期刊文献+

Knowledge based recognition of harbor target 被引量:4

Knowledge based recognition of harbor target
下载PDF
导出
摘要 A fast knowledge based recognition method of the harbor target in large gray remote-sensing image is presented. First, the distributed features and the inherent feature are analyzed according to the knowledge of harbor targets; then, two methods for extracting the candidate region of harbor are devised in accordance with different sizes of the harbors; after that, thresholds are used to segment the land and the sea with strategies of the segmentation error control; finally, harbor recognition is implemented according to its inherent character (semi-closed region of seawater). A fast knowledge based recognition method of the harbor target in large gray remote-sensing image is presented. First, the distributed features and the inherent feature are analyzed according to the knowledge of harbor targets; then, two methods for extracting the candidate region of harbor are devised in accordance with different sizes of the harbors; after that, thresholds are used to segment the land and the sea with strategies of the segmentation error control; finally, harbor recognition is implemented according to its inherent character (semi-closed region of seawater).
出处 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2006年第4期755-759,共5页 系统工程与电子技术(英文版)
关键词 multi-scale candidate region character extraction threshold segmentation. multi-scale, candidate region, character extraction threshold segmentation.
  • 相关文献

参考文献10

  • 1[1]Li Yan,Peng Jiaxiong.Feature extraction of the harbor target and its recognition,Huazhong Keji Daxue Xuebao.2001,29 (6) (in Chiuese):
  • 2[2]Milan Sonka,Vaclav Hlavac,Roger Boyle.Image processsing,analysis,and machine vision.Second Edition,People's Post & Telecom Press,2003,9:83~90:117~127.
  • 3[3]Mehmet Sezgin.Survey on image thresholding techniques and quantitative performance evaluation.Journal of Electr onic Imaging,2004,13(1):146~167.
  • 4[4]Otsu N.A threshold selection method from grey-level histograms.IEEE Trans.Syst.,Man,Cybern.,SMC-8,1978:62 ~66.
  • 5[5]Kittler J,Illingworth J.Minimum error thresholding.Pattern Recognition 1986,19:41~47.
  • 6[6]Yan H.United formulation of a class of image thresholding techniques.Pattern Recognition,1996,29:2025~2032.
  • 7[7]Blimes J A.A gentle tutorial of the EM algorithm and its application to parameter estimation for gaussian mixture and hidden markov models.Technical Report TR-97-021,International Computer Science Institute (ICSI) and Computer Science Division,Dept.of Electrical Engineer-ing and Computer Science,U.C.Berkeley,1998.
  • 8[8]Theodoridis S,et al.Pattern Recognition,Second Edition,2003,9:303~311; 512~519.
  • 9[9]Li Jizong,Introduction to Pattern Recognition,1994:313~314.
  • 10[10]Kaplan L M,Kuo C C J.Texture roughness analysis and synthesis via extended self-similar model.IEEE Trans on PAMI,1995,1(11):1043~1056.

同被引文献15

引证文献4

二级引证文献19

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部