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Intelligent Modification of Colors in Digitized Paintings for Enhancing the Visual Perception of Color-blind Viewers

  • Paul Doliotis
  • George Tsekouras
  • Christos-Nikolaos Anagnostopoulos
  • Vassilis Athitsos
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 296)

Abstract

Color vision deficiency (CVD) is quite common since 8%–12% of the male and 0.5% of the female European population seem to be color-blind to some extent. Therefore there is great research interest regarding the development of methods that modify digital color images in order to enhance the color perception by the impaired viewers. These methods are known as daltonization techniques. This paper describes a novel daltonization method that targets a specific type of color vision deficiency, namely protanopia. First we divide the whole set of pixels into a smaller group of clusters. Subsequently we split the clusters into two main categories: colors that protanopes (persons with protanopia) perceive in a similar way as the general population, and colors that protanopes perceive differently. The color clusters of the latter category are adapted in order to improve perception, while ensuring that the adapted colors do not conflict with colors in the first category. Our experiments include results of the implementation of the proposed method on digitized paintings, demonstrating the effectiveness of our algorithm.

Keywords

Color Vision Color Perception Color Cluster Digital Color Image Human Visual System Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    C. Anagnostopoulos, I. Anagnostopoulos, G. Tsekouras, and C. Kalloniatis. Intelligent modification for the daltonization process of digitized paintings. In International Conference on Computer Vision Systems, 2007.Google Scholar
  2. 2.
    J.C. Bezdek and S.K. Pal. Fuzzy models for pattern recognition. methods that search for patterns in data. IEEE Press, 11:539, 1992.Google Scholar
  3. 3.
    Bob Dougherty and Alex Wade. Vischeck site. http://www.vischeck.com, last date of access 11/02/08.
  4. 4.
    Onur Fidaner, Poliang Lin, and Nevran Ozguven. http://scien.stanford.edu/class/psych221/projects/05/ofidaner/project report.pdf, last date of access 11/02/08.
  5. 5.
    Klir G.J. Principles of uncertainty: What are they? why do we. need them? Fuzzy Sets and Systems, 74(1):15–31, 1995.CrossRefGoogle Scholar
  6. 6.
    Bruce Gray. Bruce Gray site. http://www.brucegray.com, last date of access 11/02/08.
  7. 7.
    V.A. Kovalev. Towards image retrieval for eight percent of color-blind men. In International Conference on Pattern Recognition, volume 2, pages 943–946, 2004.Google Scholar
  8. 8.
    Curtis E. Martin, J. O. Keller, Steven K. Rogers, and Matthew Kabrisky. Color blindness and a color human visual system model. IEEE Transactions on Systems, Man, and Cybernetics, Part A, 30(4):494–500, 2000.CrossRefGoogle Scholar
  9. 9.
    Jeho Nam, Yong Man Ro, Youngsik Huh, and Munchurl Kim. Visual content adaptation according to user perception characteristics. IEEE Transactions On Multimedia, 7(3):435–445, 2005.CrossRefGoogle Scholar
  10. 10.
    N. R. Pal and J. C. Bezdek. On clustering validity for the fuzzy c-means model. IEEE Transactions on Fuzzy Systems, 3:370–379, 1995.CrossRefGoogle Scholar
  11. 11.
    F. Vinot, H. Brettel, and J. D. Mollon. Digital video colourmaps for checking the legibility of displays by dichromats. Color Research and Application, 24(4):243–252, 1999.CrossRefGoogle Scholar
  12. 12.
    Seungji Yang and Yong Man Ro. Visual contents adaptation for color vision deficiency. In International Conference on Image Processing, volume 1, pages 453–456, 2003.Google Scholar
  13. 13.
    Lotfi A. Zadeh. Fuzzy sets. Information and Control, 8(3):338–353, 1965.MathSciNetCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2009

Authors and Affiliations

  • Paul Doliotis
    • 1
  • George Tsekouras
    • 2
  • Christos-Nikolaos Anagnostopoulos
    • 2
  • Vassilis Athitsos
    • 1
  1. 1.Computer Science and Engineering DepartmentUniversity of Texas at ArlingtonUSA
  2. 2.Department of Culture, Technology, and CommunicationUniversity of the AegeanGreece

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