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研究生:陳建堂
研究生(外文):Chien-tang Chen
論文名稱:偏光膜外觀瑕疵之影像檢測系統開發
論文名稱(外文):Development of an Image Inspection System for Appearance Defects in Polarizing Films
指導教授:黃昌群
指導教授(外文):Chang-Chiun Huang
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:高分子系
學門:工程學門
學類:化學工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:84
中文關鍵詞:外觀瑕疵檢測影像處理倒傳遞類神經網路
外文關鍵詞:Appearance defect inspectionImage processingBack-propagation neural network
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目前偏光膜瑕疵是利用人工檢測的方式,不僅費時而且容易因疲勞而產生檢測失誤,因此第三者客觀公正的專業檢測服務將可做為重要依據,所以本論文針對偏光膜外觀瑕疵應用影像處理來開發一套自動瑕疵檢測系統,而檢測的偏光膜瑕疵可分為五種:包括雲狀色差、條狀色差、點狀色差、刮痕與貼合不良。在影像處理過程中,先利用中值濾波器減少脈衝雜訊,由於瑕疵影像灰階差異不大,無法以一固定門檻值完整分割所有瑕疵類別,故使用統計式門檻值決定法配合灰階值大小,以選擇一個或兩個最佳門檻值來分割出瑕疵區域,再配合形態學中的閉合運算使瑕疵輪廓更平滑完整,並選擇瑕疵面積、平均灰階值和緊緻性做為瑕疵特徵。我們搜集80個瑕疵樣本,並規劃以不同數量之訓練樣本與固定的測試樣本進行實驗,利用20筆、30筆和40筆訓練樣本做為倒傳遞類神經網路的資料庫,對固定的40筆測試樣本作辨識,結果顯示在訓練樣本為30筆以上時,其辨識率皆可達到100%,驗證了倒傳遞類神經網路在訓練樣本足夠的資料庫下,可獲得相當準確的辨識率,成功被應用於偏光膜瑕疵自動檢測系統。
Currently, polarizing film defects are inspected by the human, which is tedious and easily leads to wrong judgments due to tiredness of eyes. Thus the objective inspection becomes an important and necessary basis. This thesis applies image processing and appropriate classifiers to develop an image inspection system for polarizing film appearance defects. The polarizing film defects include cloudy chromatism, stripy chromatism, spotted chromatism, scratch and pasting wrong, which are commonly occurred during processing. In image processing, the median filter is used to reduce the impulse noise of images. Since the gray values of all defects are close to each other, we cannot segment entire defect-areas of five defects by a fix threshold value. Therefore, the statistical threshold value decision method is used to choose one or two optimal threshold value with the difference of gray values in image segmentation, and then we use the closing operator in morphology to smooth the contour of defects. Moreover, the area, average gray value and compactness are selected as defect features. We divide eighty defect samples into three groups, which are the different amount of training samples and the fixed amount of testing samples, for experiment. The amounts of training samples are twenty, thirty and forty, and the amount of testing samples is forty. Finally, we have a database of the back-propagation neural network (BPNN) trained by the training samples and recognize the testing samples with the database. The result shows the recognition rate is 100% when the amount of training samples is greater than or equal to thirty; proving that the BPNN can achieve a high recognition rate with enough training samples in the database, and it can be successfully applied to the inspection of polarizing film defects.
摘要 I
ABSTRACT II
誌謝 III
目錄 IV
圖目錄 VIII
表目錄 XI
第1章 緒論 1
1.1 研究動機 1
1.2 研究步驟與方法 2
1.3 文獻回顧 3
1.4 論文架構 6
第2章 實驗設備 7
2.1 硬體設備 7
2.2 應用軟體 7
第3章 偏光膜 11
3.1 偏光膜原理與結構 11
3.2 偏光膜種類 14
3.3 偏光膜製造方法 16
第4章 數位影像處理 19
4.1 數位影像處理分析架構 19
4.1.1 影像前級處理 20
4.1.2 影像中級處理 21
4.1.3 影像後級處理 22
4.2 空間濾波 22
4.2.1 低通濾波器 24
4.2.1.1 平滑濾波器 25
4.2.1.2 中值濾波器 26
4.2.2 高通濾波器 28
4.2.2.1 拉普拉斯算子 28
4.2.2.2 索貝爾算子 29
4.3 影像分割 30
4.3.1 門檻值法 31
4.3.2 統計式門檻值決定法 31
4.4 形態學 34
4.4.1 標記化 35
4.4.2 侵蝕 36
4.4.3 膨脹 37
4.4.4 斷開 37
4.4.5 閉合 38
4.5 影像特徵擷取 39
第5章 分類器原理 40
5.1 倒傳遞類神經網路 41
5.1.1 倒傳遞類神經網路基本原理 41
5.1.2 倒傳遞類神經網路運作流程 42
5.1.3 倒傳遞類神經網路演算法 44
5.1.4 數據正規化 48
5.2 K最近鄰法 49
5.3 簡易貝氏分類器 51
第6章 實驗過程 53
6.1 實驗硬體架構 53
6.2 實驗步驟與流程 54
6.3 偏光膜瑕疵樣本 56
6.4 瑕疵影像分割 57
6.5 瑕疵影像的形態運算 59
6.6 瑕疵特徵擷取 62
6.7 瑕疵分類 66
6.7.1 瑕疵分類器參數設定 66
6.7.2 瑕疵樣本設計 68
6.7.3 瑕疵分類結果 69
6.8 結果與討論 72
第7章 結論與未來研究方向 75
7.1 結論 75
7.2 未來研究方向 76
參考文獻 78
附錄A 82
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