Touch screen defect inspection based on sparse representation in low resolution images

Liu, Liang; Li, Di; Fu, Xin; Zhang, Wu-jie

doi:10.1007/s11042-015-2559-8

Cited by 32 publications

(13 citation statements)

References 17 publications

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“…Using this technique, only two features can be extracted named multifractal spectral width and spectrum subtraction, which are considered for classifying the defect type using classification algorithm. Liang et al in [287] proposed AOI system to detect touch panel defects. In their approach, they first calculated the gray-values of the images and considered them as feature values.…”

Section: D: Hough Transformmentioning

confidence: 99%

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: D: Hough Transformmentioning

confidence: 99%

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

2020

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“…In the quality inspection of 3C products, defect inspection is one of the most important links in the manufacturing process of touch screens [58]. ùaban et al [59] proposed a fast and effective glass surface defect detection and segmentation method BiasFeed CNN through comparison with traditional CNN experiments, which solved the problem of the transparency and reflection characteristics of the glass surface.…”

Section: A Application In Mobile Phone Glass Platementioning

confidence: 99%

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Ming

Cao

Zhang³

et al. 2021

IEEE Access

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Based on the rapid development of semiconductors, integrated circuits and the Internet. 3C products such as computers, tablets, mobile phones and smart TVs have become an indispensable part of people's lives. With the prosperity and development of the 3C product market, the demand for the quality of display panels and related detection technologies are increasing. As the iconic network of deep learning, has been extensively studied in the field of image recognition and defect detection. Based on the development of CNN, this article summarizes the defect detection method of 3C products by CNN with different depths. First, we reviewed the origin of CNN and its structural components, then introduced the upgrade and improvement of important components, and finally introduced and compared the applications of CNN with different depths in defect detection. Through the comparison and summary of the effect of defect detection, we analyze the opportunities and challenges of different CNN frameworks, and exhibit the strategies for different application scenarios.

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“…This approach has a limitation of shadow effect problem that infl uence on defect detection. Liang et al [3] proposed a sparse representation-based approach to detect touch screen fl aws in low-resolution images. They used a sparsity ratio of the sparse representation coeffi cients like a measure for distinguishing defective images.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This fl aw rate of the four operations is 14.1% that is roughly up to 38.95% of the total fl aw rate in the production procedures. Accordingly, automated visual detection for the appearance fl aws on the TSs is absolutely essential in the manufacturing process [2,3]. CTSs are composed of transparent glass substrates, on the surface of which an oxide metal is regularly coated.…”

Section: Introductionmentioning

confidence: 99%

Creation of image models for inspecting visual flaws on capacitive touch screens

Peter¹,

Lin²

2018

J Appl Eng Science

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Touch screens (TSs) are commonly applied in many electronic appliances such as smartphones, tablets, etc. Currently, capacitive touch screens (CTSs) are the main touch technology of screen panels due to many excellent electronic properties. Problems exist in inspecting fl aws inlaid in appearances of CTSs with structural patterns. Area fl aws are a type of common visual defect that comprises dust, bubbles, ripple marks, and other fl aws of bigger sizes. These fl aws have the attributes of low contrast, brightness with slow changes, unusual and non-orientation forms, and sometimes both bright and dark fl aws existing at the same time in a region. This paper suggests image models based on transformation fi ltering to inspect the area fl aws on appearances of CTSs. We apply the Haar wavelet transform with fl at zone fi ltering technique to eliminate the structural patterns of background by means of fi ltering an approximate sub-image of a breakdown wavelet domain image. Subsequently, the fi ltered image is reversely transformed to obtain a rebuilt image in spatial domain. Last, the rebuilt image with intensifi ed fl aws can be simply partitioned into three species (black fl aws, gray fl aws, and white background) by using a statistical interval estimation method. Therefore, the intricate area fl aws are precisely identifi ed by the suggested scheme. We contrast our approach with three traditional methods with real samples under complex background and conduct quantitative comparisons. The effectiveness and accuracy of the developed image models are confi rmed by expert assessments, as well as by comparative analysis with the known methods in the fi eld of spatial localizations and production-related effects of fl aw detection.

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Touch screen defect inspection based on sparse representation in low resolution images

Cited by 32 publications

References 17 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

Review: Application of Convolutional Neural Network in Defect Detection of 3C Products

Creation of image models for inspecting visual flaws on capacitive touch screens

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