Colour vision model-based approach for segmentation of traffic signs
Article
Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. 2008. Colour vision model-based approach for segmentation of traffic signs. EURASIP Journal on Image and Video Processing. 2008. https://doi.org/10.1155/2008/386705
Type | Article |
---|---|
Title | Colour vision model-based approach for segmentation of traffic signs |
Authors | Gao, X., Hong, K., Passmore, P., Podladchikova, L. and Shaposhnikov, D. |
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. |
Research Group | Artificial Intelligence group |
Publisher | Hindawi |
Journal | EURASIP Journal on Image and Video Processing |
ISSN | |
Electronic | 1687-5176 |
Publication dates | |
2008 | |
Publication process dates | |
Deposited | 21 Feb 2012 |
Output status | Published |
Publisher's version | License |
Copyright Statement | 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. |
Digital Object Identifier (DOI) | https://doi.org/10.1155/2008/386705 |
Scopus EID | 2-s2.0-41249085710 |
Web of Science identifier | WOS:000207762400001 |
Language | English |
https://repository.mdx.ac.uk/item/83845
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