Important Parameters for Image Color Analysis: An Overview

  • Juliana F. S. GomesEmail author
  • Fabiana R. Leta
  • Pedro B. Costa
  • Felipe de O. Baldner
Part of the Augmented Vision and Reality book series (Augment Vis Real, volume 4)


In recent years it is noteworthy how the use of Computational Vision techniques in processing and quality control of products has advanced. The available resources in both electronic and computing were important factors in the automation development, allowing constant monitoring during the process. Such techniques have systematically evolved in the international commerce. However, there is a lack of standardization on quality control of products using image analysis. Measurements using digital image should consider important aspects, such as the effects of lighting, characteristics of the environment, the types of illuminants, the observers, to name a few, all that beyond the traceability of the system and the definition of standards. With this in mind, the aim of this chapter is to discuss the relevance of the main variables that influence the color measurement of images using computer vision techniques, in order to promote some thought about the needs of standardization.


Color analysis by image Illumination Color perception Color rendering index Color temperature 

List of Abbreviations


Computer vision systems


Light-emitting diode


Color temperature


Correlated color temperature


Charge coupled device


Complementary metal oxide semiconductor


Commission Internationale de l’Eclairage

International Commission on Illumination


Color rendering index


Spectral power distribution


Red, green, blue color system


Hue, saturation, lightness color system


Hue, saturation, intensity color system


Hue, saturation, brightness color system


Hue, saturation, value color system



The authors would like to acknowledge FAPERJ (under grants E-26/103.591/2012, E-26/103.618/2012 and E-26/171.362/2001) for its financial support. The authors would also like to acknowledge their colleagues from UFF and Inmetro for the support while conducting the experiments. They also acknowledge Dr. Ana Paula Dornelles Alvarenga and Marcelo Bezerra Guedes for the technical discussions.


  1. 1.
    Gomes, J.F.S., Leta, F.R.: Applications of computer vision techniques in the agriculture and food industry: a review. Eur. Food Res. Technol. 235, 989–1000 (2012)CrossRefGoogle Scholar
  2. 2.
    Zhendong, L., Haiye, Y., Hongnan, L., Hongxia, Z.: The application study on building materials with computer color quantification system. In: Proceeding of SPIE, vol. 6033, 603307-1 (2005)Google Scholar
  3. 3.
    Conci, A., Azevedo, E., Leta, F.R.: Computação Gráfica—Teoria e Prática, v 2. Campus Elsevier Ed., Rio de Janeiro (2008)Google Scholar
  4. 4.
    CIE 15.3: Colorimetry. CIE Publication, Vienna (2004)Google Scholar
  5. 5.
    Ling, Y., Vurro, M., Hurlbert, A.: Surface chromaticity distributions of natural objects under changing illumination. In: Proceeding of the 4th European Conference on Colour in Graphics, Imaging and Vision (CGIV), pp. 263–267 (2008)Google Scholar
  6. 6.
    CIE 13.3: Method of Measuring and Specifying Colour Rendering Properties of Light Sources. CIE Publication, Vienna (1995)Google Scholar
  7. 7.
    Luo, M.R.: The quality of light sources. Color. Technol. 127, 75–87 (2011)CrossRefGoogle Scholar
  8. 8.
    LRC—Lighting Research Center: Recommendations for Specifying Color Properties of Light Sources for Retail Merchandising. Alliance for Solid-State Illumination Systems and Technologies, vol. 8, issue 2 (2010) Google Scholar
  9. 9.
    Gomes, J.F.S., Vieira, R.R., Oliveira, I.A.A., Leta, F.R.: Influence of illumination on the characterization of banana Ripening. J. Food Eng. 120, 215–222 (2014)CrossRefGoogle Scholar
  10. 10.
    Gomes, J.F.S.: Padronização de metodologia para caracterização de cor por imagem aplicada à seleção de frutas. Doutoral Tese from Universidade Federal Fluminense (2013)Google Scholar
  11. 11.
    Mohan, L.A., Karunakaran, C., Jayas, D.S., White, N.D.G.: Classification of bulk cereals using visible and NIR reflectance characteristics. Can. Biosyst. Eng. 47, 7.7–7.14 (2005) Google Scholar
  12. 12.
    Manickavasagan, A., Sathya, G., Jayas, D.S.: Comparison of illuminations to identify wheat classes using monochrome images. Comput. Electron. Agric. 63, 237–244 (2008)CrossRefGoogle Scholar
  13. 13.
    Brown, R.O., MacLeod, D.I.A.: Color appearance depends on the variance of surround colors. Curr. Biol. 7, 844–849 (1997)CrossRefGoogle Scholar
  14. 14.
    Shevell, S.K., Wei, J.: Chromatic induction: border contrast or adaptation to surrounding light? Vision. Res. 38, 1561–1566 (1998)CrossRefGoogle Scholar
  15. 15.
    Sánchez-Zapata, E., Fuentes-Zaragoza, E., Vera, C.N.R., Sayas, E., Sendra, E., Fernández-López, J., Pérez-Alvarez, J.A.: Effects of tuna pâté thickness and background on CIEL*a*b* color parameters and reflectance spectra. Food Control 22, 1226–1232 (2011)CrossRefGoogle Scholar
  16. 16.
    Dobrzański Jr, B., Rybczyński, R.: Influence of packing method on colour perception improving the appearance of fruits and vegetables. Res. Agric. Eng. 54(2), 97–103 (2008)Google Scholar
  17. 17.
    Meléndez-Martínez, A.J., Vicario, I.M., Heredia, F.J.: Correlation between visual and instrumental colour measurements of orange juice dilutions: effect of the background. Food Qual. Prefer. 16, 471–478 (2005)CrossRefGoogle Scholar
  18. 18.
    Blasco, J., Cubero-García, S., Alegre-Sosa, S., Gómez-Sanchís, J., López-Rubira, V., Moltó, E.: Automatic inspection of the pomegranate (Punica granatum L.) arils quality by means of computer vision. Span. J. Agric. Eng. 6(1), 12–16 (2008)Google Scholar
  19. 19.
    Gomes, J.F.S., Vieira, R.R., Leta, F.R.: Colorimetric indicator for classification of bananas during ripening. Sci. Hortic. (2013). doi: 10.1016/j.scienta.2012.11.014 Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Juliana F. S. Gomes
    • 1
    Email author
  • Fabiana R. Leta
    • 2
  • Pedro B. Costa
    • 1
  • Felipe de O. Baldner
    • 1
  1. 1.Instituto Nacional de Metrologia, Qualidade e Tecnologia—InmetroDuque de CaxiasBrazil
  2. 2.Dimensional and Computational Metrology Laboratory, Mechanical Engineering DepartmentUniversidade Federal Fluminense—UFFNiteróiBrazil

Personalised recommendations