期刊文献+

复杂背景下红外弱小目标检测的算法研究综述 被引量:32

A Review of Infrared Weak and Small Targets Detection under Complicated Background
下载PDF
导出
摘要 复杂背景下低信噪比弱小目标的自动检测是当今目标自动探测研究尚未解决的一个难题。目前,国内外许多学者已经作过大量的检测算法研究,但还没有建立成熟的理论体系和切实可行的实用算法,尤其是在复杂背景干扰的抑制方面,大部分研究工作所处理的还不是真正的复杂背景。本文在分析和总结国内外现有算法研究的基础上,指出了复杂背景下红外弱小目标检测的发展趋势,并提出了检测跟踪的一些有效技术措施。 It is an unfathomed and difficult problem that weak and small targets are detected in complicated background and low SNR. Scholars at home and abroad offer many detection algorithms, but these algorithms aren't mature, especially to complicated background, and these algorithms almost deal with uncomplicated background. This paper summarizes existing algorithms of weak targets detection under complicated background, points out weak targets detection development direction and refers to many efficient technique measures.
出处 《红外技术》 CSCD 北大核心 2006年第5期287-292,共6页 Infrared Technology
基金 总装备部基金资助课题.
关键词 复杂背景 弱小目标 信噪比 背景预测 complicated background weak and small targets SNR background forecast
  • 相关文献

参考文献14

二级参考文献34

  • 1彭嘉雄,彭铁.弱目标检测的图像流法[J].红外与激光工程,1996,25(4):34-40. 被引量:29
  • 2李红艳 吴成柯.遗传算法在低信噪比图像点目标检测中的应用[J].航空学报,1999,20(6).
  • 3Paul L Rosi and Tim Ellis. Image difference threshold strategies and shadow detection[A]. In proceedings of the 6th British Machine Vision Conference, pages[C]. BMVA Press, 1995. 347-356.
  • 4David Casasent and Anqi Ye. DetectionFihers and Algorithm Fusion for ATR[J]. IEEE Transactions on image processing, 1997. 6 (1):114-125.
  • 5Sang, Nong; Zhang, Tianxu; Shi, Weiqiang. Characteristics of Contrast and Application for small Target Detection[A]. SPIE Data and Processing of Small Target[C], 1998. 3809:123-131.
  • 6Wu Q. A correlation-relaxation-labeling framework for computing optical flow-template matching from a new perspective[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995,17(9) ..843-853.
  • 7Torr P H S, Zisserman A. Concerning Bayesian motion segmentation, model averaging, matching and the trifocal tensor[A].Fifth European Conference on Computer Vision[C]. Germany Freiburg: Springer Verlag, 1998.
  • 8Irani M, Anandan P. A unified approach to moving object detection in 2D and 3D scences[J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1998,20(6):577-589.
  • 9Strehl A, Aggarwal J K. Detecting moving objects in airborne forward looking infrared sequences[A]. IEEE Workshop on Computer Vision Beyond the Visible Spectrum.. Methods and Applications, (CVBVS 1999) Proceedings[C]. Fort Collins,Colorado, USA, pub-IEEE:adr, 1999. 3-12.
  • 10Horn B K P, Schunk B G. Determing optical flow[J]. Artificial Intelligence, 1987,17:185-203.

共引文献198

同被引文献264

引证文献32

二级引证文献133

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