The TFT-LCD panel is one of the most important and promising products in the recent years. Mura defects can be created on the display panel during its production. In this research, a linear regression diagnostic model is incorporated with digital image processing theory to automatically inspect for Mura defects. A bivariate polynomial regression model is used to simulate the brightness of background images that is used in the diagnosis of outliers and influential points. The partitions of the candidate Mura defect regions are segmented using Niblack's threshold criteria. The candidate Mura defects are further evaluated. The quantified level is defined in terms of concepts already reported in the literature. Based on Weber's law and a visual perception model, the just-noticeable intensity difference index of the Mura features can be obtained and it can be subsequently used to quantify the Mura defect level. With the obtained defect level, Mura defects can be identified for exactly labelling of the perfect and imperfect LCD panels. Experiments were performed on 13 TFT-LCD panels. There are ten bad panels and three good panels in these 13 samples as determined by human visual inspection. Each bad panel has at least one Mura defect. After the automated inspection process, the results showed that the proposed method could separate the good and bad panels accurately. Compared with human visual inspection, the Mura detection rate of the distinct size and shapes can attain over 90.9 per cent correct detection and the achieved correction rate of Mura defects on each panel can be improved by 100 per cent.
|Number of pages||13|
|Journal||Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture|
|Publication status||Published - 2008 Jan 1|
All Science Journal Classification (ASJC) codes
- Mechanical Engineering
- Industrial and Manufacturing Engineering