Journal of Systems Engineering and Electronics ›› 2009, Vol. 31 ›› Issue (9): 2071-2075.

• 电子技术 • 上一篇    下一篇

改进的分形算法在弱小目标检测中的应用

王鑫, 唐振民   

  1. 南京理工大学计算机科学与技术学院, 江苏, 南京, 210094
  • 收稿日期:2008-08-01 修回日期:2008-10-05 出版日期:2009-09-20 发布日期:2010-01-03
  • 作者简介:王鑫(1981- ),女,博士研究生,主要研究方向为模式识别、目标检测和跟踪.E-mail:rongtian_helen@yahoo.com.cn

Application of improved fractal algorithm in small target detection

WANG Xin, TANG Zhen-min   

  1. School of Computer Science and Technology, Nanjing Univ. of Science and Technology, Nanjing 210094, China
  • Received:2008-08-01 Revised:2008-10-05 Online:2009-09-20 Published:2010-01-03

摘要: 针对单帧图像中的弱小目标检测问题,提出了一种改进的基于分形的快速检测方法。该方法首先利用图像的局部熵信息对目标进行粗定位,以得到一个包含目标的感兴趣区域,然后利用分形理论构造该区域的分维像,最后对分维像采用自适应阈值分割即可将弱小目标精确检测出来。与传统分形算法相比,提出的改进算法包含粗定位和细定位两部分,它将分形算法要处理的区域缩减到局部熵所估计的小范围内,从而克服了传统分形方法计算量大、抗噪性差的缺点。仿真实验结果表明,该方法能够稳健、快速、有效地检测弱小目标。

Abstract: An improved fast method based on fractal theory is presented for small and weak target detection in a single-frame image.The algorithm firstly uses the local entropy information to locate the target coarsely,and then a region of interest(ROI) containing the small target is obtained.Secondly,a fractal dimension image of this region is constructed based on the fractal theory.Finally,self-adaptive threshold segmentation is used for the fractal dimension image to get the exact detection result.Compared with the traditional fractal algorithm,the presented method is divided into two parts: coarse location and accurate location.The region to be processed by the fractal algorithm is reduced to a small range that is estimated by local entropy,thus overcoming the defect of huge computational cost and poor anti-noise capability of traditional fractal methods.The experimental results prove that the proposed method is robust,fast and effective for small and weak target detection.

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