%0 Journal Article
%T Features learning method for PCB assembling defects inspection based on statistical analysis<br>基于统计分析的PCB组装缺陷特征学习方法*
%A KUANG Yong-cong
%A OUYANG Gao-fei
%A XIE Hong-wei
%A HONG Shi-liang
%A YANG Jin-rong
%A <br>邝泳聪
%A 欧阳高飞
%A 谢宏威
%A 洪始良
%A 杨锦荣
%J 计算机应用研究
%D 2010
%I 
%X 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.
%K automatic optical inspection (AOI)
%K statistical learning
%K Bayesian decision
%K defects inspection<br>自动光学检测
%K 统计学习
%K 贝叶斯决策
%K 缺陷检测
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=D96AB8E3FFA4188BF1251944AC0A481A&yid=140ECF96957D60B2&vid=DB817633AA4F79B9&iid=0B39A22176CE99FB&sid=FA519F4FF622280A&eid=583C993D4E08F5CE&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=8