OLED Defect Inspection System Development ...

TELKOMNIKA, Vol.10, No.8, December 2012, pp. 2309~2319 e-ISSN: 2087-278X accredited by DGHE (DIKTI), Decree No: 51/Dikti/Kep/2010


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OLED Defect Inspection System Development through Independent Component Analysis Zhiliang Wang, Jian Gao*, Chuanxia Jian,Yu Cen and Xin Chen School of Electromechanical Engineering, Guangdong University of Technology, Key Laboratory of Mechanical Equipment Manufacturing & Control Technology, Ministry of Education, Guangzhou, 510006, China *Corresponding author, email: [email protected]

Abstract Organic Light Emitting Displays (OLED) is a new type of display device which has become increasingly attractive and popular. Due to the complex manufacturing process, various defects may exist on the OLED panel affecting the quality and life of the display panels. These defects have the characteristics of fuzzy boundaries, irregular in shape, low contrast with background and especially, they are mixed with the pixel texture background increasing the difficulty of a rapid recognition. In this paper, we proposed an approach to detect the defects based on the model of independent component analysis (ICA). The ICA model is applied to a perfect OLED image to estimate its corresponding independent components (ICs) and create the de-mixing matrix. Through estimation and determination of a proper ICi row vector of the faultless image, a new de-mixing matrix can be generated which constitutes only uniform information and is then applied to reconstruct the texture background of source OLED images. Through the operation of subtraction of the reconstructed background from the source images and the binary segmentation, the defects can be detected. Based on the algorithms, a defect detection system for the OLED panels was implemented and the testing work was performed in this study. The testing results show that the proposed method is feasible and effective. Keywords: Organic LED (OLED), Defect detection, Independent Component Analysis, Background reconstruction Copyright © 2012 Universitas Ahmad Dahlan. All rights reserved.

1. Introduction After decades of development, the organic light emitting display(OLED) was considered to be one of the next-generation display devices. Compared with the LCD (Liquid Crystal Display) device, OLED displays has the advantages of self-luminous, bright color display, good seismic performance, thin and flexible substrates, etc [1]. Due to its complex manufacturing processes, some key issues in the OLED technology needs to be tackled, especially for large dimension OLED panels. One of the key issues is to solve the display quality problem. Complex processes of the OLED production line will cause inevitably the panel with a variety of defects. The causes of defects could be dust and foreign matters in the assembly process, short-circuit and open circuit for scanning electrode and the signal electrode, driver IC poor contact, substrate exist rupture, scratches or blemishes, etc. Since the defects affects the life of the OLED display and users’ visual experience, it is necessary to inspect any possible defect remain in the OLED manufacturing process. The OLED defects are usually divided by their shapes and size into the point defect, line defect and Mura defect(Mura defect means a large piece of stain) [2]. The common features of these defects are irregular geometry, local uneven brightness, low contrast with the background, fuzzy boundaries, appearing in a random location [3], and horizontally and vertically regularly spaced pixels which constitute the texture background of the OLED images. Currently, machine vision technology is one of the most useful methods used in industrial inspection which can greatly improve the flexibility and level of automation. W.S.Kim, et al. [4] adopted an approach to gradually reduce the image grayscale by using a concentrated grayscale to represent the background and defects, and then extract the defects through a threshold for the image binarization. This approach can detect a limited LCD defects. S.Kim,et.al. [5] treated the OLED display images as the combination of defects and texture background, they correspond to high-frequency and low frequency components in frequency Received October 10, 2012; Revised November 20, 2012; Accepted December 1, 2012

