基于先验知识的微型零件检测中异物伪信息的剔除

微型零件视觉检测中，视场内灰尘、发屑等异物的存在会改变所提取的目标轮廓。为避免异物对测量带来的影响，提出了一种基于先验知识思想的异物伪信息剔除方法。首先对带有异物的零件图像进行角点检测；接着统计得出标准零件的角点分布特征作为先验知识；最后由标准零件角点特征得出异物伪信息判定条件，据此剔除异物伪信息。通过在实际工程项目中的成功应用，以三幅典型带异物微型零件图像的处理过程为例，证明了算法在保证测量精度的同时有效剔除了图像中的异物伪信息。

Culling of foreign matter fake information in detection of subminiature accessory based on prior knowledge

In visual detection of subminiature accessory, the extracted target contour will be affected by the existence of foreign matter in the field like dust and hair crumbs. In order to avoid the impact for measurement brought by foreign matter, a method of culling foreign matter fake information based on prior knowledge was put forward. Firstly, the corners of component image with foreign matter were detected. Secondly, the corner-distribution features of standard component were obtained by statistics. Finally, the judgment condition of foreign matter fake imformation was derived from the corner-distribution features of standard component to cull the foreign matter fake information. Through successful application in an actual engineering project, the processing experiments on three typical images with foreign matter prove that the proposed algorithm ensures the accuracy of the measurement, while effectively culling the foreign matter fake information in the images.