Se-Yun KIM, Young-Chul SONG, Chang-Do JUNG¹, and Kil-Houm PARK^*

School of Electrical Engineering and Computer Science, Kyungpook National University, Daegu 702-701, Korea
¹Department of Mathematics, Kyungpook National University, Daegu 702-701, Korea

(Received March 16, 2010; Revised December 18, 2010; Accepted December 27, 2010)

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.

Key words: TFT-LCD image, defect detection, multi-level, probability density function

^*E-mail address: khpark@ee.knu.ac.kr