We propose an evaluation system that assigns each line-type thin-film transistor liquid-crystal display defect a corresponding level that objectively agrees with human visual perception. By “objective,” we mean that the evaluation corresponds, on average, with the assessment of a group of inspectors. The basic idea is to use the human visual perception to evaluate defects. Crucial features of defects are selected to represent the human visual perception for the line-type defect. In the process, we define the “just-noticeable difference surface” (JND) and evaluate the level of defect as the distance from a feature point consisting of selected features of the JND.
The thin film transistor liquid crystal display (TFT-LCD) has become an actively used front of panel display
technology with an increasing market. Intrinsically there is a region of non uniformity with low contrast that to human
eye is perceived as a defect. Because the grey-level difference between the defect and the background is small, the
conventional edge detection techniques are hardly applicable to detect these low contrast defects. Although several effort
were dedicated in classifying the patterned TFT-LCD defects, only few researches were conducted on detecting the unpatterned
TFT-LCD defects that accounts for approximately 15% of all defects produced during the manufacturing stages. This paper proposes a detection method for the un-patterned TFT-LCD defects by using the directional filter bank (DFB), Shen-Castan filter and maximum Feret's diameter. The effectiveness of the proposed method is tested through
the experiment using real TFT-LCD panel images.
Recently, with an increasing FPD market, automatic detection of the mura in the manufacturing process has become a critical issue for manufactures interested in increasing their TFT-LCD quality. But segmentation based detection algorithms deviate from human visual perception model. To supplement the detection error produced by deviation, the mura is re-inspected through a visual inspection during manufacturing process. If we could objectively quantify each mura's defect degree, then based on some threshold of defect degree, we could reduce the number of re-inspection. We call this degree line muras defect level. Our approach is an attempt to quantify the ideal defect level of line mura, that for each individual could vary because of subjectivity, based on multiple features crucial in the detection of line mura. In the process, we approximated what we call JND surface that passes through the middle of feature points with mean
mura visibility of 0.5. Then Index function, which measures distance from JND surface, is employed to measure the objective defect level of each candidate mura.