基於區域分析和紋理特徵的視覺檢測

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姓名

黃宇辰(Yu-Chen Huang) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

基於區域分析和紋理特徵的視覺檢測
(Vision Inspection based on Blob Analysis and Texture Characterization)

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摘要(中)

以物件表面紋理特徵做為工業視覺檢測技術已逐漸成為主要趨勢之一。紋理特徵係藉由像素間的空間域關係來描述平滑度、粗糙度和型態規律性等區域特徵訊息。但空間域法易受到光源和雜訊影響，需融合BLOB分析才能達到精確的區塊特徵檢測目的。本研究因而設計了一個視覺檢測平台，結合影像前處理、BLOB分析、紋理分析功能模組，以及一個機率神經網路分類器，提供應用系統開發者做為紋理特徵選擇策略，可針對不同檢測應用快速選擇最佳的紋理特徵組合，形成智慧化的視覺檢測系統。本文最後採用地瓜品質案例，來驗證我們設計的視覺檢測平台，實驗結果顯示，整體紋理特徵平均辨識率可達76.28%。本系統結合PNN神經網路的智慧型選擇策略，擁有彈性化生成各類應用視覺檢測系統的優點。

摘要(英)

Object surface texture features have been increasingly applied in the technology of industrial visual inspection. Texture features refer to region features such as smoothness, roughness, and texture regularity described using inter-pixel spatial domain relationships. However, the spatial domain method is susceptible to light sources and noise, and consequently blob analysis must be incorporated to achieve accurate inspection of block features. To provide application developers with strategies for selecting texture features, this study designed a visual inspection platform that integrates visual preprocessing, blob analysis, and texture analysis modules, as well as a probabilistic neural network classifier. This platform is a smart visual inspection system that enables rapid selection of optimal texture-feature combinations in various inspections. Finally, the quality of sweet potatoes was used to verify the visual inspection platform developed in this study. Overall, the experimental results indicated an average recognition rate of 76.27% for the texture features of the sweet potatoes. By incorporating probabilistic neural network-based smart selection strategies, this platform can flexibly generate various applications of inspection systems.

關鍵字(中)

★ 紋理特徵
★ BLOB分析
★ 機率神經網路

關鍵字(英)

論文目次

摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章緒論 1
1.1 研究動機 1
1.2 研究目的 2
1.3 論文架構 3
第二章 BLOB分析 4
2.1 影像分割 4
2.1.1 Otsu法 5
2.1.2 變異數分割法 7
2.1.3 IHS色彩空間轉換 7
2.2形態學 8
2.2.1膨脹 9
2.2.2侵蝕 10
2.2.3閉合 10
2.2.4斷開 11
2.3 連接區塊標記 11
2.4 BLOB特徵值 13
第三章紋理分析 15
3.1 灰階共生矩陣 15
3.2 紋理特徵 17
3.3 紋理影像演算法 21
3.4 GLCM參數 22
3.4.1 影像灰階值量化 23
3.4.2 移動視窗大小 23
3.4.3 像素對應距離與方向 23
3.4.4 紋理特徵值選擇 23
3.5 PNN機率神經網路 26
3.5.1貝式分類原理 26
3.5.2 Parzen視窗法 27
第四章影像分析系統設計 31
4.1離散事件建模 31
4.2 區域分析和紋理特徵的視覺檢測系統架構 34
4.2.1區域分析 34
4.2.2紋理分析 35
4.2.3特徵值分析 36
4.3 區域分析和紋理特徵的視覺檢測系統設計 36
4.3.1 區域分析 38
4.3.2 紋理分析 39
4.3.3 特徵值分析 41
4.4 作業系統軟體合成 42
第五章實驗與結果討論 46
5.1 實驗環境 46
5.2 區域分析和紋理特徵的視覺檢測平台 49
5.3 區域分析實驗 49
5.4 紋理檢測實驗 50
5.4.1 紋理辨識模型參數 51
5.4.2 機率神經網路訓練辨識模型參數 56
5.4.3 紋理特徵驗證實驗 61
5.5實驗結果與討論 64
第六章結論與未來方向 66
6.1 結論 66
6.2 未來方向 67
參考文獻 68

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指導教授

陳慶瀚

審核日期

2016-7-25

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