rates on the CMU house and CMU hotel for all possible image pairs (%)

    算法CMU houseCMU hotel
    本文算法100.099.3
    CPD99.698.9
    GMMREG99.597.1
    Wang 等[31]100.0
    Torresani 等[14]100.0
    Zhou 等[9]≈ 100.0
    Leordeanu 等[13]99.894.8
    Caetano 等[10]< 96.0 < 90.0
    下载: 导出CSV

    表  3  汽车与摩托车图像库的配准结果 (%)

    Table  3  Matching rates on cars and motorbikes (%)

    本文算法CPDGMMREGAB
    9380828080
    下载: 导出CSV

    表  4  Jonker-Volgenant 算法性能 (测试矩阵由 Matlab 的 rand 函数自动生成.)

    Table  4  Performance of Jonker-Volgenant algorithm (The cost matrices were generated by Matlab rand function.)

    矩阵大小2005001 0002 0003 000
    所需时间 (秒)0.0020.0160.1000.3160.588
    下载: 导出CSV
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  • 收稿日期:  2015-10-10
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