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domain, respectively. Through a high-frequency passing filter, the low frequency components of the texture background was filtered in the frequency domain, leaving only high frequency which corresponding to the defects on the image. S.Chen and S.Chou[6] adopted a discrete cosine transform to filter the backgroud signals for the defect detection. This method needs to create a complex mathematical model in the frequency domain. C.Lu and D.Tsai [7] proposed a singular value decomposition (SVD) based LCD detecting method. It implements a singular value decomposition operation for a defective image, overall information includes the texture background which responding to the singular values in the diagonal matrix. By seting a zero to the specific singular value, and then inversely transforming the matrix back, the texture background components can then be removed and remain only the defects. The disadvantage of the method is that how many singular values to be set to zero depends on the trials. D.M.Tsai, et al [8] proposed an independent component analysis (ICA) model based convolution filtering scheme for detecting small defects in low-contrast uniform surface image for the LCD applications. The ICA-based optimal filter was designed with the objective that the convolution filter generated the most representative source intensity of the background surface. The constraint of the optimal filter confined the source values of all training image patches of a defect-free image. The constrained ICA model was solved by the algorithm of particle swarm optimization. The experimental results show that the proposed apprach can effectively detect various small defects in low–contrast surface image. For a OLED image acquisited by a high resolution CCD, the defects are normally mixed with the pixel texture background which makes the detection extremly difficult. An example of the OLED images with defects is shown in Figure.1. How to remove the periodic texture background interference and maintain the integrity of the defects is the key issue for the OLED defect inspection. Traditional thresholding methods can not detect these defects effectively. Wang,et al. [9] proposed a multi-image-subtraction method to detect the pixel defects of OLED panel. The defective images are processed with a pixel template which is obtained through several operations of pixel extracting and subtraction to achieve a good contrast image, and then the images are segmented by a K-means clustering method. Gao, et al. [10] proposed a corner-points based method to extract the control points from the skeleton image and removed the texture backgroung through subtracting operation. Based on the images obtained, an improved recursive Otsu method is adopted to determine the threshold for image segmentation. These detection approaches can detect most of the defects on the OLED panels with trials, but they may not suit for automatic application of the OLED defect detection. Lee ang Yoo [11] proposed a polynomial fitting approach for detecting the defects mixed with texture backgroung in TFT-LCD images. Once the background was estimated and fitted with a low-order polynomial, subtraction of the fitted background from the source image is then applied to find the threshold for binary segmentation. The proposed method worked well to detect regional defects in low-constrast TFT-LCD images. However, this approach is very computationally intensive because the background is estimated recusively by eliminating one pixel at a time throughout the entire image [8]. In this paper, the defect detection of the OLED panel with texture background is studied through the independent component analysis (ICA). We apply the ICA model to the defect-free OLED images to establish the relationship between independent components and the corresponding texture background image. The de-mixing matrix corresponding to the ICs are assessed and generated to estimate and reconstruct the background image. Through subtraction of the estimated background image from the captured testing images, the image with preliminary defects can be obtained and the defects can be further identified through binary segmentation. This paper is organized as follows: Section 2 introduces the basis of ICA model and ICA-based implementing approach, which includs the negentropy based FastICA method, ICA Image Training, de-mixing matrix determination and correction, and background image estimation and reconstruction. Through this implementing procedures, the defects on the source OLED image can be detected. Section 3 illustrates the experimental setup for OLED defect inspection and then demonstrates the experimental results from a number of OLED defective images. To validate the feasiblity of the proposed ICA-based detection approach, a background polynomial fitting-based defect detection method was implemented and the test results are shown and compared. The conclusions are presented in Section 4.

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Figure. 1 Original OLED image with a number of defects

2. Defect Inspection through the ICA 2.1 The ICA model Independent component analysis (ICA) is a method to identify implicit component from multi-dimensional statistical data. From the perspective of linear transformations and linear space, the source signals are independent non-Gaussian signal, the observed signal is a linear combination of source signals. When the source signal and the linear transformation are unknown, the ICA is a way to estimate the original signal from the observed mixed-signals. The ICA model can be used to estimate the main components of the signal. It has been widely used in analysis of physiological data, face recognition, fabric surface analysis, surface texture description and image texture feature extraction [12-14]. The basis of the ICA model is described as follow [8],[17-20]. Assume there are a number of n statistically independent random variables s1 , s2 , s3 ,L , sn , their linear combination to generate a number of m random variables x1 , x2 , x3 ,L xm : n

X = [ xi ] = AS = ∑ aij s j j =1

i = 1,2, L m

(1)

where A is a mixing coefficient matrix {aij } , X is the observed signals [ xi ] , S is the latent

source signals {s j } , which are assumed to be mutually statistically independent.

