Unsupervised Recognition of Salient Colour for Real-Time Image Processing

  • David Budden
  • Alexandre Mendes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8371)


Humans have the subconscious ability to create simple abstractions from observations of their physical environment. The ability to consider the colour of an object in terms of “red” or “blue”, rather than spatial distributions of reflected light wavelengths, is vital in processing and communicating information about important features within our local environment. The real-time identification of such features in image processing necessitates the software implementation of such a process; segmenting an image into regions of salient colour, and in doing so reducing the information stored and processed from 3-dimensional pixel values to a simple colour class label. This paper details a method by which colour segmentation may be performed offline and stored in a static look-up table, allowing for constant time dimensionality reduction in an arbitrary environment of coloured features. The machine learning framework requires no human supervision, and its performance is evaluated in terms of feature classification performance within a RoboCup robot soccer environment. The developed system is demonstrated to yield an 8% improvement over slower traditional methods of manual colour mapping.


Computer vision colour vision robotics RoboCup LUT generation 


  1. 1.
    Bishop, C.: Pattern recognition and machine learning, vol. 4. Springer (2006)Google Scholar
  2. 2.
    Budden, D., Fenn, S., Mendes, A., Chalup, S.: Evaluation of colour models for computer vision using cluster validation techniques. In: Chen, X., Stone, P., Sucar, L.E., van der Zant, T. (eds.) RoboCup 2012. LNCS (LNAI), vol. 7500, pp. 261–272. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  3. 3.
    Budden, D., Fenn, S., Walker, J., Mendes, A.: A novel approach to ball detection for humanoid robot soccer. In: Thielscher, M., Zhang, D. (eds.) AI 2012. LNCS, vol. 7691, pp. 827–838. Springer, Heidelberg (2012)Google Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Hartigan, J., Wong, M.: Algorithm AS 136: A k-means clustering algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics) 28(1), 100–108 (1979)zbMATHGoogle Scholar
  6. 6.
    Kitano, H., Asada, M., Kuniyoshi, Y., Noda, I., Osawa, E.: Robocup: The robot world cup initiative. In: Proceedings of the First International Conference on Autonomous Agents, pp. 340–347. ACM (1997)Google Scholar
  7. 7.
    Mahalanobis, P.C.: On the generalized distance in statistics. In: Proceedings of the National Institute of Sciences of India, New Delhi, vol. 2, pp. 49–55 (1936)Google Scholar
  8. 8.
    Quinlan, M.J., Chalup, S.K., Middleton, R.H.: Application of svms for colour classification and collision detection with aibo robots. In: Advances in Neural Information Processing Systems, vol. 16 (2003)Google Scholar
  9. 9.
    Szeliski, R.: Computer vision: algorithms and applications. Springer (2010)Google Scholar
  10. 10.
    Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • David Budden
    • 1
    • 2
  • Alexandre Mendes
    • 3
  1. 1.Victoria Research LabNational ICT Australia (NICTA)Australia
  2. 2.Department of Electrical and Electronic EngineeringThe University of MelbourneParkvilleAustralia
  3. 3.School of Electrical Engineering and Computer ScienceThe University of NewcastleCallaghanAustralia

Personalised recommendations