Defect Inspection Of Patterned Thin Film Transistor-Liquid Crystal Display Panels Using A Fast Sub-Image-Based Singular Value Decomposition

Thin film transistor-liquid crystal displays (TFT-LCDs) have become increasingly attractive and popular as display devices. A machine vision approach is proposed for automatic inspection of microdefects in patterned TFT-LCD surfaces. The proposed method is based on a global image reconstruction scheme using singular value decomposition. A partition procedure that separates the input image into non-overlapping sub-images is used to reduce the computation time of singular value decomposition. Taking the pixel image as a matrix, the singular values on the decomposed diagonal matrix represent different structural details of the TFT-LCD image. The proposed method first selects the dominant singular values that represent the repetitive orthogonal-line texture of the TFT-LCD surface. It then reconstructs the matrix by excluding the dominant singular values. The resulting image can effectively remove the background texture and preserves anomalies distinctly. The experiments have evaluated a variety of TFT-LCD microdefects including pinholes, scratches, particles and fingerprints at different image resolutions. The experimental results reveal that the proposed method is effective and efficient for microdefect inspection of TFT-LCD panels.