关键词: 偏光片, 缺陷检测, LBP描述符, 支持向量机（SVM）, 主成分分析（PCA）, 线性判别分析（LDA）

Abstract:

The visual defects of polarizer have a direct and crucial effect on the quality of TFT-LCD panels. In order to improve the recognition accuracy of polarizer defect image, the recognition method based on improved Local Binary Pattern（LBP） descriptor and Support Vector Machine（SVM） is proposed. Firstly, the defect image is obtained by using dark field imaging principle. For each defect image, it is divided into different regions, and the LBP features are extracted from each region to form high-dimensional composite features. The pixel mean features of different partitions are integrated with the LBP composite features. With the help of Principal Component Analysis（PCA）, the correlation and noise between the features are eliminated. Then the Linear Discriminant Analysis（LDA） is used to further project and transform to low-dimensional features. Finally, the support vector machine is utilized to classify the above features. The advantages of improved LBP descriptor, PCA, LDA and SVM are combined, and simulation experiments are carried out in a total of 250 database. The results show that the classification accuracy by using the improved LBP is 99.2%, and the recognition time is 0.92 s, thus meeting the practical application requirement of industrial production line.

Key words: polarizer, defect detection, LBP descriptor, Support Vector Machine（SVM）, Principal Component Analysis（PCA）, Linear Discriminant Analysis（LDA）