Colour vision model-based approach for segmentation of traffic signs

Authors:
Xiaohong Gao;Kunbin Hong;Peter Passmore;Lubov Podladchikova;Dmitry Shaposhnikov
Affiliations:
School of Computing Science, Middlesex University, The Burroughs, Hendon, London, UK;School of Computing Science, Middlesex University, The Burroughs, Hendon, London, UK;School of Computing Science, Middlesex University, The Burroughs, Hendon, London, UK;Laboratory of Neuroinformatics of Sensory and Motor Systems, A.B. Kogan Research Institute for Neurocybernetics, Rostov State University, Rostov-on-Don, Russia;Laboratory of Neuroinformatics of Sensory and Motor Systems, A.B. Kogan Research Institute for Neurocybernetics, Rostov State University, Rostov-on-Don, Russia
Venue:
Journal on Image and Video Processing - Color in Image and Video Processing
Year:
2008

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Abstract

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.