A Defect Inspection Method in TFT-LCD Panel Using LS-SVM

Abstract

Normally, to extract the defect in TFT-LCD inspection system, the image is obtained by using line scan camera or area scan camera which is achieved by CCD or CMOS sensor. Because of the limited dynamic range of CCD or CMOS sensor as well as the effect of the illumination, these images are frequently degraded and the important features are hard to decern by a human viewer. In order to overcome this problem, the feature vectors in the image are obtained by using the average intensity difference between defect and background based on the weber's law and the standard deviation of the background region. The defect detection method uses non-linear SVM (Supports Vector Machine) method using the extracted feature vectors. The experiment results show that the proposed method yields better performance of defect classification methods over conveniently method.

Pattern Recognition of Ship Navigational Data Using Support Vector Machine

“…An SVM is a tool used for classifying data consisting of a hyperplane, and generates the maximum margin among the data using a supervised learning method [12][13][14]. An SVM was originally developed for solving classification problems, but has recently been extended to address problems associated with regression and probability density estimations [15][16][17].…”