Region Mura Detection using Efficient High Pass Filtering based on Fast Average Operation

Kim, Seong Hoon; Kang, Tae Gyu; Jeong, Dae Hwa

doi:10.3182/20080706-5-kr-1001.01386

Cited by 7 publications

(7 citation statements)

References 2 publications

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“…For interested readers, additional references of Mura detection include Wang and Ma (2006), Kim et al (2008) for further reading. The development of an ''easy-to-implement" and ''computationally efficient" method for detecting TFT-LCD Mura defects is the main goal of this paper.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of Mura defect in TFT-LCD based on regression diagnostics

Fan

Chuang

2010

Pattern Recognition Letters

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Section: Introductionmentioning

confidence: 99%

Automatic detection of Mura defect in TFT-LCD based on regression diagnostics

Fan

Chuang

2010

Pattern Recognition Letters

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“…5 Based on the sensitivity of the human eye to mura, the level for each mura candidate is quantified using the concept of just-noticeable difference (JND), which is used to identify real muras by grading as either pass or fail. Two methodologies for the detection of mura defects have recently been proposed: background image reconstruction [5][6][7][8][9][10][11][12][13][14][15][16] and image segmentation technique. [16][17][18] The reference image can be obtained by background image reconstruction of the DUT source image or by using the representation of basis images.…”

Section: Introduction To Thin-film-transistor-liquid-crystal Display mentioning

confidence: 99%

“…At present, three approaches have been proposed to reconstruct the background image. These are discrete cosine transform (DCT), 6 high-pass filter, 13 and regression diagnostic model. 5 In Liang-Chia and Chia-Cheng, 6 a mura detection method using the DCT is proposed.…”

Section: Introduction To Thin-film-transistor-liquid-crystal Display mentioning

confidence: 99%

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A new algorithm on the automatic TFT‐LCD mura defects inspection based on an effective background reconstruction

et al. 2017

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In this study, an automatic detection method for mura defects is developed based on an accurate reconstruction of the background and precise evaluation of the mura index level. To achieve this, an effective background reconstruction method is first developed to represent the brightness intensity of the display panel. As a result, any nonuniform brightness of the background can be removed effectively. Furthermore, the associated mura level is quantified based on the sensitivity of the human eye in order to alternatively grade the liquid‐crystal display panels. The main focus of this study is on the reconstruction of the background from the display under test image. The proposed method takes full advantage of the following three existing methods: low‐pass filtering, discrete cosine transform, and polynomial surface fitting. By applying the method to several case studies, we have shown that it is more effective compared with other existing methods in detecting various types of mura defects.

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“…This method was intentionally developed to inspect spot-type defects. Kim, Kang, and Jeong (2008) processed the OLED images in the frequency domain by filtering out the plain background using a high-pass filter. The high-frequency passing components were detected as defects.…”

Section: Introductionmentioning

confidence: 99%

Design and evaluation of features and classifiers for OLED panel defect recognition in machine vision

Nguyen

Pham

Cui

et al. 2017

Journal of Information and Telecommunication

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With the rapid growth of organic light-emitting diode (OLED) display devices, the industrial manufacturing of OLED panels is currently an expanding global reality. Regarding quality control, automatic defect detection and classification are undoubtedly indispensable. Although defect detection systems have been widely considered in the literature, classification systems have not received appropriate attention. This study proposes the design of an efficient and high-performance system for defect classification by combining well-known machine-learning algorithms: support vector machine, random forest (RF), and k-nearest neighbours. To begin, possible features are designed and feature selection using principal component analysis and RF is investigated to automatically select the most effective features. Then, a hierarchical structure of classifiers is proposed for efficiently adjusting the rates of true defect and fake defect classification. The proposed system is evaluated over a database of 3502 images captured from real OLED display devices in different illumination conditions. The defects in the database are divided into 10 classes corresponding to the types of true defect and fake defect. The experiments confirm that the proposed system can achieve an accuracy of up to 94.0% for the binary classification of true defect and fake defect and an overall recognition rate of 86.3% for the 10 sub-classes. ARTICLE HISTORY

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Region Mura Detection using Efficient High Pass Filtering based on Fast Average Operation

Cited by 7 publications

References 2 publications

Automatic detection of Mura defect in TFT-LCD based on regression diagnostics

Automatic detection of Mura defect in TFT-LCD based on regression diagnostics

A new algorithm on the automatic TFT‐LCD mura defects inspection based on an effective background reconstruction

Design and evaluation of features and classifiers for OLED panel defect recognition in machine vision

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