东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2006, Vol. 27 ›› Issue (11): 1185-1188.DOI: -

• 论著 • 上一篇    下一篇

基于小波多尺度扩展分形特征的目标检测方法

魏颖;王晓哲;史泽林;徐心和;   

  1. 东北大学信息科学与工程学院;东北大学信息科学与工程学院;中国科学院沈阳自动化研究所;东北大学信息科学与工程学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110016;辽宁沈阳110004
  • 收稿日期:2013-06-23 修回日期:2013-06-23 出版日期:2006-11-15 发布日期:2013-06-23
  • 通讯作者: Wei, Y.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60475036);;

Target detection method based on wavelet multi-scale extended fractal feature

Wei, Ying (1); Wang, Xiao-Zhe (1); Shi, Ze-Lin (2); Xu, Xin-He (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China; (2) Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
  • Received:2013-06-23 Revised:2013-06-23 Online:2006-11-15 Published:2013-06-23
  • Contact: Wei, Y.
  • About author:-
  • Supported by:
    -

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摘要： 研究复杂自然背景下的小目标检测问题,对Kaplan的扩展分形特征进行改进,使其对目标尺寸具有一定的适应性·考虑自然背景和斑点状人造目标在水平、垂直方向特性,利用小波分析良好的方向选择性,通过多级小波分解互能量交叉,在两个方向上有效地增强目标、抑制背景干扰,从而提出一种基于小波分析的多尺度扩展分形特征·实验结果表明,基于该特征的目标检测算法对复杂地面背景、海面背景的红外图像和电视图像具有较好的稳健性和适应性,能从单帧图像中较好地检测出小目标,具有检测速度较快、易于实现的特点·

关键词: 目标检测, 自相似, 多尺度, 扩展分形特征, 小波分解, 互能量交叉

Abstract: Investigates detection of small targets in complex natural background by way of improving Kaplan's extended fractal feature to adapt itself to target dimensions to a certain extent. In view of the natural background and both the horizontal and vertical directionalities of the speckled man-made targets, a multi-scale extended fractal feature via wavelet analysis is proposed utilizing the directionalities which are well decomposed by wavelet to combine the horizontal and vertical wavelet decomposition of their mutual energy respectively, thus intensifying effectively the target image both horizontally and vertically with background disturbance suppressed. Experimental results showed that the target detection based on such a feature is stable and adaptable to infrared/TV images on either complex ground or sea surface and available to locate quickly the small target from single-frame images.

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