隨著網路世代的興起與行動裝置的普及，人們使用電子產品的時間越來越長，對於眼睛的依賴與造成的負擔也越來越大，導致輔助視力的眼鏡需求量逐漸上升。眼鏡的鏡片因應近遠視的需求都是曲面的形狀，多焦點鏡片是一種新型的鏡片類型，可以同時兼具看較遠視野與看較近視野，但缺點是當鏡片在磨製的過程中，由於鏡片中不同區域的曲度有些微的變化，而導致鏡片在成像上容易出現光學變形。如果光學變形太嚴重，消費者在使用上會造成生活上的不便，因此鏡片的品質需要嚴格把關。現行檢測儀器只有量測鏡片之度數、透光率等鏡片數據，目前無儀器針對扭曲瑕疵進行量化與檢測，僅依賴專業人員的經驗判斷，因此本研究開發自動化扭曲瑕疵檢測系統，將具有一貫性、準確性、可循環檢測等優點。
本研究針對多焦點鏡片的扭曲瑕疵提出使用同心圓標準板成像在鏡片上，並計算每一圈同心圓邊點的質心半徑描述子，接著使用EWMA管制法將有扭曲瑕疵的區域找出來。之後藉著瑕疵影像與標準板影像進行線條差異比對，並將根據瑕疵發生的位置將影像分三個區域，再分別輸入三個區域的扭曲量特徵值，透過建立模糊歸屬函數與規則庫，最後使用GA-ANFIS模式進行瑕疵嚴重程度的分類。本研究初步使用小樣本實驗確定各程序之較佳參數設定，由較佳參數進行大樣本實驗，共使用150張測影像進行扭曲瑕疵偵測與分類。實驗結果顯示，本研究方法之扭曲瑕疵誤判率(α')為10.94%、檢出率(1-β)為81.09%且瑕疵嚴重程度之正確分類率(1-γ)達94%。

The using time of electronic devices is becoming longer for people’s daily lives, and the burden of people’s eyes is getting larger, while we approaching the rising of internet generation and the popularity of mobile devices. Thus, the demand for eyes-glasses is gradually increasing. The lenses of glasses are made to be curved to meet requirements of myopia and hyperopia. Multifocal lens is a new type of lens that is designed to provide focus of both distance and near objects. The main risk of producing multifocal lens is that the lens is easy to cause optical distortion in imaging while different regions of the lens have different curvatures when the lens is in the process of grinding. If the optical distortion defects are too serious on lens, the consumers will have distorted scenes in their sights and cause inconvenience in lives. Therefore, the quality of the lens needs to be strictly controlled. This study develops an automated distortion defect detection system for multifocal lenses to replace professional inspectors relying on experienced judgments.
In this paper, we propose a novel approach based on a standard pattern of concentric circles to inspect optical distortion defects on multifocal lenses. First, we calculate edge points of each circle of the concentric circles based on a centroid-radii model. Second, we find distortion defect areas through the exponentially weighted moving average (EWMA) control scheme. Third, we make difference comparison between defect image and standard pattern image. Fourth, we divide the image into three levels of severity according to defect locations on the lens. Fifth, we input the distortion deviations for the three levels and set up the fuzzy membership functions and inference rule base. Finally, the GA-ANFIS model is applied to classify the levels of severity for the distortion defects. Experimental results show that the proposed methods achieves a high 94% correct classification rate of severity of distortion defect, 81.09% distortion defect detection rate and 10.94% false alarm rate.

