Low-contrast surface inspection of mura defects in liquid crystal displays using optical flow-based motion analysis

Tsai, Du‐Ming; Tsai, Hsin-Yang

doi:10.1007/s00138-010-0256-1

Cited by 23 publications

(9 citation statements)

References 11 publications

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“…In case of no defect has been detected, the light will be scattered such that it will not be detected by the camera. Tsai and Tsai in [249] used unconventional illumination and [290] image acquisition techniques in inspecting Mura defects in LCDs. The LCD panel is assumed to move along a track.…”

Section: A Contrast and Illumination Settingsmentioning

confidence: 99%

“…However, this solution is considered costly and needs special alignment. 3D optical [176] Adaptive template Rule-based 100% 0% -ViBe, SDL, SAM& RPCA [199] Systematic matrix Rule-based Up to 100% 0.05% 1.64s (total) [52], [198], [407], [408] [196] Textural Adaptive thresholding Rule-based 78% (precision) ->0.203s/image - [246] ICA Rule-based --8.1ms/image - [249] Optical flow Rule-based --0.054s/image - [248] Modified Hough transform Rule-based -0% 0.696s/image Hough transform [271] Segmentation F-SVDD 95% -7.8s/panel SVDD [271] Segmentation QK-SVDD 96% 7.54% 60ms/panel SVDD [267] PCA [415] can be considered an effective tool to mitigate these problems and to provide full description of the component 3D nature.…”

Section: Limitations Of Aoi Systems and Future Directionsmentioning

confidence: 99%

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A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, microelectronics , and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.

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Section: A Contrast and Illumination Settingsmentioning

confidence: 99%

Section: Limitations Of Aoi Systems and Future Directionsmentioning

confidence: 99%

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

2020

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“…Optical flow has also been used to analyze motion in one, three or four dimensions [22,9,5]. In this work, we investigate the convergence of the HS optical flow problem in the generalized case of dimension n ≥ 1.…”

Section: Statement Of the Problemmentioning

confidence: 99%

A Proof of Convergence of the Horn--Schunck Optical Flow Algorithm in Arbitrary Dimension

Tarnec¹,

Destrempes²,

Cloutier³

et al. 2014

SIAM J. Imaging Sci.

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The Horn and Schunck (HS) method, which amounts to the Jacobi iterative scheme in the interior of the image, was one of the first optical flow algorithms. In this article, we prove the convergence of the HS method, whenever the problem is well-posed. Our result is shown in the framework of a generalization of the HS method in dimension n ≥ 1, with a broad definition of the discrete Laplacian. In this context, the condition for the convergence is that the intensity gradients are not all contained in a same hyperplane. Two other articles ([17] and [13]) claimed to solve this problem in the case n = 2, but it appears that both of these proofs are erroneous. Moreover, we explain why some standard results on the convergence of the Jacobi method do not apply for the HS problem, unless n = 1. It is also shown that the convergence of the HS scheme implies the convergence of the Gauss-Seidel and SOR schemes for the HS problem.

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“…Surface defects on glass substrates generally include scratches, cracks, bubbles, inclusions, dirt and other markings [1][2][3] . In resistive touch panel glass, the presence of these defects can result in low light transmittance and low levels of visibility.…”

Section: Introductionmentioning

confidence: 99%

Development of an optical inspection platform for surface defect detection in touch panel glass

Chang

Chen

Gabayno

et al. 2016

International Journal of Optomechatronics

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An optical inspection platform combining parallel image processing with high resolution opto-mechanical module was developed for defect inspection of touch panel glass. Dark field images were acquired using a 12288-pixel line CCD camera with 3.5 µm per pixel resolution and 12 kHz line rate. Key features of the glass surface were analyzed by parallel image processing on combined CPU and GPU platforms. Defect inspection of touch panel glass, which provided 386 megapixel image data per sample, was completed in roughly 5 seconds. High detection rate of surface scratches on the touch panel glass was realized with minimum defects size of about 10 µm after inspection. The implementation of a custom illumination source significantly improved the scattering efficiency on the surface, therefore enhancing the contrast in the acquired images and overall performance of the inspection system.

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Low-contrast surface inspection of mura defects in liquid crystal displays using optical flow-based motion analysis

Cited by 23 publications

References 11 publications

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

A Proof of Convergence of the Horn--Schunck Optical Flow Algorithm in Arbitrary Dimension

Development of an optical inspection platform for surface defect detection in touch panel glass

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