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Method for Improving Camouflage Image Quality using Texture Analysis

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Kalyani V. Patil, K. N. Pawar
10.5120/ijca2017915907

Kalyani V Patil and K N Pawar. Method for Improving Camouflage Image Quality using Texture Analysis. International Journal of Computer Applications 180(8):6-8, December 2017. BibTeX

@article{10.5120/ijca2017915907,
	author = {Kalyani V. Patil and K. N. Pawar},
	title = {Method for Improving Camouflage Image Quality using Texture Analysis},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2017},
	volume = {180},
	number = {8},
	month = {Dec},
	year = {2017},
	issn = {0975-8887},
	pages = {6-8},
	numpages = {3},
	url = {http://www.ijcaonline.org/archives/volume180/number8/28818-2017915907},
	doi = {10.5120/ijca2017915907},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Traditional evaluation method of camouflage texture effect is subjective evaluation. It’s very tedious and inconvenient to direct the texture designing. In this project, a systemic and rational method for direction and evaluation of camouflage texture designing is proposed. Camouflage consists of things such as leaves, branches, or brown and green paint, which are used to make it difficult for anenemy to see military forces and equipment. A camouflage texture evaluation method predicated on SSIM (Weight structural homogeneous attribute) is given to access the effects of camouflage texture at first.Then nature image features between the camouflage texture and the background image are calculated to help direct the designing camouflage texture. In this project, we focus on the essential of the human visual system, and its relative significance of the different factors of affecting camouflage texture. The proposed method developed a computational vision model to evaluate the perceived differences between camouflage texture image and background image. And a variety of features, measuring thresholds for discriminating small changes in naturalistic images have been studied to direct the camouflage texture designing.

References

  1. Andrew Owens and Connelly Barnes “Camouflaging an Object from Many Viewpoints “1063-6919/14 $31.00 © 2014 IEEE DOI 10.1109/CVPR.2014.350
  2. KeGu, GuangtaoZhai, Xiaokang Yang, Wenjun Zhang, and Min Liu “STRUCTURAL SIMILARITY WEIGHTING FOR IMAGE QUALITY ASSESSMENT”
  3. Hu Jiang-hua, Qin Lei, Fu Tian-qi “Image Processing Based on Mathematical Morphology in Camouflage” 013.57978-0-7695-5050-3/13 $26.00 © 2013 IEEE DOI 10.1109/ICIG.2013.57
  4. Yusra A. Y. Al-Najjar, Dr. Der Chen Soong “Comparison of Image Quality Assessment: PSNR, HVS, SSIM, UIQI” International Journal of Scientific & Engineering Research, Volume 3, Issue 8, August-2012 ISSN 2229-5518
  5. Marina Lukashevich, RaufSadykhov BSUIR, Minsk, Belarus “Texture Analysis: Algorithm for Texture Teatures Computation” IV International Conference “Problems of Cybernetics and Informatics” (PCI'2012), September 12-14, 2012
  6. ArashAbdiHejrandoost, Reza Safabakhsh “Thinning Based Multipurpose Camouflage Pattern Design ” 978-1-4577-1535-8/11/$26.00 ©2011 IEEE
  7. Song Liming and GengWeidong “ A new Camouflage Texture Evaluation Method based on WSSIM and Nature Image Features”978-1-4244-7874-3/10/$26.00@2010
  8. Andrzej Materka and Michal Strzelecki “Texture Analysis Methods – A Review”

Keywords

Camouflage Image

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