• Journal of IKEEE
• /
• v.18 no.2
• /
• pp.234-239
• /
• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• Industrial Engineering and Management Systems
• /
• v.8 no.3
• /
• pp.148-154
• /
• 2009

In recent days, large-sized flat-panel display (FPD) has been increasingly applied to computer monitors and TVs. Mura defects, appearing as low contrast or non-uniform brightness region, sometimes occur in manufacturing of the Thin-Film Transistor Liquid-Crystal Displays (TFT-LCD). Implementation of automatic Mura inspection methods is necessary for TFT-LCD production. Various existing Mura detection methods based on regression diagnostics, surface fitting and data transformation have been presented with good performance. This paper proposes an efficient Mura detection method that is based on a regression diagnostics using studentized residuals for automatic Mura inspection of FPD. The input image is estimated by a linear model and then the studentized residuals are calculated for filtering Mura regions. After image dilation, the proposed threshold is determined for detecting the non-uniform brightness region in TFT-LCD by means of monitoring the every pixel in the image. The experimental results obtained from several test images are used to illustrate the effectiveness and efficiency of the proposed method for Mura detection.

• Journal of Information Display
• /
• v.12 no.3
• /
• pp.121-124
• /
• 2011

The mechanism for lattice-shaped mura defects was proposed by characterizing the electro-optic properties of liquid crystal (LC), which showed different transmission properties between the normal and mura defect areas. An increase in the mura defect rate was observed when the dotted LC in the one drop filling (ODF) was exposed for a longer time. The dotted LC droplet at the edge evaporated more rapidly than that in the center. This resulted in a higher concentration of polar singles at the edge of the dotted LC droplet, leading to a higher ${\Delta}n$ value and higher transmittance. This implies that the reductio of the exposure time of the dotted LC to air plays a critical role in decreasing the occurrence of lattice-shaped mura defects in ODF.

• Journal of Broadcast Engineering
• /
• v.18 no.4
• /
• pp.525-534
• /
• 2013

Recently, mura defect inspection techniques are receiving attention in LCD production procedure since demands of TFT-LCD are growing. In this paper, we propose an automatic mura defect inspection method using gabor wavelet transform and DCT. First, we generate a reference panel image using DCT based method. For original panel image and generated reference panel image, we apply a gabor wavelet transform to eliminate texture information in images. Then, we extract mura defect regions from the difference image between gabor wavelet transform image of original panel and generated reference panel image. Finally, all mura defect regions are quantified to detect accurate mura defects. Experimental results show that our method is more accurate and efficient than previous methods.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.347-350
• /
• 2003

In this paper, a new automated defects detection method for a TFT-LCD panel is presented. An input image is preprocessed to lessen small abnormal noises and non-uniformity of the image. The adaptive multi-thresholds are used to detect Muras, which are the major defects occurred on TFT-LCD panels. Those are determined adaptively depending on the brightness and the brightness distribution of a local block. For the synthetic images and real Mura images, the proposed algorithm can effectively detect Muras in a reasonable time.

• Journal of Institute of Control, Robotics and Systems
• /
• v.14 no.5
• /
• pp.415-419
• /
• 2008

Detecting and correcting defects on LCD glasses early in the manufacturing process becomes important for panel makers to reduce the manufacturing costs and to improve productivity. Many attempts have been made and were successfully applied to detect and identify simple defects such as scratches, dents, and foreign objects on glasses. However, it is still difficult to robustly detect low-contrast defect region, called Mura or blemish area on glasses. Typically, these defect areas are roughly defined as relatively large, several millimeters of diameter, and relatively dark and/or bright region of low Signal-to-Noise Ratio (SNR) against background of low-frequency signal. The aim of this article is to present a robust algorithm to segment these blemish defects. Early 90's, a highly robust estimator, known as the Model-Fitting (MF) estimator was developed by X. Zhuang et. al. and have been successfully used in many computer vision application. Compared to the conventional Least-Square (LS) estimator the MF estimator can successfully estimate model parameters from a dataset of contaminated Gaussian mixture. Such a noise model is defined as a regular white Gaussian noise model with probability $1-\varepsilon$ plus an outlier process with probability $varepsilon$. In the sense of robust estimation, the blemish defect in images can be considered as being a group of outliers in the process of estimating image background model parameters. The algorithm developed in this paper uses a modified MF estimator to robustly estimate the background model and as a by-product to segment the blemish defects, the outliers.

• Journal of Institute of Control, Robotics and Systems
• /
• v.15 no.8
• /
• pp.761-771
• /
• 2009

Detecting defects on FPD (Flat Panel Display) color filter before the full panel is made is important to reduce the manufacturing cost. Among many types of defects, the low contrast blemish such as Suzi Mura is difficult to detect using standard CCD cameras. Even skilled inspectors in the inspection line can hardly identify such defects using bare eyes. To overcome this difficulty, point spectrometer has been used to analyze the spectrum to differentiate such defects from normal color filters. However, scanning ever increasing-size color filters by a point spectrometer takes too long time to be used in real production line. We propose a system using a spectral camera which can be viewed as a line scan camera composed of an array of point spectrometers. Three types of lighting system that exhibit different illumination spectrums are devised together with a calibration method of the proposed spectral camera system. To visualize the defect areas, various processing algorithms to identify and to enhance the small differences in spectrum between defective and normal areas are developed. Experiments shows 85% successful visualization. of real samples using the proposed system.

• 한국정보디스플레이학회:학술대회논문집
• /
• 2004.08a
• /
• pp.328-330
• /
• 2004

In order to stabilize the rubbing process for liquid crystal panels, the authors developed "the sparkling dot area ratio evaluation method". This method quantifies the fiber length dispersion of rubbing cloths, which is a major cause of mura defects. The newly developed method enables quantitative evaluation of rubbing cloths and contributes to the improvement of rubbing process uniformity.

• Journal of KIISE:Software and Applications
• /
• v.34 no.11
• /
• pp.953-960
• /
• 2007

To correctly detect muras, visual defects in TFT-LCD panels, image distortion occurring on the profess of capturing panels should be corrected. In general vision systems, there are several known methods to restore the observed image. However, the vignetting effect particularly shown only in panel images cannot be easily restored through traditional methods because it is combined with background non-uniformity due to the unique characteristic of panel. To increase the reliability of image restoration, the vignetting effect should be properly corrected after being separated from image background. Therefore, in this paper we present a new method to analyze and correct the vignetting effect of panel images using principal component analysis. Experimental results for a total of 175 test images showed that the average contrast error of the muras in the distorted images was reduced from 37% to 11% and the mura misidentification rate was decreased from 14.8% to 2.2% by image restoration.

• 한국정보디스플레이학회:학술대회논문집
• /
• 2008.10a
• /
• pp.1269-1271
• /
• 2008

We find some array mura are caused by S/D bridge or channel open in 4 mask process. The gray tone PR thickness non-uniformity is the main reason of these defects. By the adjustment of exposure and dry etch parameters, S/D bridge and channel open ratio drops by 10.87%.