期刊文献+

数码迷彩的生成算法研究 被引量:21

Research of Digital Camouflage Generation Algorithm
在线阅读 下载PDF
导出
摘要 传统迷彩大都是由不规则的斑点或条纹组成,其边缘平滑、不同色彩间界限分明,视觉区分度强,以致隐蔽的效果大打折扣。数码迷彩能够有效破坏伪装目标的外形,使不同色彩间的边界模糊、破碎,具有良好的伪装隐身效果。本文提出了一种根据目标背景图像生成数码迷彩的算法,它首先将背景图案的颜色量化,提取出背景主色,然后确定马赛克方块的大小,最后生成数码迷彩。实验表明,利用此算法生成的数码迷彩相对于传统迷彩能够更好地与自然背景相融合,达到良好的光学伪装效果。 The traditional camouflage is based on a number of irregular spots or stripes,whose edges are smooth as well as they have clear boundaries between different colors.The visual distinction degree is so strong that the camouflage effect is debased.The digital camouflage can break the boundary of a camouflaged target between different colors,and make it blurred and broken,so as to attain a better camouflaged effect.An algorithm which can generate digital camouflage figures is presented with background images of the target.It quantizes the colors of the background,extracts the dominant colors,and then determines the size of a mosaic block.Finally,the digital camouflage figure is generated.The experimental result shows that,the digital camouflage is easier to blend with the natural background than the traditional camouflage,and achieves a good effect of the optical camouflage.
出处 《光电工程》 CAS CSCD 北大核心 2010年第11期110-114,共5页 Opto-Electronic Engineering
基金 总后基建营房部科研项目(营080709)
关键词 传统迷彩 数码迷彩 背景图像 光学伪装 traditional camouflage digital camouflage background image optical camouflage
  • 相关文献

参考文献5

二级参考文献15

  • 1周红,李晓霞,徐英,赵大鹏.动态变形伪装对红外成像导引头探测距离的影响[J].红外技术,2005,27(1):29-33. 被引量:6
  • 2常发亮,刘静,乔谊正.基于自组织神经网络的彩色图像自适应聚类分割[J].控制与决策,2006,21(4):449-452. 被引量:6
  • 3申岳国,霍晓强,李晓齐.工程机械迷彩伪装的设计与优化[J].工程机械,2006,37(8):29-33. 被引量:5
  • 4徐英.基于背景代表色提取的迷彩伪装颜色选取算法[J].光电工程,2007,34(1):100-103. 被引量:28
  • 5SAAD M A, BOVIK A C. Extracting regions of interest from still images: color saliency and wavelet-based approaches[C]//2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, New Jersey, USA: Institute of Electrical and Electronics Computer Society, January 2009: 540-543.
  • 6RAUT S, RAGHUVANSHI M, DHARASKAR R, RAUT A. Image segmentation-a state-of-art survey for prediction[C]//Proceedings- International Conference on Advanced Computer Control, New Jersey, USA: Institute of Electrical and Electronics Engineers Computer Society, January 2009: 420-424.
  • 7YEO N C, LEE K H, VENKATESH Y V, ONG S H. Colour image segmentation using the self-organizing map and adaptive resonance theory[J]. Image and Vision Computing, 2005, 23(12): 1060-1079.
  • 8KONISHI T, OMATU S, SUGA Y. Extraction of riceplanted area using a self-organizing feature map[J]. Artificial Life and Robotics, 2007, 11(2): 215-218.
  • 9CHANDA B. Digital image processing and analysis[M]. Prentice Hall, 2007.
  • 10LI Dongming, WANG Yuanzhi, Du B. Research on segmentation methods of weed and soil background under HSI color model[C]//Proceedings-2009 2nd International Workshop on Knowledge Discovery and Data Mining, Piscataway, New Jersey, USA: Institute of Electrical and Electronics Engineers Computer Society, 2009: 628-631.

共引文献36

同被引文献151

引证文献21

二级引证文献74

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