摘要
为了对道路标志图像进行自动分类,通过图像颜色空间变换,将图像的RGB量值转换为H(色度)S(饱和度)I(亮度)量值,采用Sobel算子进行道路标志图像的边缘检测,利用行扫描法进行区域填充,以获取二值化的道路标志图像区域,提取道路标志二值化图像的不变矩与形状参数作为图像特征值,设计BP神经网络道路标志图像几何形状分类器,以道路标志图像的H、I为特征值,设计了欧式距离分类器,实现道路标志背景颜色的识别。融合道路标志图像几何形状和背景颜色的识别算法,并利用道路标志的分类知识和自动分类方法,能有效实现道路标志图像的自动识别。
To achieve automatic classifying traffic sign, a sign classifying method was put forward, sign HSI color model was obtained from sign RGB model, the edge of traffic sign image was detected by Sobel operator, sign region was filled by line-scanning method to gain the binary image of traffic sign, the invariant-moments and form factors of traffic sign image were extracted as image feathers, the shape of traffic sign was recognized by BP nerve net classifier, a euclid- distance classifier was designed to recognize the background color of traffic sign by H and I values. Automatic classifying result shows that the shape and color of traffic sign can be recognised accurately by the method. 3 tabs, 5 figs, 10 refs.
出处
《交通运输工程学报》
EI
CSCD
北大核心
2006年第4期91-95,111,共6页
Journal of Traffic and Transportation Engineering
基金
国家自然科学基金项目(50578128)
高等学校博士学科点专项科研基金项目(20050497009)
关键词
交通安全
道路标志
安全辅助驾驶
图像识别
traffic safety
traffic signs
driving safety assistance
image recognition
