Advertisement

An Evolutionary Framework for Colorimetric Characterization of Scanners

  • Simone Bianco
  • Francesca Gasparini
  • Raimondo Schettini
  • Leonardo Vanneschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4974)

Abstract

In this work we present an evolutionary framework for colorimetric characterization of scanners. The problem consists in finding a mapping from the RGB space (where points indicate how a color stimulus is produced by a given device) to their corresponding values in the CIELAB space (where points indicate how the color is perceived in standard, i.e. device independent, viewing conditions). The proposed framework is composed by two phases: in the first one we use genetic programming for assessing a characterizing polynomial; in the second one we use genetic algorithms to assess suitable coefficients of that polynomial. Experimental results are reported to confirm the effectiveness of our framework with respect to a set of methods in the state of the art.

Keywords

Evolutionary Framework Color Patch Total Little Square Characterization Problem Color Scanner 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Berns, R.S., Shyu, M.J.: Colorimetric characterization of a desktop drum scanner using a spectral model. Journal of Electronic Imaging 4(4), 360–372 (1995)CrossRefGoogle Scholar
  2. 2.
    Cagnoni, S., Rivero, D., Vanneschi, L.: A purely-evolutionary memetic algorithm as a first step towards symbiotic coevolution. In: Proceedings of the 2005 IEEE Congress on Evolutionary Computation (CEC 2005), Edinburgh, Scotland, pp. 1156–1163. IEEE Press, Piscataway (2005)CrossRefGoogle Scholar
  3. 3.
    Cantu-Paz, E., Goldberg, D.E.: Are multiple runs of genetic algorithms better than one? In: Cantú-Paz, E., Foster, J.A., Deb, K., Davis, L., Roy, R., O’Reilly, U.-M., Beyer, H.-G., Kendall, G., Wilson, S.W., Harman, M., Wegener, J., Dasgupta, D., Potter, M.A., Schultz, A., Dowsland, K.A., Jonoska, N., Miller, J., Standish, R.K. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 801–812. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Cheung, T.L.V., Westland, S., Connah, D.R., Ripamonti, C.: Characterization of colour cameras using neural networks and polynomial transforms. Journal of Coloration Technology 120(1), 19–25 (2004)CrossRefGoogle Scholar
  5. 5.
    Cheung, V., Westland, S., Li, C., Hardeberg, J., Connah, D.: Characterization of trichromatic color cameras by using a new multispectral imaging technique. J. Opt. Soc. Am. A 22, 1231–1240 (2005)CrossRefGoogle Scholar
  6. 6.
    Ebner, M.: Evolving color constancy. Pattern Recognition Letters 27(11), 1220–1229 (2006)CrossRefGoogle Scholar
  7. 7.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  8. 8.
    Holland, J.H.: Adaptation in Natural and Artificial Systems. The University of Michigan Press, Ann Arbor, Michigan (1975)Google Scholar
  9. 9.
    Huffel, S.V., Vandewalle, J.: The total least squares problem: computational aspects and analysis, Society for industrial and applied mathematics, Philadelphia (1991)Google Scholar
  10. 10.
    Kang, H.R.: Computational coolor technology, vol. PM159. SPIE Press (2006)Google Scholar
  11. 11.
    Kang, H.R., Anderson, P.G.: Neural network application to color scanner and printer calibrations. Journal of Electronic Imaging 1(2), 125–135 (1992)CrossRefGoogle Scholar
  12. 12.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Ryan, C., et al. (eds.) Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, vol. 2, pp. 1137–1143 (1995)Google Scholar
  13. 13.
    Koza, J.R.: Genetic Programming. The MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  14. 14.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)Google Scholar
  15. 15.
    Schettini, R., Barolo, B., Boldrin, E.: Colorimetric calibration of color scanners by back-propagation. Pattern Recognition Letters 16(10), 1051–1056 (1995)CrossRefGoogle Scholar
  16. 16.
    Shen, H.-L., Mou, T.-S., Xin, J.H.: Colorimetric characterization of scanners by measures of perceptual color error. Journal of Electronic Imaging 15(4), 1–5 (2006)CrossRefGoogle Scholar
  17. 17.
    Shen, H.-L., Xin, J.H.: Colorimetric and spectral characterization of a color scanner using local statistics. Journal of Imaging Science and Technology 48(4), 342–346 (2004)Google Scholar
  18. 18.
    Shen, H.-L., Xin, J.H.: Spectral characterization of a color scanner by adaptive estimation. Journal of the Optical Society of America A 21(7), 1125–1130 (2004)CrossRefGoogle Scholar
  19. 19.
    Vanneschi, L., Valsecchi, A., Cagnoni, S., Mauri, G.: Heterogeneous cooperative coevolution: Strategies of integration between gp and ga. In: Keijzer, M., et al. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2006, vol. 1, pp. 361–368. ACM Press, New York (2006)CrossRefGoogle Scholar
  20. 20.
    Vrhel, M.J., Trussell, H.J.: Color scanner calibration via a neural networks. In: Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 6, pp. 3465–3468 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Simone Bianco
    • 1
  • Francesca Gasparini
    • 1
  • Raimondo Schettini
    • 1
  • Leonardo Vanneschi
    • 1
  1. 1.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversity of Milano-BicoccaMilanItaly

Personalised recommendations