摘要
采用多尺度轮廓结构元素的开运算和闭运算去除噪声,小尺度轮廓结构元素提取图像的梯度图像,根据仿生学原理,通过分析韦伯比曲线,将图像根据背景亮度划分为低暗区、中间区和高亮区,分别采用不同的公式计算边缘阈值来分割图像;采用提出的方法对不同特征图像进行了检测,并对几种常见的边缘检测方法做了对比。研究结果表明,与常见的几种方法相比较,提出的路面裂缝检测算法检测结果更好,可以有效地从路面图像中提取裂缝。
This paper uses opening and closing operations based on multi-scale contour structuring elements to remove noise, and grade image is obtained by the smallest size contour structuring. An image can be divided into low dark area, middle area and high bright area through Weber＇s law according to bionics principle and background brightness. The edge thresholds for the different areas are calculated according to the different equations respectively. The proposed crack edge detector is tested on popular images having different image properties and also compared with popular edge detectors from the literature. Experimental results show that the proposed crack edge detector exhibits much better performance than the one of competing operators, and may efficiently be used for the detection of crack in digital pavement images. 2 tabs, 5 figs, 17 refs.
出处
《长安大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第5期21-24,共4页
Journal of Chang’an University(Natural Science Edition)
基金
国家西部交通建设科技项目(200431881213)
关键词
道路工程
裂缝检测
视觉模型
数学形态学
轮廓结构元素
road engineering
crack detection
visual model
morphology
contour structuring element
