一种机械零件图像边缘特征的提取方法

为了在嵌入式系统中实现对零件的有效分类,针对机械零件边缘特征比较明显的特点,提出了一种机械零件图像边缘特征的提取方法.首先采用Kirsch算子提取零件二值图像的边缘,然后以零件质心为中心将边缘图像划分为若干个子区域,并对各子区域分别计算出其修正的归一化中心矩,并将以此形成的行向量作为零件分类识别的特征.实验分析中采用K均值聚类算法对提取的零件边缘特征进行分类,实验结果验证了该方法的有效性.

A Method of Extracting Edge Feature of Mechanical Component Image

In order to effectively classify mechanical components in embedded system, a novel method was proposed to extract edge features of mechanical component images since mechanical components have comparatively obvious edge features. First, the Kirsch operator was used to extract the edge of mechanical component binary image. Then the edge image was divided into several areas with the centroid as the center, and the modified normalized central moments of each area were calculated. The vectors including these central moments were constructed to be the edge features of mechanical components. In the experiments, the means clustering algorithm was used to classify the components based on the extracted edge features, and the effectiveness was proved by the results.