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應用動態影像處理之光流法於液晶顯示器面板之Mura瑕疵檢測 摘 要 本研究主要探討液晶顯示器面板製程中的Mura瑕疵，所謂Mura瑕疵意指在同一光源且相同底色之畫面下，因視覺感受到的光源不同的頻率響應而感覺亮度上的差異，此瑕疵在顯示器面板上呈現光源不均現象，並且與周圍背景具低對比度，因此需透過人工反覆以不同之視角來觀察面板上是否存在Mura瑕疵，而傳統之自動視覺檢測系統大都藉由靜態取像檢驗，此方式會因光源角度投射的位置不佳或是顯示器面板的擺放位置不佳，無法在低對比之影像有效偵測瑕疵。 本研究提出動態取像策略，對移動中之待測面板進行連續取像，使光源以不同角度投射面板而有效顯示Mura在影像中之區域，並利用相鄰不同時序影像中造成的瑕疵位置變異來凸顯其灰階差異。時序影像之差異偵測主要使用動態影像處理技術之光流法(Optical flow)計算相鄰連續影像中每一像素點之移動量，並設計量測移動量變化之特徵指標來標示Mura在影像中之區域。除了使用傳統二維光流法外，本研究同時針對單一移動方向開發一維光流法於連續影像之瑕疵檢測，以縮短計算時間，增加檢測效率。 本研究實驗對象包含彩色濾光片的Mura影像，玻璃基板的影像以及透過電腦模擬之影像(影像大小皆為 關鍵詞：機器視覺；瑕疵檢測；動態影像；光流法；Mura；TFT-LCD。 |
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Low-contrast surface inspection of mura defects in liquid crystal displays
using optical flow-based motion image analysis Student：Shin-Yang Tsai Advisor: Dr. Du-Ming Tsai Department of Industrial Engineering and Management ABSTRACT This research proposes a machine vision scheme for mura defect detection in TFT-LCD manufacturing. Mura is a Japanese word for blemish, which typically shows brightness imperfections from its surroundings in the surface. Since mura appears as a low-contrast region without clear edges in the surface, human inspectors need to continuously observe the hardly visible defect from different viewing angles. The traditional automatic visual inspection algorithms detect mura defects from individual still images. They neglect that a mura defect may not be visibly sensed in the image from a still system. In
this study, the TFT-LCD panel is assumed to move along a track, where
different light sources illuminate from different angles to the inspection
panel. While the TFT-LCD panel passes through a fixed camera, the light
reflection from different angles can effectively enhance the mura defect in the low-contrast motion images. This
research therefore proposes a motion detection scheme based on optical flow
techniques to identify mura defects in motion
images. Since the TFT-LCD moves along a single direction, both
two-dimensional (2D) and one-dimensional (1D) optical flow motion detection
methods are developed. Three discriminative features based on the flow
magnitude, mean flow magnitude and flow density in the optical flow field are
presented to extract the defective regions in each image of the motion
sequence. Both real glass substrates and synthetic panels are used to
evaluate the efficacy of the proposed inspection schemes. Experimental
results have shown that the proposed 1D optical flow method works as well as
the 2D optical flow method to detect very low-contrast mura
defects of small size, and achieves a high processing rate of 20 frames per
second for images of size 200 Keywords: Machine vision; Defect detection; Motion images; Optical flow;
Mura; TFT-LCD inspection |
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《範例說明》
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Spot mura原始影像 |
強化影像 |
偵測結果 |
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Line mura原始影像 |
強化影像 |
偵測結果 |
