Supervised Object Class Colour Normalisation

  • Ekaterina Riabchenko
  • Jukka Lankinen
  • Anders Glent Buch
  • Joni-Kristian Kämäräinen
  • Norbert Krüger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)


Colour is an important cue in many applications of computer vision and image processing, but robust usage often requires estimation of the unknown illuminant colour. Usually, to obtain images invariant to the illumination conditions under which they were taken, color normalisation is used. In this work, we develop a such colour normalisation technique, where true colours are not important per se but where examples of same classes have photometrically consistent appearance. This is achieved by supervised estimation of a class specific canonical colour space where the examples have minimal variation in their colours. We demonstrate the effectiveness of our method with qualitative and quantitative examples from the Caltech-101 data set and a real application of 3D pose estimation for robot grasping.


  1. 1.
    Alata, O., Quintard, L.: Is there a best color space for color image characterization or representation based on multivariate gaussian mixture model? CVIU 113, 867–877 (2009)Google Scholar
  2. 2.
    Buch, A.G., Kraft, D., Kämäräinen, J.K., Petersen, H.G., Krüger, N.: Pose estimation using local structure-specific shape and appearance context. In: ICRA (accepted, 2013)Google Scholar
  3. 3.
    Chong, H., Gortler, S., Zickler, T.: The von Kries hypothesis and a basis for color constancy. In: ICCV (2007)Google Scholar
  4. 4.
    Cootes, T., Taylor, C., Cooper, D., Graham, J.: Active shape models – their training and application. Computer Vision and Image Understanding 61(1) (1995)Google Scholar
  5. 5.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories. In: CVPR Workshop on Generative-Model Based Vision (2004)Google Scholar
  6. 6.
    Foster, D.: Color constancy. Vision Research 51, 341–349 (2010)Google Scholar
  7. 7.
    Gijsenij, A., Gevers, T., van de Weijer, J.: Computational color constancy: Survey and experiments. IEEE Trans. on Image Processing 20(9) (2011)Google Scholar
  8. 8.
    Hansen, T., Olkkonen, M., Walter, S., Gegenfurtner, K.R.: Memory modulates color appearance. Nature Neuroscience 9(11), 1367–1368 (2006)CrossRefGoogle Scholar
  9. 9.
    Ho-Phuoc, T., Guyader, N., Landragin, F., Guerin-Dugue, A.: When viewing natural scenes, do abnormal colors impact on spatial or temporal parameters of eye movements? Journal of Vision 12((2):4) (2012)Google Scholar
  10. 10.
    Kamarainen, J.K., Ilonen, J.: Learning and detection of object landmarks in canonical object space. In: 20th Int. Conf. on Pattern Recognition, ICPR 2010 (2010)Google Scholar
  11. 11.
    Kang, S., Kapoor, A., Lischinski, D.: Personalization of image enhancement. In: CVPR (2010)Google Scholar
  12. 12.
    Kasper, A., Xue, Z., Dillmann, R.: The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics. The International Journal of Robotics Research 31, 927–934 (2012)CrossRefGoogle Scholar
  13. 13.
    Kim, S., Pollefeys, M.: Robust radiometric calibration and vignetting correction. PAMI 30(4) (2008)Google Scholar
  14. 14.
    Moerland, T., Jurie, F.: Learned color constancy from local correspondences. In: IEEE Int. Conf. on Multimedia & Expo, ICME (2005)Google Scholar
  15. 15.
    Obdrzalek, S., Matas, J., Chum, O.: On the interaction between object recognition and colour constancy. In: ICCV (2003)Google Scholar
  16. 16.
    Reinhard, E., Ashikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21 (2001)Google Scholar
  17. 17.
    Umeyama, S.: Least-squares estimation of transformation parameters between two point patterns. IEEE PAMI 13(4), 376–380 (1991)CrossRefGoogle Scholar
  18. 18.
    van de Weijer, J., Schmid, C., Verbeek, J.: Using high-level visual information for color constancy. In: ICCV (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ekaterina Riabchenko
    • 1
  • Jukka Lankinen
    • 1
  • Anders Glent Buch
    • 2
  • Joni-Kristian Kämäräinen
    • 3
  • Norbert Krüger
    • 2
  1. 1.Lappeenranta University of Technology (Kouvola Unit)Finland
  2. 2.Mærsk McKinney Møller InstituteUniversity of Southern DenmarkDenmark
  3. 3.Department of Signal ProcessingTampere University of TechnologyFinland

Personalised recommendations