The ICA model describes how the observed signals X are generated by the two matrixes of the mixing matrix A and the latent source signals S . Based on the assumption, the ICA analysis is use to obtain a de-mixed matrix W in an unsupervised learning process. The independent signals Y are formed by the matrix W transforming the observed signals X . That is: Y = WX = W ( As )

(2)

Y = [ y1 , y2 ,L yn ] where W is a de-mixing matrix, is independent signals, used to estimate the latent source signals. Each row vector of the signals Y contains independent components called ICs, which are required to be as mutually independent as possible. In this paper, X represents the OLED image signals obtained which may contain various defects, it is a mixture of multiple signals, S represents the main background signals. Various types of defects and interference signals are treated as the unknown mixed matrix A . Through the ICA model, the matrix Y can be determined and used to estimate the components of the OLED background signal. Generally, for the pre-processing of the ICA, the steps of centering and whitening process are needed. The main processes of the ICA algorithm are demonstrated in Figure.2. At first, the observed signal X needs to centered by subtracting the mean of each columns of

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X . The matrix X with zero mean is then passed through the whitening matrix V to remove the second-order dependency, which is calculated by the formula of:

V = 2 ⋅ [Cov( X )]

−1/ 2

(3) where V is the whitening matrix, Cov ( X ) is the covariance matrix of X . The rows of the whitened input matrix are uncorrelated and have unit variance. This means that the variances of the ICs are equalized and have unit variance.

Figure 2. Main processes of Independent component analysis

2.2 The FastICA through the negentropy The entropy is a measure of events randomness, and the more random variables the greater the entropy has [16]. In the information theory, there is a basic rule: for the equal variance of the random variables, its Gaussian variables have the largest entropy. This indicates that the Gaussian distribution is the most unpredictable、unstructured of all distributions, we can use an relative entropy to the Gaussian distribution to define the degree of non-Gaussian variables. Hyvarien has proposed a fast optimization iterative algorithm called the FastICA which is a negative entropy-based fixed-point algorithm [14],[17]. The objective of the FastICA algorithm is to maximize the statistical independency of the ICs. The non-Gaussianity of ICs can be measured by the concept of negentropy which means the negative entropy and is defined as:

J ( y ) = H ( ygauss ) − H ( y )

(4) where y gauss is a Gaussian random vector of the same covariance matrix as y. H ( y gauss ) is the entropy of the Gaussian random vector y gauss . H ( y ) is the entropy of a random vector y with density p y (η ) defined as

H ( y ) = −∫ py (η ) log py (η )dη.

(5)

The negentropy is always non-negative and is zero if and only if y has a Gaussian distribution. It is well justified as an estimator of the non-Gaussianity of the ICs. From the definition of negative entropy, when the stronger of non-Gaussian distribution for y , the larger value of J ( y ) . Due to the difficulty in computing of the negentropy, this study adopted the classical approximation method by use of higher-order moments through the following formula for the OLED defect inspection application [19]: J ( y) ≈

{

(

2 1 1 E ( y3 ) + E { y4 } − 3 E ( y2 ) 12 48

)}

2

(6) Considering the advantages of the convergence faster and robust of the FastICA algorithm, this paper utilizes the negative entropy as a search direction and extracts independent component signals from the images. The algorithm was implemented in this study through the Labview dynamic programming by calling the FastICA function in Matlab. 2.3 ICA-based implementing procedure In order to detect OLED panel defects, we apply the ICA to a defect-free OLED image firstly to establish the relationship between independent component primitives and the TELKOMNIKA Vol. 10, No. 8, December 2012 : 2309 – 2319

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corresponding image texture. Then the de-mixing matrix W can be obtained to estimate the background image. Through the subtracting operation of the testing images with the estimated background images, the image with preliminary defects can be obtained and the defects can be further identified through binary segmentation. The flow chart of the ICA based defect inspection procedure is illustrated in Figure 3.

