均值移動(Mean-shift)平滑技術於表面瑕疵檢測之探討

The study of mean shift smoothing for defect detection in low-contrast surfaces and heterogeneous surfaces

婁介宇(Jie-Yu Lou)

指導教授 ： 蔡篤銘

元智大學/工程學院/工業工程與管理學系/碩士(2009年)

https://doi.org/10.6838/YZU.2009.00086

摘要

本研究主要是探討Mean-shift平滑技術於表面瑕疵檢測之應用，主要檢測之表面對象為低對比且光不均之無紋路影像和具異質性之紋路影像。Mean-shift方法具有自動叢集之能力，應用在影像處理上主要為影像平滑，具有消除雜訊(denoising)並同時保留邊緣之功能。

本研究針對無紋路影像之表面特性，利用Mean-shift平滑過程中每一像素點之收斂位置設計出位移量(fd)指標和灰階差收斂權重(fw)指標。位移量(fd)指標為根據Mean-shift方法中正常區域之像素點會比瑕疵區域產生較小的移動量來區分正常和瑕疵區域，而灰階差收斂權重(fw)指標則根據正常背景點和瑕疵區域之權重加總值的差異，來區分正常背景點和瑕疵區域之像素點。針對具異質性紋路表面上之污漬瑕疵，本研究先藉由量測影像中邊緣點(Edge)之梯度(Gradient)方向的亂度(Entropy，熵)將灰階影像轉為熵值影像，再利用Mean-shift平滑方法對此熵值影像進行處理以平滑背景點之熵值而保留瑕疵點之熵值，突顯出正常之背景區域和瑕疵區域。

本研究針對無紋路之TFT-LCD面板上之Mura瑕疵與異質性紋路之多晶矽太陽能晶片上之指觸或污漬瑕疵進行分析，實驗結果顯示透過Mean-shift平滑技術可有效達到區分正常背景和瑕疵區域之檢測目的。

關鍵字

機器視覺 ； 瑕疵檢測 ； 均值移動(Mean-shift)平滑技術 ； 紋路指標設計

並列摘要

The mean shift technique has been an attractive alternative for noise removal, region segmentation and object tracking in image processing. In this thesis, the feasibility of mean shift smoothing for defect detection in complicated surfaces is studied. The proposed methods especially focus on low-contrast non-textured surfaces such as mura defects (uneven brightness) in TFT-LCD panels, and the heterogeneous surfaces such as polycrystalline solar wafers.

Mean shift smoothing involves an iterative procedure that shifts each data point to the mode of the data points based on a kernel estimator of density. For non-textured surfaces, two mean shift-based methods are proposed. The first method shifts each pixel to the mode in the image, and the distance between the original pixel location and its converged position is used as the discrimination measure. A defect-free pixel will converge fast in its neighborhood and results in a small shift, while a defective pixel will need a larger shift to converge. In order to speed up the computation, a weight measure that uses the kernel function to calculate the gray-level variation in the spatial window in one single mean-shift iteration is also proposed for detecting low-contrast defects. For heterogeneous solar wafers, the fingerprint and contamination defects are studied. Since the grain edges in the polycrystalline wafer in a small spatial window show more consistent edge directions and a defect region presents high variation of edge directions, the entropy of gradient directions of each pixel in a small neighborhood window is first calculated to convert the gray-level image into an entropy image. The mean-shift smoothing procedure is then performed to remove defect-free grain edges in the entropy image. The preserved edge points in the resulting image are declared as defective ones. Experimental results have shown that mean-shift technique can be an effective tool for low-contrast defect detection in non-textured surfaces. It also performs well for defect detection in heterogeneous surfaces if the defect features can be adequately extracted.

並列關鍵字

Machine vision ； defect detection ； surface inspection ； mean-shift smoothing ； Solar wafer

參考文獻

張志傑，2008年，「應用非線性擴散模式於異質性紋路之多晶太陽能晶片表面微裂瑕疵檢測」，碩士論文，私立元智大學工業工程與管理研究所。

郭家成，2005年，「薄膜電晶體液晶顯示器Mura瑕疵檢測技術之研發」，碩士論文，國立台北科技大學自動化科技研究所。

陳心怡，2007年，「太陽能電池板表面瑕疵檢測」，碩士論文，國立中央大學資訊工程研究所。

蔡欣儒，2007年，「太陽能電池板的尺寸量測與線路瑕疵檢測」，碩士論文，國立中央大學資訊工程研究所。

周政衛，2005年，「TFT-LCD Mura缺陷自動化光學檢測」，碩士論文，國立成功大學製造工程研究所。