Features learning method for PCB assembling defects inspection based on statistical analysis
基于统计分析的PCB组装缺陷特征学习方法*

In order to reduce the experience-dependence of automatic optical inspection (AOI),proposed a Bayesian-based features learning for PCB assembling defects inspection. By statistical learning images of good product sample and defective product sample,selected features with better ability of classification capacity, and based on the risk minimization of Bayesian,worked out the decision parameters for feature classing. Experimental results show that the proposed method effectively simplifies the programming and debugging of user inspection application, and greatly improves the efficiency and accuracy of AOI.