应用聚类和分形实现复杂背景下的扩展目标分割

张坤华; 杨烜

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应用聚类和分形实现复杂背景下的扩展目标分割

信息科学 | 更新时间：2020-08-12

- 应用聚类和分形实现复杂背景下的扩展目标分割
- Segmentation for extended target in complex backgrounds based on clustering and fractal
- 光学精密工程 2009年17卷第7期页码：1665-1671
- 作者机构：
  
  深圳大学信息工程学院,广东深圳,518060
- 作者简介：
  
  [ "张坤华 (1973-),女,四川绵竹人,博士,讲师,2003年于中科院光电技术研究所获得博士学位" ]
  [ "主要从事图像处理、模式识别、目标检测与跟踪、信号处理等方面的研究。E-mail: zhang_kh@szu.edu.cn 杨烜 (1969-),女,陕西西安人,博士,教授,1998年于西安交通大学获得博士学位,2001年于西安电子科技大学博士后出站,主要从事图像处理、数据融合、信号处理等方面的研究。E-mail: xyang0520@263.net" ]
- 基金信息：
  
  国家自然科学基金资助项目(No.60572101)();深圳大学科研启动基金资助项目(No.200745)();国家重点实验室基金资助项目(No.51483040105
- DOI：
  中图分类号： TP391
- 收稿日期：2008-08-15，
  
  修回日期：2008-10-09，
  
  网络出版日期：2009-07-25，
  
  纸质出版日期：2009-07-25
- 稿件说明：
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张坤华, 杨烜. 应用聚类和分形实现复杂背景下的扩展目标分割[J]. 光学精密工程, 2009,17(7): 1665-1671

ZHANG Kun-hua, YANG Xuan. Segmentation for extended target in complex backgrounds based on clustering and fractal[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1665-1671
张坤华, 杨烜. 应用聚类和分形实现复杂背景下的扩展目标分割[J]. 光学精密工程, 2009,17(7): 1665-1671 DOI：

ZHANG Kun-hua, YANG Xuan. Segmentation for extended target in complex backgrounds based on clustering and fractal[J]. Editorial Office of Optics and Precision Engineering, 2009,17(7): 1665-1671 DOI：

摘要

将K-均值聚类方法与分形理论相结合

提出了一种分两个阶段对扩展目标进行分割的方法。在预分割阶段

运用粗糙集理论求取初始聚类中心

在K-均值聚类分割和区域连通的基础上

检测图像边缘并进行边界跟踪

对于获得的目标和背景团块根据扩展目标特性确定目标潜在区域。在进一步分割阶段

给出图像分维数随尺度变化的函数

利用自适应阈值

根据分形理论的尺度不变性进一步抑制预分割结果中的自然背景

并运用形态学开运算消除背景粘连。实验表明该方法能有效并可靠地实现复杂背景下扩展目标的精确分割

分割出的扩展目标轮廓细节保持良好。

Abstract

A new segmentation algorithm which was divided into two steps was proposed for an extended target in complex backgrounds by utilizing the K-means clustering and fractal theory. Firstly

the K-means clustering algorithm was improved by using the rough set theory to determine initial cluster centroids. On the basis of K-means clustering segmentation and region connection

the edges of the target and backgrounds were extracted accurately and intactly. After boundary tracking

the potential target regions were detected according to the characteristics of the extended target. Secondly

by giving the function of a fractal dimension changing with the scale

the natural backgrounds in potential target regions were removed by the fractal scale invariance. Then

the background conglutination was eliminated by a mathematical morphology method. The experimental results indicate that the algorithm can segment the extended target in complex backgrounds correctly and reliably

and the segmented target reserves a good contour.

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Keywords

references

彭真明,蒋彪,肖峻. 基于脉冲耦合神经网络的空中扩展目标检测 [J]. 强激光与粒子束,2007,19(12):2011-2016. PENG ZH M, JIANG B, XIAO J. Aerial extended target detection based on unit-linking pulse coupled neural networks [J]. High Power Laser and Particle Beams, 2007,19(12):2011-2016. (in Chinese)[2] 孔刚,张启衡. 复杂背景下扩展目标多尺度小波分割策略 [J]. 光电子激光,2004,15(2):216-220. KONG G, ZHANG Q H. Mutiscale wavelet based segmentation of extended target in complex environment [J]. Journal of OptoelectronicsLaser , 2004,15(2):216-220. (in Chinese)[3] 宿丁,张启衡,谢盛华. 复杂背景下扩展目标双尺度分形分割算法 [J]. 仪器仪表学报,2006,27(6):2103-2106. SU D,ZHANG Q H,XIE SH H. Double-scale fractal segmentation for extended target in complex background [J]. Chinese Journal of Scientific Instrument, 2006,27(6):2103-2106. (in Chinese)[4] 刘韬,蔡淑琴,曹丰文,等. 基于距离浓度的K-均值聚类算法[J]. 华中科技大学学报(自然科学版), 2007,35(10):50-52. LIU T, CAI SH Q, CAO F W, et al.. K-means clustering algorithm based on distance concentration [J]. Journal of Huazhong University of Science and Technology Nature Science, 2007,35(10):50-52.(in Chinese)[5] 刘艳丽,刘希. 一种基于密度的K-均值算法[J]. 计算机工程与应用, 2007,43(32):153-155. LIU Y L, LIU X. K-means clustering algorithm based on density [J]. Computer Engineering and Applications, 2007,43(32):153-155.(in Chinese)[6] 边肇棋,张学工.模式识别[M]. 北京:清华大学出版社,2000. BIAN ZH Q, ZHANG X G. Pattern Recognition [M]. Beijing: Qinghua University Press, 2000. (in Chinese)[7] 邵锐,巫兆聪,钟世明. 基于粗糙集的K-均值聚类算法在图像分割中的应用[J]. 测绘信息与工程,2005,30(5):1-2. SHAO R, WU ZH C, ZHONG SH M. Application of rough sets and k-means clustering to image segmentation [J].Journal Geomatics, 2005,30(5):1-2. (in Chinese)[8] 王国胤. Rough集理论与知识获取 [M]. 西安:西安交通大学出版社,2001. WANG G Y. Rough Set Theory and Knowledge Acquisition [M]. Xi'an: Xi'an Jiaotong University press, 2001. (in Chinese)[9] 史册. 对一种快速边缘跟踪算法的讨论 [J]. 小型微型计算机系统, 2002,12(6):641-645. SHI C. A discussion of a fast algorithm for boundary tracking [J]. Minicomputer System, 2002,12(6):641-645. (in Chinese)[10] PENTLAND A. Fractal-based description of natural scenes [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984,6(6):661-674.[11] PELEG S. Multiple resolution texture analysis and classification [J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984,6(4):518-523.

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