目錄
摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VII
表目錄 X
第一章 緒論 1
1.1 研究背景 1
1.2 鏡片簡介 1
1.3 鏡片光學像差類型介紹 5
1.4 畸變瑕疵檢驗 7
1.5 研究動機與目的 8
1.6 鏡片檢測之困難與限制 11
1.7 論文架構 12
第二章 文獻探討 13
2.1 自動化視覺檢測系統 13
2.2 扭曲瑕疵檢測 14
2.3 鏡片檢測 15
2.4 物件形狀描述與檢測 16
2.5 微量偏移管制法之應用 18
2.6 模糊理論用於瑕疵分類 19
第三章 研究方法之相關學理 23
3.1 形狀描述子 23
3.1.1 鍊碼 23
3.1.2 傅立葉描述子 25
3.1.3 小波描述子 25
3.1.4 質心-半徑描述子 25
3.2 微量偏移管制法 28
3.2.1 移動平均管制法 28
3.2.2 累積和管制法 29
3.2.3 指數加權移動平均管制法 30
3.3 結合基因演算法的調適性網路模糊推論系統 32
3.3.1 模糊推論系統 32
3.3.2 調適性網路模糊推論系統 36
3.3.3 基因演算法 38
第四章 研究流程與技術應用 43
4.1 模擬影像製作與前處理 45
4.1.1 樣本影像之二值化Otsu法 45
4.1.2 樣本影像之細線化 47
4.2 樣本影像扭曲瑕疵之距離特徵擷取 48
4.3 微量偏移管制法用於特徵值異常偵測 50
4.3.1 扭曲瑕疵偵測之CUSUM法 51
4.3.2 扭曲瑕疵偵測之EWMA法 54
4.4 扭曲程度判斷之GA-ANFIS分類方法 58
4.4.1 扭曲量之模糊推論系統 59
4.4.2 扭曲量之調適性網路模糊推論系統(ANFIS) 63
4.4.3 扭曲量之基因演算法 66
第五章 實驗與結果分析 73
5.1 硬體架設與影像檢測軟體開發 73
5.2 扭曲瑕疵檢測之績效指標 75
5.3 扭曲檢測方法之較佳參數設定 76
5.3.1 累積和管制法之參數設定 77
5.3.2 指數加權移動平均管制法之參數設定 80
5.3.3 基因演算法參數設定 84
5.4 大樣本實驗之扭曲瑕疵檢測績效分析與方法比較 88
5.5 敏感度分析 95
5.5.1 標準圖案之不同線條粗細對檢測效益的影響 95
5.5.2 不同光線的明亮度對檢測效益的影響 99
5.5.3 不同鏡片顏色對於檢測效益的影響 102
5.5.4 不同標準板圖案對於檢測效益的比較 105
第六章 結論與後續研究方向 117
6.1 結論 117
6.2 未來研究方向 119
參考文獻 120


圖目錄
圖1 多焦點鏡片之功能區塊劃分圖 4
圖2 使用鏡片近距離看階梯之扭曲示意圖 (a)有扭曲; (b)無扭曲 4
圖3 使用鏡片遠距離看建築物之扭曲示意圖 (a)有扭曲; (b)無扭曲 4
圖4 汽車車頭燈之球面像差示意圖 5
圖5 路燈之彗星像差示意圖 5
圖6 文字之像散像差示意圖 6
圖7 樹林之場曲像差示意圖 6
圖8 畸變圖形 (a)桶形畸變; (b)枕形畸變 7
圖9 鏡片製造商之檢驗鏡片數據圖 8
圖10 使用不同多焦點鏡片拍攝結果 12
圖11 四方向鍊碼與八方向鍊碼 24
圖12 八方向鍊碼編碼範例 24
圖13 質心-半徑模型用於形狀描述 26
圖14 模糊推論系統架構 33
圖15 常用的歸屬函數圖 34
圖16 扭曲瑕疵使用三角形歸屬函數之重心法去模糊化圖形 35
圖17 ANFIS之網路架構圖 36
圖18 基因演算法之流程圖 38
圖19 建立族群之染色體 39
圖20 輪盤選擇法之示意圖 40
圖21 基因演算法之交配功能圖 41
圖22 本研究多焦點鏡片扭曲瑕疵分類系統之流程圖 44
圖23 影像模擬扭曲瑕疵之前後比較 (a)同心圓標準板(無扭曲); (b)扭曲瑕疵之樣本影像 45
圖24 樣本影像之二值化 47
圖25 樣本影像之細線化 47
圖26 質心至同心圓邊點之距離示意圖 49
圖27 (a)樣本影像之其中一圈同心圓; (b)為(a)同心圓之距離特徵描述結果 50
圖28 瑕疵影像之其中一圈同心圓距離特徵輸入至CUSUM管制圖之偵測結果 52
圖29 第1~8圈同心圓之距離特徵使用CUSUM管制圖之偵測結果 53
圖30 瑕疵影像之其中一圈同心圓距離特徵輸入至EWMA管制圖之偵測結果 55
圖31 第1~8圈同心圓之距離特徵使用EWMA管制圖之偵測結果 57
圖32 扭曲瑕疵影像與同心圓標準板重疊之差異比較圖 58
圖33 扭曲瑕疵發生位置之嚴重度分區影像 59
圖34 本研究使用ANFIS之架構網路圖 64
圖35 結合基因演算法的調適性網路模糊推論系統 71
圖36 多焦點鏡片之扭曲瑕疵檢測方法與分類之各步驟過程 72
圖37 本研究架設檢驗系統之示意圖 74
圖38 本研究開發扭曲瑕疵檢測系統之使用者介面 74
圖39 CUSUM管制法用於小樣本實驗之檢測ROC曲線 78
圖40 部分樣本影像使用CUSUM法之扭曲瑕疵檢測結果 79
圖41 EWMA管制法用於小樣本實驗之檢測ROC曲線 82
圖42 部分樣本影像使用EWMA法之扭曲瑕疵檢測結果 83
圖43 GA模式之不同族群大小對於瑕疵分類正確率之曲線圖 84
圖44 GA模式之不同交配率對於瑕疵分類正確率之曲線圖 85
圖45 GA模式之不同突變率對於瑕疵分類正確率之曲線圖 86
圖46 不同因子水準參數組合分類正確率圖 88
圖47 移動平均法之期數參數ROC曲線 91
圖48 不同瑕疵偵測方法之ROC曲線圖 92
圖49 不同瑕疵偵測方法之檢測結果比較圖 93
圖50 不同線條粗細之樣本影像 96
圖51 待測影像改變線條粗細後之檢測績效ROC曲線 97
圖52 待測影像改變線條粗細後之瑕疵檢測結果 98
圖53 待測影像不同亮度影像與其對應之灰階方圖 99
圖54 待測影像改變影像亮度後之檢測績效ROC曲線 100
圖55 待測影像改變影像亮度之瑕疵檢測結果 101
圖56 (a)橘色鏡片、(b)綠色鏡片與(c)透明鏡片之扭曲瑕疵影像 102
圖57 待測影像不同鏡片顏色檢測績效之ROC曲線 103
圖58 待測影像不同顏色之瑕疵檢測結果 104
圖59 三種標準板製作同一種扭曲瑕疵之過程影像 107
圖60 六種變形程度與三種不同區域大小在三種標準板之扭曲檢出績效圖 109
圖61 小區域配合不同變形程度之檢測結果 110
圖62 中區域配合不同變形程度之檢測結果 111
圖63 大區域配合不同變形程度之檢測結果 112
圖64 六種不同變形程度對於三種標準板之檢測績效圖 113
圖65 不同區域大小對於三種檢驗板之績效圖 114
圖66 不同標準板之檢測ROC曲線 115
圖67 不同扭曲程度對於三種標準板之檢測效益比較 116
圖68 不同扭曲區域大小對於三種標準板之檢測效益比較 116


