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。
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.
出处
《计算机应用》
CSCD
北大核心
2012年第3期759-761,共3页
journal of Computer Applications
基金
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)
