Automatic mura detection based on thresholding the fused normalized first and second derivatives in four directions

— The size of flat-panel liquid-crystal displays is getting larger; as a result, it is becoming harder to inspect for defects and may require a human visual inspector to judge the severity of the defects on the final product. Recently, mura phenomenon, which is defined as a visual blemish with non-uniform shapes and boundaries, is becoming a serious unpleasant effect which needs to be detected and inspected in orderto standardize the LCD's quality. Hence, an automation process based on machine vision has proven to be a good choice to facilitate and stabilize the process. An effective general algorithm for detecting different types of mura defects with various contrast, shape, and direction, based on the fusion of the normalized magnitude of first- and second-order derivative responses in four directions, is proposed. The experiments applied on various types of pseudo-mura with different shapes show an efficient detection rate of more than 90%.