计算机应用 ›› 2012, Vol. 32 ›› Issue (03): 759-761.DOI: 10.3724/SP.J.1087.2012.00759

• 图形图像技术 • 上一篇    下一篇

基于特征区域的图像自动配准

舒小华1,2,沈振康2   

  1. 1.湖南工业大学 电气与信息工程学院,湖南 株洲 412008;
    2.国防科学技术大学 ATR实验室,长沙 410073
  • 收稿日期:2011-09-21 修回日期:2011-11-25 发布日期:2012-03-01 出版日期:2012-03-01
  • 通讯作者: 舒小华
  • 作者简介:舒小华(1965-),男,湖南邵阳人,副教授,博士研究生,主要研究方向:信号与信息处理、数字图像处理;沈振康(1936-),男,上海人,教授,博士生导师,主要研究方向:数字图像处理、目标识别、信号处理。
  • 基金资助:

    湖南省自然科学基金资助项目(09JJ3115);湖南省高校产业化培育项目(10CY006)。

Automatic image registration based on feature region

SHU Xiao-hua1,2, SHEN Zhen-kang2   

  1. 1.School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou Hunan 412008, China;
    2.ATR Laboratory, National University of Defense Technology, Changsha Hunan 410073, China
  • Received:2011-09-21 Revised:2011-11-25 Online:2012-03-01 Published:2012-03-01
  • Contact: Xiao-Hua SHU

摘要: 为了解决基于特征的图像配准中的特征点的定义和提取问题,提出了一种以特征区域替代特征点的定义和提取方法。该方法应用Moravec算子选择候选特征区域,使用具有旋转不变性的Zernike矩表征该区域的特性;采用二级匹配策略进行特征区域的匹配,即基于自组织映射神经网络的初始匹配及精细匹配;建立图像的配准框架并实现图像的配准。实验结果表明,该方法能有效地提取图像的特征点并能准确地进行特征点的匹配,整个配准过程完全自动进行。

关键词: 图像配准, 特征点, 特征区域, Zernike矩, 二级匹配策略

Abstract: In order to solve the problem of feature points definition and extraction in image registration based on feature points, an approach was proposed in this paper. Feature region was defined and extracted instead of feature point. Moravec operator was applied to choose the preparatory feature regions, and rotation-invariant Zernike moment was used to characterize the feature regions. Two-step matching strategy was employed for matching the feature regions, i.e. the initial matching was based on self-organizing mapping network and the fine matching. The automatic image registration framework was established and the image registration was realized. The experiments show that this method can effectively extract the image feature points and perform accurate matching of the feature points, the registration process is completely automated.

Key words: image registration, feature point, feature region, Zernike moment, two-step matching strategy

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