Developing a similarity index for static camouflaged target detection

References

• Sten N and Lars B. Assessing camouflage using textural features. Proc. SPIE, 2001, 4370, 60–71.
   Google Scholar
• Bhajantri NU and Nagabhushan P. Camouflage defect identification: a novel approach. Proc. 9th Int. Conf. on Information technology, Bhubaneswar, India, December 2006, pp. 145–148.
   Google Scholar
• Gilmore MA, Jonse CK, Hayns AW, Tolhurst DJ, To M and Troscianko T. Use of a vision model to quantify the significance of factors effecting target conspicuity. Proc. SPIE, 2006, 6239, 1–12.
   Google Scholar
• Muller T and Muller M. Computer-aided camouflage assessment in real-time. Proc. SPIE, 2007, 6543, 701–711.
   Google Scholar
• Engeldrum PG. Image quality modeling: where are we? IS&T’s 1999 PICS Conference, Savannah, GA, April 1999, pp. 251–255.
   Google Scholar
• Wang Z and Bovik AC. A universal image quality index. IEEE Signal Process. Lett., 2002, 9, 81–84.
   Web of Science ®Google Scholar
• Wang Z, Bovik AC, Sheikh HR and Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 2004, 13, (4), 600–612.
   PubMed Web of Science ®Google Scholar
• Watson AB and Solomon JA. Psychophysica: Mathematica notebooks for psychophysical experiments. Spatial Vis., 1997, 10, 447–466.
   PubMedGoogle Scholar
• Piella G and Heijmans H. A new quality metric for image fusion. IEEE Conf. on Image processing, Barcelona, Spain, September 2003, pp. 173–176.
   Google Scholar
• Amintoosi M, Fathy M and Mozayani N. Video enhancement through image registration based on structural similarity. Imag. Sci. J., 2011, 59, 238–251.
   Web of Science ®Google Scholar
• Song L and Geng W. A new camouflage texture evaluation method based on WSSIM and nature image features. Proc. Int. Conf. on Multimedia technology, Ningbo, China, October 2010.
   Google Scholar
• Yang CK, Wu T.-C, Lin J.-C and Tsai W.-H. Color image sharpening by moment-preserving technique. Signal Process., 1995, 45, 397–403.
   Web of Science ®Google Scholar
• Yendrikhovskij S, MacDonald L, Bech S and Jensen K. Enhancing colour image quality in television displays. Imag. Sci. J., 1999, 47, 197–211.
   Web of Science ®Google Scholar
• Honga G and Luo MR. New algorithm for calculating perceived colour difference of images. Imag. Sci. J., 2006, 54, 86–91.
   Web of Science ®Google Scholar
• Liu HX, Xie M and Huang M. Image color-difference evaluation based on color-difference formula. Proc. 4th Int. Cong. on Image and signal processing, Shanghai, China, October 2011, pp. 1771–1774.
   Google Scholar
• Robert ME and Thomas JT. ‘The eye and night vision’, USAF Special Report, Night Vision Manual for the Flight Surgeon, 1992, AL-SR-1992-0002.
   Google Scholar
• Nouchine H, Roger BH, Tootell. Projection of rods and cones within human visual cortex. Human Brain Mapp., 2000, 9, 55–63.
   PubMed Web of Science ®Google Scholar
• Hemmendinger H. Erratum: How the CIE 1931 color-matching functions were derived from the wright–guild data. Color Res. Appl., 1998, 23, 259.
   Web of Science ®Google Scholar
• Zhang XM and Wandell BA. A spatial extension of S-CIELAB and CIEDE2000, Proc. 4th IS&T/SID Color Imaging Conf., Scottsdale, AZ, November 1996.
   Google Scholar
• Wyszecki G and Stiles WS. Color Science – Concepts and Methods, Quantitative Data and Formulae. 2000, 2nd edition (Wiley-Interscience, New York).
   Google Scholar
• Gong Y, Proietti G and Faloutsos C. Image indexing and retrieval based on human perceptual color clustering. Proc. CVPR ’98, Santa Barbara, CA, June 1998.
   Google Scholar
• Godlove IH. Improved color-difference formula, with applications to the perceptibility and acceptability fadings. J. Optical Soc. America, 1951, 41, 760–772.
   Web of Science ®Google Scholar
• Berns R.S. Billmeyer and Saltzman’s Principles of Color Technology. 2000 (John Wiley & Sons, New York).
   Google Scholar
• Uroz J, Luo R and Morovic J. Color difference perceptibility for large-size printed images. Proc. Colour Imaging Science Conf., Derby, UK, April 2000.
   Google Scholar
• Toet A. ‘Photosimulation camouflage detection test’. US Army Natick Soldier Research. Development and Engineering Center ATTN, 2009, MA 01760-5020.
   Google Scholar
• Yu DKC and Oulton DP. The application of machine vision to food and agriculture: a review. Imag. Sci. J., 2006, 57, 197–217.
   Google Scholar
• Wei X, Li J and Chen G. An image quality estimation model based on HVS. TENCON 2006 IEEE Region 10 Conf., Hong Kong, China, November 2006, pp. 1–4.
   Google Scholar
• Wei X, Li L and Chen G. A new image coding quality assessment. IEEE Asia Pacific Conf. (APCCAS 2008), Macau, China, November–December 2008, pp. 518–521.
   Google Scholar