CFP last date
22 April 2024
Reseach Article

Integrated Saturation Weighting based Color Cat Algorithm

by Karamjit Kaur Dhillon, Aarti Vaish
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 5
Year of Publication: 2016
Authors: Karamjit Kaur Dhillon, Aarti Vaish
10.5120/ijca2016910705

Karamjit Kaur Dhillon, Aarti Vaish . Integrated Saturation Weighting based Color Cat Algorithm. International Journal of Computer Applications. 146, 5 ( Jul 2016), 36-40. DOI=10.5120/ijca2016910705

@article{ 10.5120/ijca2016910705,
author = { Karamjit Kaur Dhillon, Aarti Vaish },
title = { Integrated Saturation Weighting based Color Cat Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 5 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 36-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number5/25397-2016910705/ },
doi = { 10.5120/ijca2016910705 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:49:34.763372+05:30
%A Karamjit Kaur Dhillon
%A Aarti Vaish
%T Integrated Saturation Weighting based Color Cat Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 5
%P 36-40
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Color Constancy has capability to displace the precise shades in provided picture by considering the effectation of color gentle source. Many color constancy methods has been proposed so far to boost the color constancy accuracy charge further. In present literature number this type of process can be acquired which works optimistically in many case. Although the color cat shows successful benefits around available methods, however it is still struggling with the matter of irregular illuminate and bad brightness. Thus to cope with this problem for the reason that paper a brand new integrated color cat approach is proposed for the reason that dissertation. The new approach has applied color normalization and saturation weighting as post managing of color cat to lessen the effectation of irregular illuminate and bad brightness. The general gain shows the effectiveness of the proposed technique.

References
  1. K. Barnard. Improvements to gamut mapping colour constancy algorithms. ECCV, 2000.
  2. K. Barnard, L. Martin, A. Coath, and B. Funt. A comparison of computational color constancy algorithms: Experiments with image data. TIP, 2002.
  3. H. G. Barrow and J. M. Tenenbaum. Recovering Intrinsic Scene Characteristics from Images. Academic Press, 1978.
  4. S. Bianco and R. Schettini. Color constancy using faces. CVPR, 2012.
  5. D. H. Brainard and W. T. Freeman. Bayesian color constancy. JOSA A, 1997.
  6. G. Buchsbaum. A spatial processor model for object colour perception. Journal of the Franklin Institute, 1980.
  7. A. Chakrabarti, K. Hirakawa, and T. Zickler. Color constancy with spatio-spectral statistics. TPAMI, 2012.
  8. D. Cheng, D. K. Prasad, and M. S. Brown. Illuminant estimation for color constancy: why spatial-domain methods work and the role of the color distribution. JOSA A, 2014.
  9. G. D. Finlayson, S. D. Hordley, and P. M. Hubel. Color by correlation: A simple, unifying framework for color constancy. TPAMI, 2001.
  10. D. A. Forsyth. A novel algorithm for color constancy. IJCV, 1990.
  11. P. Gehler, C. Rother, A. Blake, T. Minka, and T. Sharp. Bayesian color constancy revisited. CVPR, 2008.
  12. A. Gijsenij and T. Gevers. Color constancy using natural image statistics and scene semantics. TPAMI, 2011.
  13. A. L. Gilchrist. Seeing Black and White. Oxford University Press, 2006.
  14. J. van de Weijer, T. Gevers, and A. Gijsenij. Edge-based color constancy. TIP, 2007.
  15. G. D. Finlayson, M. S. Drew, and C. Lu, “Intrinsic images by entropy minimization,” in Computer Vision-ECCV 2004. Springer, 2004, pp. 582–595.
  16. B. Maxwell, C. Smith, and R. Friedhoff, “Method and system for separating illumination and reflectance using a log color space,” Oct. 18 2007, US Patent App. 11/403,719.
  17. Y. Bai and X. Zhang, “Transformations and White Point Constraint Solutions for a Novel Chromaticity Space,” Apr. 18 2013, US Patent App. 13/342,873.
  18. B. Mazin, J. Delon, and Y. Gousseau, “Illuminant estimation from projections on the planckian locus,” in Computer Vision–ECCV 2012. Workshops and Demonstrations. Springer, 2012, pp. 370–379.
  19. B. Zhang and A. Batur, “Method and apparatus for white balance,” Jan. 28 2010, US Patent App. 12/510,853.
  20. B. Zhang, “Method and apparatus for improving the stability of histogram correlation algorithm,” Sep. 10 2013, US Patent 8,532,381.
Index Terms

Computer Science
Information Sciences

Keywords

Color constancy Contrast Enhancement Image enhancement Adaptive Histogram Equalization method Color and Illumination Distribution.