Automatic mura detection based on thresholding the fused normalized first and second derivatives in four directions Hani Jamleh Tsung-Yu Li Shen-Zhi Wang Chien-Wen Chen Chia-Chia Kuo Ko-Shun Wang Charlie Chung-Ping Chen Abstract — 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 order to 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%. Keywords — LCD, mura, defect detection, fused responses, digital image processing. DOI # 10.1889/JSID18.12.1058 1 Introduction Thin-film-transistor liquid-crystal displays (TFT-LCDs) have experienced rapid growth in terms of applications and manufacturing trends. A wide variety of products, e.g., note- books, TVs, monitors, PDAs, and mobile devices reflect a high demand in the production of LCDs because of their great overall performance, high resolution, and clear visibil- ity. To enhance the mass production of TFT-LCDs, espe- cially for quite large panel sizes, the quality control and defect inspection become a difficult task and is costly; hence, an automatic inspection system using machine vision would be the best choice instead of manual inspection. Machine vision plays an important role in the detection of stains or blemishs in an LCD, the so-called mura defect, which is defined as a visual defect in TFT-LCDs with low contrast and non-uniform brightness regions. Automating the process would enhance the detection rate and feasibility as well as reduce manpower cost to the lowest level because nowadays the manual inspection by skilled engineers is still considered to be the dominating process in the industry. This makes the process inconsistent, non-standardized, and costly. An automated, accurate, fast, consistent, and reliable inspection system becomes crucial for both the TFT-LCD manufacturer and the end-users to quantify and classify mura defects in the new manufactured LCD panels, in which it acts as a link between them for a better under- standing of LCDs’ quality. The main components of a TFT- LCD include a backlight module, liquid crystal, polarizer, color filter, and TFT array. Defects such as area, point, line, and curve would severely affect the visualization of a LCD panel. However, there are a variety of sources of defects in LCD panels, such as unevenness in the color filter, contami- nation between layers, and non-uniform distribution of liq- uid-crystal materials. In fact, many researchers in the field have paid immense attention to automate the mura detection and inspection. 1–4 The Video Electronics Standards Association (VESA) 5 and Semiconductor Equipment and Material International (SEMI) 6 have spent much effort on setting standards for classifying and quantizing defects, respectively. Several mura-detection algorithms have been proposed in the lit- erature. Chen et al. 1 proposed a detection algorithm based on the Laplacian of the Gaussian (LoG) filter for cluster muras. Song et al. 2 utilized morphological operational tools in image processing to improve the detectability, and it was mainly designed to detect blob-mura defects. Lee and Yoo 3 used modified regression diagnostics and Niblack’s threshold- ing to detect region-mura quantize it based on segmenting the panel image into small sub-windows. Many literature citations about the study of the effect of the size of the mura and its location related to the measurement of human visual perception have been studied. 11,12 This paper mainly focuses on the detection of mura defects that exists on the front of screen (FOS) of a LCD panel; these defects could appear with different shapes, sizes, contrasts, polarities, and types. The intent of the seg- mentation of a photographed FOS is to separate defects from the background. As a result, this process classifies each image pixel in a way that it could be defective (mura) or intact (background). In this study, we propose an efficient algorithm based on the fusion of the first- and second-order derivative responses in four directions in order to enhance the edges of mura defects in an LCD FOS sample. A labeled Received 05-18-10; accepted 10-15-10. H. O. Jamleh, T-Y. Li, and C. C-P. Chen are with the Graduate Institute of Electronics Engineering, National Taiwan University, Rm. 405, BL Bldg., Taipei, 10617 Taiwan, ROC; telephone +886-9290-62095, e-mail: jamleh@ntu.tw. S-Z. Wang is with the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taiwan, ROC. C-W. Chen, C-C. Kuo, and K-S. Wang are with the Measurement Technology Department, AU Optronics Technology Center, Hsinchu, Taiwan, ROC. © Copyright 2010 Society for Information Display 1071-0922/10/1812-1058$1.00. 1058 Journal of the SID 18/12, 2010