Figure 3. ICA defect inspection procedure 2.3.1. ICA Image Training and de-mixing matrix determination For the ICA application, a training image patch will be selected from the defect-free images. Let X represents the image matrix with the size of n × m, which will be divided into n row vectors, and each row vector size is 1 × m. The input image can thus be expressed by the matrix of X = [ x1 , x2 ,L xn ] . Through the ICA model, the de-mixing matrix W can be obtained with the corresponding independent component ICi ( yi ) , i = 1， 2， L n , and the size of each component ICi

is 1 × m. The de-mixing matrix W of the data x can be determined by

y = Wx

(7)

and n

x = ∑ si ai = As i

(8)

where ai (i = 1, 2,..., n) is the ith column of the mixing matrix An×m , and s = [ s1 , s2 ,..., sn ] . The vector T

y is an estimate of the source s , and its components are required to be as statistically independent as possible. For example, for a defect-free image with the size 26*400, one row vector of the image is selected as training image patch with the size of 1*400 as shown in Table1 a). The components ICi ( yi ) ( i = 1， 2， L 26 ) of the image patch corresponding to 26 row vectors can be obtained which are shown in Figure.3. The independence between the independent components will be measured by the largest negative entropy through the formula (4). According to the central limit theorem [16], if an independent signal follows an nonGaussian distribution, then the components ICi obtained above will obey a super-Gaussian distribution or sub-Gaussian distribution. Random variables with Super-Gaussian distribution are symmetrically distributed around the mean value with a large amplitude. On the contrary, variables with sub-Gaussian distribution are also symmetrically distributed around the mean value, but with a small amplitude [15,16]. For an image with texture background, the component ICi with super-Gaussian distribution contains a global signal with some peaks, which represent the periodical texture signals. On the other hand, the component ICi with sub-Gaussian distribution shows a smooth variant signal, which represents the uniform background. According to this theorem, the independent components ICi ( yi ) ( i = 1， 2， L 26 ) of the exampled image can be obtained and shown in Figure.4. From this Figure, we found that the components of IC16 , IC20 , IC11 contain a peak signal, which means that these components are rich in texture information.

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On the other hand, the components of IC17 , IC13 and IC15 have a smooth signal without peaks, which means these components represent the background signal of the image.

Figure 4. The independent components of a defect-free image 2.3.2 De-mixing matrix correction and image fitting In order to remove the periodical texture in the background of the OLED images, we use the variance as the assessment criteria for estimation and selection of the components ICi : D ( ICi ) = E  ICij − E ( ICi ) 

2

(9)

where ICij represent the component with the number j data in ICi ( i = 1, 2,L , n ; j = 1, 2,L m ). Base on the ICi variance value, the sequence of the independent component ICi is rearranged in descending order and it is shown in Figure.5. It can be seen from this Figure that the independent component IC16 shows the largest fluctuation and the independent component IC15 shows the most flatness signal. Therefore, the independent component IC15 is selected to represent the uniform background information of the image. Through the analysis of the data of ICi , texture information can be fed back to the demixing matrix W . The final ICi have the strongest uniform background information, define the corresponding vector Wi in W are placing vector Wk , using Wk replace the first n vectors in W , n is usually taken 0.75 times the number of ICi . After the replacing of the W , a new de-mixing matrix W ∗ is reconstructed, which will be used to estimate the uniform background of the OLED source image. In Figure.5, the data of IC15 is the most flat signal, so we can select W15 as the replacing vector and reconstruct a new de-mixing matrix W ∗ . This de-mixing matrix W ∗ received from defect-free image can be used to reconstruct the uniform background for OLED image, the background reconstruction can be implemented as follow:

Y ∗ = W ∗x

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(10)

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In the formula, x is tested image, Y ∗ is the estimated uniform background for the image to be detected, the he texture of the background information has been removed. Subtract the estimated background from the defective image, a clear result can be obtained binarization operation. Table1 shows the main procedure of the OLED defect inspection.

Figure 5 The independent components in a descending order

Table.1 ICA test results for defective image No.

