基于分形理论的焊缝信息提取

在基于机器视觉信息的焊缝跟踪系统中，受焊接条件的影响，所拍图像经常存在许多干扰信号，严重地影响了准确焊缝边缘位置信息的获得．针对这一问题，文中利用基于分形理论的边缘提取算法对有干扰图像中的焊缝边缘位置进行了提取．该算法利用各向同性的分数布朗随机场模型对焊缝图像进行分析，通过适当选取无标度区域求出了像素点及其邻域的分数维，并据此来区分焊缝边缘区域．文中还用该算法对自然光及弧光(混合气体保护焊)干扰条件下的焊缝图像进行了处理，结果表明，在较强的干扰条件下，此算法也能够将焊缝边缘位置信息准确提取出来，使焊缝中心位置误差不超过0．3mm．

Acquirement of Welding Seam Information Based on Fractal Theory

In the weld seam-tracking system based on machine vision, the welding seam images acquired in the welding process are mixed with strong noises because of the effect of welding conditions. This makes it difficult to get precise seam situation. In this paper, a new edge extraction algorithm based on the fractal theory was introduced to seek welding edge's position in the seam images with noises. In this algorithm, seam images are analyzed depending on the fractional Brownian field model, and the fractal dimension of some pixels and their neighborhood in seam images are calculated by selecting the scaleless range properly, thus distinguishing the seam edge area. The proposed algorithm was then applied to process the images with noises in natural light and arc light (mixed gas shielded welding). The result shows that this algorithm can be used to Correctly extract the position information of welding seam even with strong interference, with the central position error of welding seam less than 0.3mm.