article

An independent component analysis-based filter design for defect detection in low-contrast surface images

Authors:

Ping-Chieh Lin,

Chi-Jie LuAuthors Info & Claims

Pattern Recognition, Volume 39, Issue 9

Pages 1679 - 1694

https://doi.org/10.1016/j.patcog.2006.03.005

Published: 01 September 2006 Publication History

Abstract

In this paper, we propose a convolution filtering scheme for detecting small defects in low-contrast uniform surface images and, especially, focus on the applications for backlight panels and glass substrates found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and even worse, the sensed image may present uneven brightness on the surface. All these make the defect detection in low-contrast surface images extremely difficult. In this study, a constrained independent component analysis (ICA) model is proposed to design an optimal filter with the objective that the convolution filter will generate the most representative source intensity of the background surface without noise. The prior constraint incorporated in the ICA model confines the source values of all training image patches of a defect-free image within a small interval of control limits. In the inspection process, the same control parameter used in the constraint is also applied to set up the thresholds that make impulse responses of all pixels in faultless regions within the control limits, and those in defective regions outside the control limits. A stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to solve for the constrained ICA model. Experimental results have shown that the proposed method can effectively detect small defects in low-contrast backlight panels and LCD glass substrate images.

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 39, Issue 9

September, 2006

259 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Pattern Recognition Society © 2006.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 September 2006

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Shen RGhasempour YCosta XHassanieh HAsadi ACox LPerino DWidmer JGiustiniano D(2023)Scattering from Rough Surfaces in 100+ GHz Wireless Mobile Networks: From Theory to ExperimentsProceedings of the 29th Annual International Conference on Mobile Computing and Networking10.1145/3570361.3613306(1-15)Online publication date: 2-Oct-2023
https://dl.acm.org/doi/10.1145/3570361.3613306
Jiang XYang XYing ZZhang LPan JChen S(2019)Segmentation of shallow scratches image using an improved multi-scale line detection approachMultimedia Tools and Applications10.1007/s11042-018-6222-z78:1(1053-1066)Online publication date: 1-Jan-2019
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Spurek PTabor JRola POciepka M(2017)ICA based on asymmetryPattern Recognition10.1016/j.patcog.2017.02.01967:C(230-244)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1016/j.patcog.2017.02.019
Sindagi VSrivastava S(2017)Domain Adaptation for Automatic OLED Panel Defect Detection Using Adaptive Support Vector Data DescriptionInternational Journal of Computer Vision10.1007/s11263-016-0953-y122:2(193-211)Online publication date: 1-Apr-2017
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Liu YLiu YChen Y(2011)High-speed inline defect detection for TFT-LCD array process using a novel support vector data descriptionExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.11.04638:5(6222-6231)Online publication date: 1-May-2011
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Liu YLiu YChen Y(2010)Fast support vector data descriptions for novelty detectionIEEE Transactions on Neural Networks10.1109/TNN.2010.205385321:8(1296-1313)Online publication date: 1-Aug-2010
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Tsneg YTsai D(2010)Defect detection of uneven brightness in low-contrast images using basis image representationPattern Recognition10.1016/j.patcog.2009.09.00643:3(1129-1141)Online publication date: 1-Mar-2010
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