Independent component analysis-based defect detection in patterned liquid crystal display surfaces

摘要

In this paper, we propose a machine vision approach for automatic detection of micro defects in periodically patterned surfaces and, especially, aim at thin film transistor liquid crystal display (TFT-LCD) panels. The proposed method is based on an image reconstruction scheme using independent component analysis (ICA). ICA is first applied to a faultless training image to determine the de-mixing matrix and the corresponding independent components (ICs). The ICs representing the global structure of the training image are then identified and the associated row vectors of those ICs in the de-mixing matrix are replaced with a de-mixing row representing the least structured region of the training image. The reformed de-mixing matrix is then used to reconstruct the TFT-LCD image under inspection. The resulting image can effectively remove the global structural pattern and preserve only local anomalies. A number of micro defects in different TFT-LCD panel surfaces are evaluated with the proposed method. The experiments show that the proposed method can well detect various ill-defined defects in periodically patterned surfaces.