东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (5): 728-732.DOI: -

• 论著 • 上一篇    下一篇

带钢表面缺陷图像小波融合方法

王永慧;颜云辉;吴艳萍;梁惠升;   

  1. 东北大学机械工程与自动化学院;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-05-15 发布日期:2013-06-22
  • 通讯作者: Wu, Y.-P.
  • 作者简介:-
  • 基金资助:
    国家高技术研究发展计划项目(2008AA04Z135);;

Image fusion based on wavelet transformation for strip steel surface defects

Wang, Yong-Hui (1); Yan, Yun-Hui (1); Wu, Yan-Ping (1); Liang, Hui-Sheng (1)   

  1. (1) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-05-15 Published:2013-06-22
  • Contact: Wu, Y.-P.
  • About author:-
  • Supported by:
    -

摘要: 采用两个CCD图像传感器在多种采集方式下获得不同缺陷图像,运用小波变换法对缺陷图像进行融合,结合带钢表面缺陷特征对融合算法和规则进行了探讨,并对融合后的缺陷图像质量进行评价.实验结果显示了小波融合方法的优越性,该方法能更全面、更准确、更大限度地获得缺陷图像信息,解决了单一CCD缺陷采集模式下存在的缺陷特征丢失问题,为后续的缺陷识别与分类提供有效、可靠的数据支持.

关键词: 小波变换, 图像融合, 表面缺陷, 带钢

Abstract: Two CCD camera sensors were applied to the same object to acquire different defect images on strip steel surface in several ways, then the images were implemented to those defects through wavelet transformation. The fusion algorithm and rules based on the abstract features from the real surface defects are discussed with the quality of defect images after fusion evaluated. Experimental results showed obviously the superiority of the approach mentioned above because it can acquire more accurate defect image data as much as possible, thus solving the problem of missing the defects due to the acquisition by single CCD camera and providing more useful and reliable data for further process of image recognition and classification.

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