Open Access

Colour Vision Model-Based Approach for Segmentation of Traffic Signs

  • Xiaohong Gao1Email author,
  • Kunbin Hong1,
  • Peter Passmore1,
  • Lubov Podladchikova2 and
  • Dmitry Shaposhnikov2
EURASIP Journal on Image and Video Processing20072008:386705

Received: 28 July 2007

Accepted: 11 December 2007

Published: 13 December 2007


This paper presents a new approach to segment traffic signs from the rest of a scene via CIECAM, a colour appearance model. This approach not only takes CIECAM into practical application for the first time since it was standardised in 1998, but also introduces a new way of segmenting traffic signs in order to improve the accuracy of colour-based approach. Comparison with the other CIE spaces, including CIELUV and CIELAB, and RGB colour space is also carried out. The results show that CIECAM performs better than the other three spaces with 94%, 90%, and 85% accurate rates for sunny, cloudy, and rainy days, respectively. The results also confirm that CIECAM does predict the colour appearance similar to average observers.


ColourImage ProcessingPattern RecognitionComputer VisionColour Space

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Authors’ Affiliations

School of Computing Science, Middlesex University, Hendon, UK
Laboratory of Neuroinformatics of Sensory and Motor Systems, A.B. Kogan Research Institute for Neurocybernetics, Rostov State University, Rostov-on-Don, Russia


© Xiaohong Gao et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.