Colour Vision Model-Based Approach for Segmentation of Traffic Signs

被引:15
作者
Gao, Xiaohong [1 ]
Hong, Kunbin [1 ]
Passmore, Peter [1 ]
Podladchikova, Lubov [2 ]
Shaposhnikov, Dmitry [2 ]
机构
[1] Middlesex Univ, Sch Comp Sci, London NW4 4BT, England
[2] Rostov State Univ, Lab Neuroinformat Sensory & Motor Syst, AB Kogan Res Inst Neurocybernet, Rostov Na Donu 344090, Russia
基金
俄罗斯基础研究基金会;
关键词
D O I
10.1155/2008/386705
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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. Copyright (C) 2008 Xiaohong Gao et al.
引用
收藏
页数:7
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