Automatic defect inspection for LCDs using singular value decomposition

Lu, Chi-Jie; Tsai, Du‐Ming

doi:10.1007/s00170-003-1832-6

Cited by 128 publications

(73 citation statements)

References 20 publications

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“…The glass defects can only cause a visualization problem, whereas the cell defects are more severe and not only affect the visualization but also electrical failure and operational malfunction (Lu & Tsai, 2005). To efficiently capture defects in the internal layers, the OLED device is illuminated with different lighting conditions and photographed from different camera viewing angles.…”

Section: Oled Panel Defectsmentioning

confidence: 99%

“…There are several factors that can generate defects including dust and foreign matter in the assembly process, short circuits, and open circuits in the scanning electrode and signal electrode, poor driver-integrated circuit contact, substrate ruptures, and scratches (Wang, Gao, Jian, Cen, & Chen, 2012). The defects on an OLED panel can cause not only visual failure but also electrical failure and operation malfunction (Lu & Tsai, 2005). The size of OLED defects is generally minute and mixed with the texture in the background, making manual inspection impractical.…”

Section: Introductionmentioning

confidence: 99%

“…Chen and Chou (2008) considered mura defects as low contrast and luminance variation without a clear contour on a uniformly produced surface, and then proposed two methods to detect defects based on discrete cosine transform and discrete wavelet transform. Lu and Tsai (2005) detected several defect types such as pinholes, scratches, and particles or fingerprints by using singular value decomposition. Considering the texture on the panel background as a periodical, repetitive pattern, proper singular values are selected to reconstruct the background and then the anomalies of the defects can be extracted by subtraction.…”

Section: Introductionmentioning

confidence: 99%

“…Considering the texture on the panel background as a periodical, repetitive pattern, proper singular values are selected to reconstruct the background and then the anomalies of the defects can be extracted by subtraction. Independent component analysis is also proposed (Lu & Tsai, 2008;Wang et al, 2012) in a similar methodology as given by Lu and Tsai (2005) reconstructing the background texture through the independent components extracted from the faultless images. Gao, LiuChuanxia, and Chen (2012) presented a recursive scheme of the adaptive Otsu method to calculate the threshold for image segmentation of defect/background.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Oled Panel Defectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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show abstract

“…A singular value decomposition of an M x N (without loss of generality, we take M > N ) matrix A is any factorization of the form A =UEVT (1) where U is an M x M orthogonal matrix, V is an N x N orthogonal matrix, and Z is an Figure 1 shows an image and its eigen-images. From the figure we can find that the first three eigen-images possess most the structure feature and energy of the image.…”

Section: Singular Value Decomposition and Image De-noising 21 Singulmentioning

confidence: 99%

SVD-Based Image De-nosing with the Minimum Energy Model

Zhang¹,

Hai-feng²,

Sang³

et al. 2008

2008 Congress on Image and Signal Processing

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This paper proposes a new solution integrating energy function into singular value decomposition (SVD) for image de-noising. The singular values on the diagonal matrix obtained through SVD represent different components in image. By selecting the proper singular values that represent signal and discarding the ones that represent noise, the additive noise of an image can be eliminated effectively. In order to obtain the optimal number of the singular values for image reconstruction and to eliminate the noise, the paper presents a minimum energy model. This model is used to obtain the optimum number for de-noising through calculating the minimum in the defined energy curve. The experiment results show that the established model is effective in the circumstance that the image has simple/regular structure/pattern.

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Dielectrophoretic Lens

2012

Introduction to Adaptive Lenses

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Automatic defect inspection for LCDs using singular value decomposition

Cited by 128 publications

References 20 publications

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

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

SVD-Based Image De-nosing with the Minimum Energy Model

Dielectrophoretic Lens

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