基于偏振信息的强反射工件高光去除及视觉测量方法

Highlight Removal of High Reflectivity Workpiece and Vision Measurement Based on Polarization Information

搭建了一种基于反向传播神经网络与高斯分布函数的图像高光去除模型。该模型可以优化图像的特征提取及特征匹配。以高反射率金属工件表面作为实验对象,进行了边缘特征提取及视觉测量精度分析,实验结果表明,所提方法可实现0.75 mm的视觉测量精度,一定程度上验证了所提方法的可行性。

A model of the image highlight removal based on the back propagation neural network and the Gaussian distribution functions is established, which is beneficial to optimize the image feature extraction and the image feature matching. With the high reflectivity workpiece as the experimental object, the extraction of edge features and the analysis of the vision measurement accuracy are conducted. The experimental results show that the proposed method can be used to realize a vision measurement accuracy of 0.75 mm, which verifies the feasibility of this proposed method to a certain degree.