表目錄
表1 常用鏡片類型之特性比較表 2
表2 透明產品產生扭曲瑕疵之相關比較表 10
表3 相關文獻比較表 21
表4 扭曲瑕疵之模糊集合定義表 60
表5 不同扭曲量區域之特徵值歸屬函數表 61
表6 本研究所提出之模糊推論規則庫 62
表7 CUSUM管制法用於小樣本實驗之檢測績效評估表 77
表8 EWMA管制法用於小樣本實驗之檢測績效評估表 80
表9 GA模式之不同族群大小參數對於檢測績效評估的結果 84
表10 GA模式之不同交配率參數對於檢測績效評估的結果 85
表11 GA模式之不同突變率參數對於檢測績效評估的結果 86
表12 本研究之基因演算法較佳參數設定 86
表13 不同水準與因子參數設計表 87
表14 各因子水準參數組合結果表 87
表15 大樣本實驗之樣本彙整表 88
表16 大樣本實驗之檢測績效結果 89
表17 大樣本之扭曲嚴重程度分類結果表 89
表18 移動平均法之期數參數績效評估表 90
表19 不同瑕疵偵測方法之比較結果表 91
表20 各方法之實驗結果彙整表 94
表21 待測影像改變線條粗細之檢測績效表 96
表22 待測影像改變影像亮度後之檢測績效表 100
表23 待測影像不同鏡片顏色之檢測績效表 102
表24 三種不同標準板之特性比較表 105
表25 不同扭曲之變形程度範圍表 107
表26 不同扭曲之區域大小之範圍表 108
表27 每種標準板測試樣本之影像張數表 108
表28 六種扭曲變形程度與三種扭曲區域大小之不同組合在三種標準板模擬影像之扭曲檢出績效表 109
表29 六種不同變形程度對於三種標準板之績效表 113
表30 不同區域大小對於三種標準板之績效表 114
表31 不同標準板之檢測績效表 115

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