1

2 3

4 5

Process procedure Selected defectfree image and get W* Original defective image Reconstructed image background ∗ by W Background removed image

ICA-based OLED image process results

Binary result

3. System development and Experimental tests Based on the study of the defect detection method, an automatic defect detection system is developed which consists of XYZ motion table, LED light, CCD image acquisition, motion Controller, and detection software. The experimental setup of the detection system sy is shown in Figure.6. 6. In the testing experiment, the motion table is constructed by the KOLLMORGEN linear motors with the range of 500mm*500mm, the profile dimension of the XYZ table is 1200*850*800mm. The motion is driven by the IMAC400 controller from fr Deltatau Plc. The CCD camera adopts the TAKEX FC30CL CCD with the resolution of 640*480 pixels,

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45views/s. The detection software system for the OLED panel defect was implemented through Labview and C program and the main detecting process of the inspection system developed is illustrated in Figure 7. To further verify the validity of the algorithm adopted, a defective OLED image with the size of 88 × 180 shown in Table2.(a1) is selected. Through the ICA operation to the defect-free L IC88 can be obtained image selected in Table2.(b1), the independent components of IC1，IC2， which is sorted in a descending order. The component IC45 has the most flat variance and its corresponding W45 is used as a replacing row vector. Once the first 66 vectors(0.75×n) in the W are replaced with the W45 , a new de-mixing matrix W ∗ can be determined and applied to reconstruct the background of the source images. Subtracting the reconstructed background in Table2.(c1) from the original image in Table2.(a1), the result is shown in Table2.(d1) and the defects can be clearly detected further by the binarization operation shown in Table2.(e1). To compare the defect detection result, a polynominal fitting based detection approach is also implemented in this paper, the details of the approach can refer to the reference in [21]. The testing results to the same images used in the ICA-based approach are shown in Table2.(a2)~(e2). Comparing the results from the two methods, it is clearly shown that the ICAbased detection method works better than the polynominal fitting based detection approach.

Z-axis table X-axis table OLED panel Camera/CCD

Air platform

Y-axis table Marble Base

Figure 6 Experimental Setup for OLED panel defect Inspection

Figure 7 Detecting process of the developed inspection system Several other tests are performed in this paper to validate the ICA defect detection method. The original OLED images are shown in Figure.8 (a1)~ (a3) and its corresponding defect detecting results are shown in Figure.8 (b1)~(b3). Compare these results to the original images, it can be found that the approach proposed can detect most of the defects, and the location and boundary contour of the defects can be well preserved.

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Table 2 Example of OLED image process ICA based Polynomial Fitting(PF) based Defect Inspection Defect Inspection ICA process OLED inspection results Process OLED inspection results procedure procedure (a2) A defective (a1)A defective source source image image, same as (a1) (b1)A defect-free image for ICA de-mixing matrix

(b2) Fitted Backgroung by 3-order polynominal

(c1)Reconstructed background image

(c2) Fitted Backgroung by 4-order polynominal

(d1)The image by background removal

(d2)The image by Backgrough removed from (c2)

(e1) Defects inspected

(e2) Defects inspected from (d2)

(a1)

(a2)

(a3)

(b1)

(b2)

(b3)

Figure 8 Testing examples of the defect inspection system developed: developed (a1)~(a3) are exampled source OLED images, and (b1)~(b3) are the corresponding inspected defect images OLED Defect Inspection System Development through Independent … (Zhiliang Wang)

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Although the method contains several key issues including ICA analysis, ICs estimation, new de-mixing matrix generation, and background subtraction, it is still useful for a large number of image inspection since the de-mixing matrix obtained can be used repeatedly which will improve the efficiency of defect detection process.

4. Conclusion Due to the characteristics of low-contrast, irregular in shape and periodical texture background interference of a OLED image, it is very difficult to effectively detect these defects. This paper applied the independent component analysis (ICA) algorithm to detect the OLED panel defective images. Through analysis and estimation of the ICs row vectors of the faultless image, the proper ICi with the most smooth variance can be determined and used to replace the corresponding row vectors in the mixing matrix. Thus, a new de-mixing matrix can be generated which constitutes only uniform information and is then applied to reconstruct the texture background of source OLED images. Through the subtraction and segmentation operation to the source images, the defects can be detected. This paper implemented the proposed detection system and also setup an experimental XYZ table for the inspection. Through a number of experimental tests carried out on the table, the detection system was verified. From the testing results, wo found that the proposed method can effectively remove the texture background and detect most of the defects. Futher work will be carried out on improvement of system integration including software, table control and CCD acquisition, and also strength its industrial application.

