上海交通大学学报(自然版) ›› 2013, Vol. 47 ›› Issue (12): 1842-1847.

• 自动化技术、计算机技术 • 上一篇    下一篇

基于局部特征点检测与匹配的微悬臂梁变形受力测量方法

刘洪涛,梁振宁,胡文,莫锦秋,王石刚
  

  1. (上海交通大学 机械与动力工程学院,  上海 200240)
     
  • 收稿日期:2013-04-06 出版日期:2013-12-28 发布日期:2013-12-28
  • 基金资助:

    国家自然科学基金资助项目(50875169)

A Method for  Deformation and Force Measurement of Micro-Cantilever Based on Local Feature Point Detecting and Matching

LIU Hongtao,LIANG Zhenning,HU Wen,MO Jinqiu,WANG Shigang
  

  1. (School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai 200240, China)
  • Received:2013-04-06 Online:2013-12-28 Published:2013-12-28

摘要:

提出了一种基于局部特征点检测与匹配的微悬臂梁变形受力测量方法.通过光学显微镜得到微悬臂梁变形前后的图像和基于放大的微悬臂梁表面的散斑纹理特征,在尺度空间中定位具有局部响应极值的LOG(Laplace of Gaussian)特征点,并在LOG特征点周围提取局部仿射不变封闭区域,其质心可以作为具有亚像素精度的特征点位置.由封闭区域构造仿射不变特征描述算子并进行特征点匹配,根据匹配点的位移信息进行悬臂梁弯曲挠度曲线拟合以描述微悬臂梁的弯曲变形,并采用最小二乘法计算悬臂梁受力大小与受力点.通过对实际的微悬臂梁变形图像实验,验证了所提方法的有效性.
 
 

关键词: 局部特征点,  , 仿射不变特征矢量,  , 特征匹配, 微力测量

Abstract:

 A method based on local feature point detecting and matching for force measurement of micro-cantilever was proposed. Images of micro-cantilever before and after deformation were obtained by optical microscope. Based on the enlarged speckle texture of the cantilever surface, the LOG (Laplace of Gaussian) feature points with the local extreme response value were detected in the scale space. An affine-invariant closed region was extracted for each LOG feature point. Then the centroid of this closed region could be calculated and treated as a feature point with subpixel positioning. Affine-invariant feature descriptors could be constructed and used for the matching of the corresponding points in images before and after deformation. The deformation of the microcantilever could be described by deflection curve obtained by fitting the displacement of the matching points, and then the force value as well as position applied by the loader on the cantilever were calculated using the least square method. The performance of the proposed method was verified by experiments based on actual deformation of microcantilever.
 

Key words: local feature point, affine invariant feature descriptor, feature matching, micro-force measurement

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