臺灣博碩士論文加值系統

本研究主要使用賈柏濾波器(Gobor filtering)檢測結構性與統計性紋路之表面瑕疵，應用賈柏轉換法於紋路分析時，傳統之方法皆利用二維賈柏過濾器，其優點為將視窗之紋路資訊由二維空間域轉換為頻率域來擷紋路特徵值，可大幅低雜訊影響。就一個影像尺寸為 ，視窗尺寸為 之問題，二維賈柏轉換的運算複雜度為 ，其運算複雜且易受物件旋轉影響。
本研究為了改善傳統二維賈柏轉換法的缺點，提出了一維環形投影轉換法（1-D ring-projection transformation）與一維賈柏轉換法(1-D Gabor transformation)結合，利用一維環形投影轉換法將二維視窗資訊壓縮為一維資訊，可使賈柏轉換的運算複雜度大幅降低為 ，且不受物件旋轉之影響。
本研究分別對灰階與彩色紋路影像進行分析，並可對結構性與統計性紋路進行表面瑕疵檢測。其中針對彩色影像資訊輸入是擷取色彩模型(color model)中兩個與亮度無關之色彩特徵值(color features)，並經環形投影轉換後與一維彩色賈柏轉換法結合，用以偵測一維灰階賈柏轉換法無法凸顯之彩色紋路瑕疵。實驗中以紡織品、石材、塑膠品、LCD面板為測試樣本，由實驗結果得知，本研究之方法確實可以不受旋轉影響且能快速有效凸顯一致性紋路表面之異常瑕疵。

In this study, we use machine vision to defect embedded in homogenously textured surfaces. In order to avoid noise interference in the spatial domain, we employ the Gabor transform method in the spatial-frequency domain to detect local defects. Traditional Gabor-based methods use 2-D Gabor filters for texture analysis. They are computationally intensive and affected by rotation. Given a problem with image size and filter size , the computational complexity of 2-D Gabor filters is .
The proposed method in this study first 1-D ring-projection transformation to compress 2-D images to 1-D signals, and then employs 1-D gabor filters to detect defects. In this way, the computational complexity can be significantly reduced to , and the detection result is invariant to rotation. Both structural textures such as machined surfaces and textile fabrics and stochastic textures such as leather and castings in gray-level and color image are investigated. Experimental results have
shown that the proposed method is effective and efficient for detecting local defects in textured surfaces.

第一章 緒論 …………………………………………………………………….....1
1.1 研究動機與目的……………………………………..…………………...1
1.2 研究範疇與限制……………………………………………………..…...2
1.3 研究方法簡介………………………………………………………….....2
第二章 文獻回顧…………………………………………………………………...6
2.1 灰階影像之紋路瑕疵檢測………………………………………………..6
2.2 彩色影像之紋路瑕疵檢測………………………………………………..7
2.3 紋路分析…………………………………………………………………..8
2.3.1 空間域之紋路分析……………………………………………..9
2.3.2 頻率域之紋路分析……………………………………………..9
2.3.3 二維賈柏轉換法之介紹……………………………………….10
2.3.4 二維賈柏轉換法的應用………………………………………11
第三章 灰階紋路影像之檢測方法……………………………………………….13
3.1 灰階影像之環形投影法….………………..……………………………15
3.2 一維灰階賈柏轉換法……………………………………………………17
3.2.1 一維賈柏轉換法介紹…………………………………………17
3.2.2 一維賈柏轉換法應用於灰階影像瑕疵檢驗…………………18
3.3一維灰階賈柏轉換法之實驗分析………………………………………19
3.3.1 系統架構與實驗環境…………………………………………20
3.3.2 一維灰階賈柏轉換之實驗步驟………………………………22
3.3.3 一維賈柏轉換法之瑕疵檢測實驗……………………………23
3.3.4 灰階影像之視窗分析…………………………………………31
3.3.5灰階影像之光源影響分析…………………………………….32
3.3.6灰階影像旋轉影響分析…………………………………….…34
3.3.7灰階影像之瑕疵尺寸之影響分析…….……………….………34
3.4一維賈柏與傳統二維賈柏的實驗之比較分析 ……….………………..35
第四章 彩色紋路影像之檢驗方法…….…………………………………………56
4.1色彩模型…………………………………………………………………56
4.1.1 NTSC-RGB色彩模型………………………………………….57
4.1.2 CIE-XYZ色彩模型……………………………………………57
4.1.3 CIE-LAB色彩模型…………………………………………….57
4.1.4 Drg色彩模型…………………………………………………..58
4.2 彩色影像之環型投影方法………………………………………………59
4.3 一維彩色賈柏轉換法……………………………………………………60
4.3.1 一維彩色賈柏轉換法介紹 ...…………………………………60
4.3.2 一維彩色賈柏轉換法應用於彩色瑕疵檢驗...…….…………61
4.4 一維彩色賈柏轉換法之實驗結果….………………………………...…64
4.4.1 一維彩色賈柏轉換之實驗步驟………………………………64
4.4.2 探討彩色紋路之實驗結果…………………………………….64
4.4.3 彩色影像之視窗分析………………..………………………...66
4.4.4 彩色影像旋轉影響分析………………………………………66
4.4.5彩色影像之光源影響分析…………………………………….67
4.4.6彩色影像之瑕疵尺寸影響分析……………………………….67
4.5實驗結果之結論與建議………………………………………………….68
第五章 結論與建議…………………………………………….…………………85
參考文獻……………………………………………………………………………87
附錄一 一維賈柏轉換法與其他一維資訊結合….……………………………….90
附錄二 三、四章之實驗訓練數據………………………………………………..93
附錄三 實驗結果之型一型二誤差分析………………………………………….. 94
附錄四 程式說明…………………………………………………………………..95

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