• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Conference on Mathematics of Data/Image Pattern Recognition, Compression and Encryption with Applications >Automatic inspection of small component on loaded PCB based on SVD and SVM
【24h】

Automatic inspection of small component on loaded PCB based on SVD and SVM

机译：基于SVD和SVM的装载PCB上的小型分量的自动检查

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Automatic inspection of small components on loaded Printed Circuit Board (PCB) is difficult due to the requirements of precision and high speed. In this paper, an automatic inspection method is presented based on Singular Value Decomposition (SVD) and Support Vector Machine (SVM). For the image of loaded PCB, we use prior location of component to get approximate region of the small component. Then the accurate numeral region of the small component can be segmented by using the projection data of this region. Next, Singular Values (SVs) of the numeral region can be obtained through SVD of the gray image. These SVs are used as the features of small component to train a SVM classifier. Then, the automatic inspection can be completed by using trained SVM classifier. The method based on projection data can overcome some difficulties of traditional method using connected domain, and reduce complexity of template matching. The SVD avoids using binary image to analyze the numerals, so the numeral information is retained as much as possible. Finally, the experimental results prove that the method in this paper is effective and feasible to some extent.
机译：由于精度和高速的要求，难以自动检查加载的印刷电路板上的小部件（PCB）。本文基于奇异值分解（SVD）和支持向量机（SVM）来提出自动检查方法。对于加载PCB的图像，我们使用组件的前面位置来获得小组件的近似区域。然后，可以通过使用该区域的投影数据来分割小部件的精确标号。接下来，可以通过灰度图像的SVD获得数字区域的奇异值（SV）。这些SVS用作培训SVM分类器的小组件的特征。然后，可以使用训练的SVM分类器完成自动检查。基于投影数据的方法可以克服使用连接域的传统方法的一些困难，并降低模板匹配的复杂性。 SVD避免使用二进制图像来分析数字，因此数字信息尽可能多地保持。最后，实验结果证明了本文的方法在一定程度上是有效和可行的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号