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首页> 外文会议>Advances in Image and Video Technology; Lecture Notes in Computer Science; 4319 >Computer-Aided Vision System for MURA-Type Defect Inspection in Liquid Crystal Displays
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Computer-Aided Vision System for MURA-Type Defect Inspection in Liquid Crystal Displays

机译：用于液晶显示器的MURA型缺陷检查的计算机辅助视觉系统

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摘要

This research proposes a new automated visual inspection method to detect MURA-type defects (color non-uniformity regions) on Liquid Crystal Displays (LCD). Owing to their space saving, energy efficiency, and low radiation, LCDs have been widely applied in many high-tech industries. However, MURA-type defects such as screen flaw points and small color variations often exist in LCDs. This research first uses multivariate Hotelling T~2 statistic to integrate different coordinates of color models and constructs a T~2 energy diagram to represent the degree of color variations for selecting suspected defect regions. Then, an Ant Colony based approach that integrates computer vision techniques precisely identifies the flaw point defects in the T~2 energy diagram. The Back Propagation Neural Network model determines the regions of small color variation defects based on the T2 energy values. Results of experiments on real LCD panel samples demonstrate the effects and practicality of the proposed system.
机译：这项研究提出了一种新的自动视觉检查方法，以检测液晶显示器（LCD）上的MURA型缺陷（颜色不均匀区域）。由于其节省空间，能源效率和低辐射，LCD已被广泛应用于许多高科技行业。但是，LCD中经常存在MURA型缺陷，例如屏幕缺陷点和较小的颜色变化。本研究首先使用多元Hotelling T〜2统计量整合颜色模型的不同坐标，并构建T〜2能量图来表示颜色变化的程度，以选择可疑缺陷区域。然后，结合计算机视觉技术的基于蚁群的方法可以精确地识别T〜2能量图中的缺陷点缺陷。反向传播神经网络模型根据T2能量值确定颜色变化小的缺陷区域。在实际的LCD面板样品上的实验结果证明了该系统的有效性和实用性。

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