规则图像特征的标准互相关相似函数

孙明磊; 张 融; 朱小峰; 宗光华

规则图像特征的标准互相关相似函数

1.
北京航空航天大学机械工程及自动化学院, 北京 100191
2. 北京市劳动保护科学研究所, 北京 100054

基金项目: 中国博士后科学基金资助项目(20070420287);国家863计划资助项目(2004AA404260)

详细信息

作者简介:
孙明磊(1974-),男,湖北襄樊人,博士后,sunminglei@gmail.com.

中图分类号: TP 242.6⁺2
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- 被引次数: 0
出版历程
- 收稿日期: 2007-12-23
- 网络出版日期: 2008-12-31

Normalized cross correlation computation for geometry image features

1.
School of Mechanical Engineering and Automation, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
2. Beijing Municipal Institute of Labour Protection, Beijing 100054, China

摘要

摘要: 利用标准互相关算子对机器视觉领域常用的十字图像特征进行匹配运算,发现相似函数在峰值区域的分布特征为4个互相对称的双曲面.受此启发,对规则几何图像特征(如矩型、圆型等)的标准互相关相似函数展开了研究.以图像特征概率分布的形式给出了NCCO算子在二值图像匹配下的相似函数计算式.推导并证明了矩型、圆型等典型图像特征相似函数在峰值点区域具有确定的数学模型.基于上述数学模型,在微装配显微视觉系统上进行了实验验证,对模板优化、图像特征的尺寸等优化方法进行了探讨.上述结论对图像定位、图像标定、图像跟踪等技术研究具有实用价值.
- 视觉 /
- 图像 /
- 相关方法 /
- 模式匹配
Abstract: Normalized cross correlation operator (NCCO) was used for pattern matching to localize popular cross-shaped features in microscopic vision. Distribution feature of similarity function around peak zone was found to be four symmetry hyperboloid planes. Inspired by this, some research work on pattern matching of geometry image features (such as rectangular, circular, etc.) was presented. A probability distribution based formula computing NCCO with two binary images was proposed. Mathematic models of some typical geometry image features- (rectangular, circular, cross-type, box-type) similar functions around peak point were derived from and proved. Based on these models, some experiments about template image optimum and feature size optimum were conducted on a microscopic vision workcell. Conclusions above are practically useful to image positioning, image calibration and image tracking techniques.
- vision /
- image /
- correlation method /
- pattern matching

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参考文献(1)

[1] 于起峰,陆宏伟,刘肖琳．基于图像的精密测量与运动测量[M]．北京: 科学出版社,2002:132-134 Yu Qifeng,Lu Hongwei,Liu Xiaolin. Precision measurement and motion measurement based on image[M].Beijing: Publishing House of Science,2002:132-134(in Chinese) [2] Milan S,Vaclav H, Roger B．Image processing, analysis, and machine vision[M]. Washington DC:Brooks and Cole Publishing of Thomson,1998:622-623 [3] Ge Yang．Scale-based integrated microscopic computer vision techniques for micromanipulation and micro assembly ．Minnesota:The Graduate School of the University of Minnesota,2004 [4] 孙明磊,李大寨,余志伟,等．基于NCCO的十字图像特征亚像素定位[J]．北京航空航天大学学报,2005,31(7):780-784 Sun Minglei,Li Dazhai,Yu Zhiwei,et al. Locating cross-shaped image feature with subpixel accuracy based on NCCO[J]. Journal of Beijing University of Aeronautics and Astronautics,2005,31(7):780-784(in Chinese) [5] Brown L G．A survey of image registration techniques[J]．ACM Computer Surveys,1992,24(4):325-376 [6] 孙明磊．显微视觉图像分析技术应用及实验研究．北京:北京航空航天大学机械工程及自动化学院,2006 Sun Minglei. Application and experimental research on image-analysis based methodology in microscopic computer vision . Beijing: School of Mechanical Engineering and Automation,Beijing University of Aeronautics and Astronautics,2006(in Chinese)

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