A survey of automated visual inspection
Computer Vision and Image Understanding
Automatic defect classification for semiconductor manufacturing
Machine Vision and Applications
Independent component analysis: algorithms and applications
Neural Networks
Independent component analysis for noisy data: MEG data analysis
Neural Networks
Edge Detection and Texture Segmentation Based on Independent Component Analysis
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Independent Component Analysis of Textures
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Face recognition using independent component analysis and support vector machines
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Learning multiview face subspaces and facial pose estimation using independent component analysis
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Defect detection of IC wafer based on two-dimension wavelet transform
Microelectronics Journal
Automatic detection of Mura defect in TFT-LCD based on regression diagnostics
Pattern Recognition Letters
Journal of Intelligent Manufacturing
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In this paper, we propose a machine vision approach for automatic detection of micro defects in periodically patterned surfaces and, especially, aim at thin film transistor liquid crystal display (TFT-LCD) panels. The proposed method is based on an image reconstruction scheme using independent component analysis (ICA). ICA is first applied to a faultless training image to determine the de-mixing matrix and the corresponding independent components (ICs). The ICs representing the global structure of the training image are then identified and the associated row vectors of those ICs in the de-mixing matrix are replaced with a de-mixing row representing the least structured region of the training image. The reformed de-mixing matrix is then used to reconstruct the TFT-LCD image under inspection. The resulting image can effectively remove the global structural pattern and preserve only local anomalies. A number of micro defects in different TFT-LCD panel surfaces are evaluated with the proposed method. The experiments show that the proposed method can well detect various ill-defined defects in periodically patterned surfaces.