Acknowledgments This work is supported by Key Joint Project of National Natural Science Foundation of China (Grant No.U1134004), by National Basic Research Program of China (973 Program, Grant No.2011CB013104), by Scientific Innovation Key Project of High Education Disciplinary Construction from Guangdong Educational Department (Grant No. 2012CXZD0020) and by the Specialised LED Sector Fund of Guangdong Provincial R&D project (Grant No.2011A081301001, No. 2012A080303004) .

References [1] Hsieh C. Alternative Display Technology Report: OLED Technology. Display Search, an NPD Group Company, 2006. [2] Yoshitake Y M R, and Tamura T. Evaluation and discrimination method of ”mura” in liquid crystal displays by just noticeable difference observation. SPIE 2002;4902:715 - 722. [3] Xu X Z, Feng Y M, et al. A new test method for OLED brightness uniformity[J], Photonics 2007,33(1). [4] Kim W S, et.al. Detection of spot-type defects on liquid crystal display modules, Key engineering Materials. 2004; 270-273:808-813. [5] S.H. Kim, et.al. Region Mura Detection using Efficient High Pass Filtering based on Fast Average Operation. Proceedings of the 17th World Congress, International Federation of Automatic Control. 2008;17(1):Part1. [6] Chen S, Chou S. TFT-LCD Mura defect detection using wavelet and cosine transforms. Journal of Advanced Mechanical Design, System, and Manufacturing. 2008; 2(3):441-445 [7] Lu C J and Tsai D M. Automatic defect inspection for LCD using singular value decomposition. The International Journal of Advanced Manufacturing Technology. 2005; 25(1):53-61. [8] Tsai D M, Lin P C and Lu C J. An independent component analysis-based filter design for defect detection in low-contrast surface images, Pattern Recognition. 2006; 39:1679-1694. [9] Wang Z L, Gao J, Chen X, et al. OLED Pixel Defect Detection through Patterned Background Removal, Sensor Letter. 2012;Accepted. [10] Gao J, Wang Z L, Liu Y Y, et al. Development of OLED PanelDefect Detection System through Improved Otsu Algorithm, Proceedings of the 2012 International Conference on Intelligent Robotics and Applications (ICIRA 2012), in Concordia University, Montreal, Quebec, Canada. (Accepted, 2012) [11] Lee J Y, and Yoo S I. Automatic detection of region-mura defect in TFT-LCD, IEICE Trans.Inf.Syst. 2004; E87-D:2317-2378. [12] Jensen R and Eltoft T. Independent component analysis for texture segmentation, Pattern Recognition. 2003;36:2301-2315. [13] Manduchi R and Portilla J. Independent component analysis of textures, Proceeding of the IEEE International Conference on Computer Vision, Kerkyra, Greece, 1999;1054-1060.

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[14] Hyvärinen A. The Fixed-Point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis, Neural Processing Letters.1999;10(1):1-5 [15] Jiang P, et al. Discussion of Super-Gaussian and Sub-Gaussian Distribution, Journal of Data Acquisition and Processing(Chinese). 2009;24(z1): 115-118 [16] Wang K F. An ICA Algorithm Based on Exponential Power Family of Densities and Gaussian Mixture Densities Models [D]. Dalian University of Technology, 2009. [17] Hyvärinen A and Oja E. A fast fixed-point algorithm for independent component analysis, Neural Computation. 1997; 9(7): 1483-1492. [18] Roberts S and Everson R. Independent Component Analysis: Principles and Practice. Cambridge,U.K: Cambridge Univ.Press, 2001. [19] Oja E,Yuan Z J. The FastICA Algorithm Revisited:Convergence Analysis. IEEE Trans.Neural Networks. 2006;17 (6) :1370-1381. [20] Lu C J, Tsai D M. Independent component analysis-based defect detection in patterned liquid crystal display surfaces. Image and Vision Computing. 2008;26:955-970 [21] Wang Z L, OLED Display Panel oriented Defect Detection Algorithm and System Development, Dissertation, Guangdong University of Technology, China, June, 2012.

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