| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 95 === There is a large category of defects, called mura, in TFT-LCD manufacturing. Mura appears as a low-contrast and non-uniform brightness region in the image. The human inspectors are unable to distinguish between the normal regions and mura effectively. A mura defect in the two-dimensional image has low contrast to the background, and shows no clear edges from its surrounding neighborhood. An enhanced image of the LCD surface only intensifies the intrinsic non-uniformity of the LCD panel, which makes the detection task even more difficult. When mura is scanned horizontally or vertically in the image, the gray-level profile of a faultless 1D image appears as a straight line, and the defective 1D image of mura has a distinct oscillation. Therefore, this study proposes a one-dimensional machine vision scheme to detect defects in low-contrast TFT-LCD panel images.
The proposed method in this research involves two detection stages. The first detection stage utilizes a fast line fitting to detect anomalous 1D signal. Then the suspected defect signal is further verified, and the location of the defect is extracted in the second detection stage using empirical mode decomposition (EMD). EMD is used to generate a collection of intrinsic mode functions (IMF) and a residue. The IMF in each cycle involves only one mode of oscillation of the input signal. It is locally mono-frequent. The residue represents the global trend of the primitive signal. The second detection stage utilizes IMFs and the residue to identify the location of mura defects. In this research, 19 true LCD panel images have been evaluated. The experimental results show that the proposed method can effectively detect various mura defects including spot-, gravity-, ring- and line-mura, yet no defects are declared for all faultless LCD images. The proposed method in its present form is very computationally fast in the first detection stage. However, it is computationally intensive in the second stage due to the sifting process of EMD.
|
|---|
