Thin film transistor-liquid crystal displays (TFT-LCD), owing to their space saving, energy efficiency, and low radiation, have been replacing cathode-ray tubes (CRT). However, defects such as screen flaw points and small color deviations often exist in TFT-LCDs. To detect the MURA-type defects, the color non-uniformity regions, this research proposes a new automated visual inspection method. We first use multivariate Hotelling T 2  statistic to integrate different coordinates of color models and construct a T 2  energy diagram to represent the degree of color deviations 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. And, 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 TFT-LCD panel samples demonstrate the effects and practicality of the proposed system.
['Hong-Dar Lin', 'Chih-Hao Chien']
Automated Detection of Color Non-Uniformity Defects in TFT-LCD