Future Generation Computer Systems
Integrated machine learning approaches for complementing statistical process control procedures
Decision Support Systems
Pattern Recognition
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Wavelet-based principal component analysis applied to automated surface defect detection
ACC'08 Proceedings of the WSEAS International Conference on Applied Computing Conference
Computer-Aided Vision System for Surface Blemish Detection of LED Chips
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
A Wavelet-Based Neural Network Applied to Surface Defect Detection of LED Chips
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
WSEAS Transactions on Computer Research
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This research proposes a new automated visual inspection method to detect MURA-type defects (color non-uniformity regions) on Liquid Crystal Displays (LCD). Owing to their space saving, energy efficiency, and low radiation, LCDs have been widely applied in many high-tech industries. However, MURA-type defects such as screen flaw points and small color variations often exist in LCDs. This research first uses multivariate Hotelling T2 statistic to integrate different coordinates of color models and constructs a T2 energy diagram to represent the degree of color variations for selecting suspected defect regions. Then, an Ant Colony based approach that integrates computer vision techniques precisely identifies the flaw point defects in the T2 energy diagram. The Back Propagation Neural Network model determines the regions of small color variation defects based on the T2 energy values. Results of experiments on real LCD panel samples demonstrate the effects and practicality of the proposed system.