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.
TFT-LCD generally has the intrinsic non-uniformity due to the variance of the backlight. The region that has the perceptible non-uniformity is defined as a defect, called area-mura. In this paper, we present a new segmentation method for detecting area-mura. We first extract candidates of area-muras using regression diagnostics and then select the real area-muras among those candidates based on the size and SEMU index, a measure of contrast based on human brightness perception. Performance of the presented method has been evaluated on those TFT-LCD panel samples provided by Samsung Electronics Co., Ltd.