Computer-Aided Vision System for MURA-Type Defect Inspection in Liquid Crystal Displays
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 T 2 statistic to integrate different coordinates of color models and constructs a T 2 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 T 2 energy diagram. The Back Propagation Neural Network model determines the regions of small color variation defects based on the T 2 energy values. Results of experiments on real LCD panel samples demonstrate the effects and practicality of the proposed system.
KeywordsComputer vision system MURA-type defects Hotelling T2 statistics Ant colony algorithm Back propagation network
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