Saturated Imaging for Inspecting Transparent Aesthetic Defects in a Polymeric Polarizer with Black and White Stripes

Yu, Changjun; Chen, Peibing; Zhong, Xing; Pan, Xizhou; Deng, Yuanlong

doi:10.3390/ma11050736

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…Authors in [223]- [229] considered inspecting polarising film defects, according to the fact that polarising film is a key component in LCDs. Special aesthetic defects in polymeric film polarizers have nearly identical reflectivity and transmissivity as normal regions.…”

Section: Othermentioning

confidence: 99%

“…Such defects, in the industry known as "convex or concave points", have extremely low contrast and are difficult to image under normal illumination. Authors in [224]- [227], [229] defined them as "transparent defects", such as dents, bulges and transparent impurities. Because of their extremely low contrast, these transparent points are the main obstacle to detecting defects in polymeric polarizers with existing detection technology.…”

Section: Othermentioning

confidence: 99%

“…Blue coaxial light [280] This setup was used to detect surface deformation in LCD of smartphones with the aid of precision plate. By the illumination from the blue coaxial light, the images of the standard circle holes on the precision plate are reflected on the surface of the measured LCD, and then the images are further reflected onto the surface of the CCD camera by the semi-reflecting mirror, thus the circle hole images from the measured reflected LCD [227], [229] This setup was used to detect aesthetic defects in LCD. The setup consisted of white and black stripes that is positioned under the inspected polymeric polarizer.…”

Section: Suitable For Solder Joint Inspectionmentioning

confidence: 99%

“…Finally, they did not show the necessity of using such a simulated technique. Authors in [226], [229], conducted similar approaches; however, they considered extremely slight transparent aesthetic defects.…”

Section: B: Region Extractionmentioning

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: Othermentioning

confidence: 99%

Section: Othermentioning

confidence: 99%

Section: Suitable For Solder Joint Inspectionmentioning

confidence: 99%

Section: B: Region Extractionmentioning

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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“…In our previous studies [7,[13][14][15], we have focused on an imaging mechanism for transparent microdefects, such as dents and bumps, and proposed a so-called saturated imaging method to obtain high-contrast defect images. Through simulation and experimentation, we have found that the saturated image of a defect varies regularly with its stereo shape.…”

Section: Introductionmentioning

confidence: 99%

Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images

Deng¹,

Pan²,

Zhong³

2020

Applied Sciences

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Featured Application: The paper provides a convenient method to determine if a suspected microdefect of polarizer film is real. This is useful for manufacturing of diverse flexible/ transparent thin-film. Abstract:In the industry of polymer film products such as polarizers, measuring the three-dimensional (3D) contour of the transparent microdefects, the most common defects, can crucially affect what further treatment should be taken. In this paper, we propose an efficient method for estimating the 3D shape of defects based on regression by converting the problem of direct measurement into an estimation problem using two-dimensional imaging. The basic idea involves acquiring structured-light saturated imaging data on transparent microdefects; integrating confocal microscopy measurement data to create a labeled data set, on which dimensionality reduction is performed; using support vector regression on a low-dimensional small-set space to establish the relationship between the saturated image and defects' 3D attributes; and predicting the shape of new defect samples by applying the learned relationship to their saturated images. In the discriminant subspace, the manifold of saturated images can clearly show the changing attributes of defects' 3D shape, such as depth and width. The experimental results show that the mean relative error (MRE) of the defect depth is 3.64% and the MRE of the defect width is 1.96%. The estimation time consumed in the Matlab platform is less than 0.01 s. Compared with precision measuring instruments such as confocal microscopes, our estimation method greatly improves the efficiency of quality control and meets the accuracy requirement of automated defect identification. It is therefore suitable for complete inspection of products.

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Improved imaging of extremely-slight transparent aesthetic defects using a saturation level-guided method

et al. 2020

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The quality-control process of polarizer production is hampered by the presence of extremely-slight transparent aesthetic defects (ESTADs). The saturated imaging method based on stripe structured backlight can effectively improve the imaging contrast of ESTADs. However, the contrast is very sensitive to the saturation degree, which requires careful manual selection. This paper presents a saturation level-guided image enhancement method that is simple to deploy in industrial settings. First, a new definition of the saturation level for structured backlit imaging with translation, scale, and rotation invariance is proposed. Then, an empirical model of contrast versus saturation level is established. Using the contrast data measured at five saturation levels, the optimal saturation level can be estimated using the parameter optimization method. The experimental results demonstrate that the method is effective, easy to use, and an improvement of imaging effects for transparent thin-film defect detection algorithms.

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Saturated Imaging for Inspecting Transparent Aesthetic Defects in a Polymeric Polarizer with Black and White Stripes

Cited by 4 publications

References 10 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

Efficient Shape Estimation of Transparent Microdefects with Manifold Learning and Regression on a Set of Saturated Images

Improved imaging of extremely-slight transparent aesthetic defects using a saturation level-guided method

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