基于聚类分析的管路图像亚像素边缘提取算法

Cluster analysis based sub-pixel edge extraction for tube's image

为了保证管路的加工精度,实现无应力装配,在管路加工后需要测量其三维尺寸。基于机器视觉的管路测量方法由于具有速度快、精度高的特点,越来越广泛地应用在管路三维测量领域。针对该技术中传统边缘提取方法难以准确获得管路边缘的问题,提出一种在复杂光照环境下,快速准确提取管路亚像素精度边缘的方法。首先利用频域滤波滤除噪声,聚类分析细致分割管路区域;然后应用图像形态学提取边缘初值区域,根据局部区域灰度变化求解边缘变化模型;最终实现了管路图像亚像素精度边缘提取,消除了噪声对边缘提取的影响。实验证明,利用本文提取的亚像素边缘,准确可靠,且精度达到0.04个像素尺寸,能够在管路三维重建中提供精确的管路边缘信息。

To promise stress-free and precise assembly for bend tubes,the geometric parameters shoule be measured during the manufacturing stage.Machine vision based measurement for bend tube is a rapid and accurate method,which is widely used in the field of 3D measurement.Aiming at the problem that the traditional extraction in this method could not obtain pipeline edge accurately,a sub-pixel edge extraction method for tube s image even was proposed under a complex illumination condition.The extraction was explained in four steps:the low-frequency noises were filtered with spectral filtering method,and a clustering analysis method was applied to segment the tube s region precisely;the pixel edge was obtained with image morphology method.The surface fitting method was employed to fitting the variation of local gray values to realize the sub-pixel edge points.Experiments results showed that the method could extract sub-pixel edge accurately and reliably.The accuracy reached 0.04 pixel width,which could provide the accurate edge for reconstructing tube s 3D model.