吉林大学学报(工学版) ›› 2013, Vol. 43 ›› Issue (增刊1): 47-50.

• 论文 • 上一篇    下一篇

基于变电站多模图像的自动集成配准方法

夏英杰1,2, 李金屏1,2, 陈瑞3   

  1. 1. 济南大学 信息科学与工程学院,济南 250022;
    2. 济南大学 山东省网络环境智能计算技术重点实验室,济南250022;
    3. 中国兵器工业集团第53研究所 仿真与信息中心,济南 250032
  • 收稿日期:2012-05-25 发布日期:2013-06-01
  • 作者简介:夏英杰(1978-),男,讲师.研究方向:图像处理.E-mail:ise_xiayj@ujn.edu.cn
  • 基金资助:

    国家自然科学基金项目(60873089);山东省教育科学规划课题重点项目(2008ZK0007);济南大学科研计划项目(XKY0926).

Automatic synthetic registration method based on substation multi-modal images

XIA Ying-jie1,2, LI Jin-ping1,2, CHEN Rui3   

  1. 1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China;
    2. Shandong Provincial Key Laboratory of Network based Intelligent Computing, University of Jinan, Jinan 250022, China;
    3. Simulation and Information Center, The 53RD Research Institute of China North Industries Group Corporation, Jinan 250032, China
  • Received:2012-05-25 Published:2013-06-01

摘要:

针对采集的变电站多模图像,提出了一种改进的多模态图像的自动集成配准方法。变电站多模图像的尺寸不同,所以先对其进行小波变换,以变换后得到的两幅概貌图像为待配准图像,以六种配准方法中的每一种作为一个适应度函数。使用遗传算法进行搜索,分别寻找两幅概貌图像的最佳配准位置,再映射回小波变换前的多模图像,最后使用动态可信度进行集成。实验结果证明,该方法能实现不同尺寸的多模态图像的自动配准,速度较快,鲁棒性强,比单一的配准方法准确性高,并且可以不断地修正可信度,具有较强的适应性。

关键词: 多模图像配准, 小波变换, 动态可信度, 集成配准方法

Abstract:

An improved automatic synthetic registration method for substation multi-modal images is proposed.Two multi-modal images were different sizes,so wavelet transform was done.And the results were prepared for registration.In order to search the best registration position of two multi-modal images,genetic algorithm was used in the method.And one of the six methods was the fitness function of genetic algorithm in each search.Then six results were obtained for original images,and were integrated based on dynamic credibility.It is proved that automatic registration for multi-modal images of different sizes can be realized.This method is high speed and robustness,and it is more accurate than any single registration method.Besides,the credibility can be corrected dynamically.It has strong adaptability.

Key words: multi-modal image registration, wavelet transform, dynamic credibility, synthetic registration method

中图分类号: 

  • TP751.1

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