Effective defect detection in thin film transistor liquid crystal display images using adaptive multi-level defect detection and probability density function

A new automated inspection algorithm is proposed for detecting critical defects based on adaptive multi-level defect detection and probability density function in thin film transistor liquid crystal display (TFT-LCD) images containing a background region’s non-uniform and random noises. To improve the detecting capability for a critical-defect-detecting algorithm, the background region’s non-uniformity is eliminated using statistical values such as the mean and standard deviation of a test image. For the defect detection, the candidate defects are collected on each detection level and used to find a probability density function based on Parzen-window technique. Through simulation it was verified that the proposed method has superior capability for detecting critical defects which results in smaller brightness difference between a defect and its neighbors.