在眾多平面顯示器的種類裡，又以在我們生活週遭許多應用都可看見的薄膜電晶體液晶顯示器(TFT-LCD)最受到矚目，例如：數位相機、手機、筆記型電腦、MP3、監視器與TV等。
而在TFT-LCD製程中首先面臨的最大的挑戰為如何檢出瑕疵，例如：Mura、SIMI、孔洞(pinhole)、刮痕(scratch)、結晶(grain)、SIRO-NUKE 、粉塵(particle)等瑕疵。但在本研究中因設備的因素，所以只檢測Mura、SIMI、孔洞、刮痕、結晶、SIRO-NUKE等項目。
本研究將藉由機器視覺理論發展一套以Haar小波轉換與空間域濾波為基礎之TFT-LCD自動化瑕疵檢測的演算法。實驗結果顯示所提出之的演算法可有效的去除面板上之正交週期性紋路與受光不均勻之問題，且能有較佳的檢測性能。

The thin film transistor liquid crystal display(TFT-LCD) is one of the most popular flat panel displays that can be found in many products in our daily life such as digital camera, cellular phone, notebook computer, MP3, monitor and TV, etc.
A big challenge in the manufacturing of TFT-LCD is to detect the defects such as Mura, SIMI, pinhole, scratch, grain, SIRO-NUKE, particle, etc. Due to the equipment limitation, we test only some defects that includes Mura, SIMI, pinhole, scratch, grain, SIRO-NUKE.
In this paper, we have developed a machine vision algorithm for automatic TFT-LCD defect detection based on Haar wavelet transform and spatial filtering. Experiments show that the proposed algorithm can effectively eliminate perpendicular periodical patterns, inhomogeneous illumination on TFT-LCD panels and has better performance for most of defects.

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