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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 蔡曜陽(Yao-Yang Tsai) | |
| dc.contributor.author | Shin-Yin Huang | en |
| dc.contributor.author | 黃世穎 | zh_TW |
| dc.date.accessioned | 2021-06-16T08:48:06Z | - |
| dc.date.available | 2018-08-26 | |
| dc.date.copyright | 2013-08-26 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-08-20 | |
| dc.identifier.citation | [1] 李洪全，實用坐標測量技術，化學工業出版社出版。
[2] D. W. Manthey, K. N. KnappII,and D. Lee, “Calibration of a laser range-finding coordinate-measuring machine”, Opt. Eng.33 (1994), p.3372–3380 [3] B. Kusnoto, C.A. Evans, “Reliability of a 3D surface laser scanner for orthodontic applications”,Am J Orthod Dentofacial Orthop, 122 (2002), p.342–348 [4] L. Zhang, B. Curless,and S. M. Seitz,“Rapid shape acquisition using color structured light and multi-pass dynamic programming”, 3D Data Processing Visualization and Transmission (2002), p.24–26 [5] D. M. Meadows, W. 0. Johnson, and J. B. Allen, “Generation of Surface Contours by Moire Patterns”, Applied Optics, Vol. 9, Issue 4 (1970), p. 942–947 [6] 張家壽，應用數位投影疊紋法於微小尺寸表面之量測，國立臺灣大學應用力 學研究所，碩士論文，民國91年 [7] Tianchu Li, Anbo Wang, Kent Murphy, and Richard Claus, “White-light scanning fiber Michelson interferometer for absolute position distance measurement”, Opt. Lett. 20 (1995), p.785–787 [8] M Sonka, V Hlavac,and R Boyle,“Image processing, analysis, and machine” Vision, Second Edition (1999). [9] Zhongren Wang ,Shuxian Wang ,Yanming Quan ,”Image Mosaic for On-machine Measurement of Large-scale Workpiece”,South China University of Technology (2010). [10] Tatiana Baidyk, Ernst Kussul, Oleksandr Makeyev and Graciela Velasco,“Pattern Recognition for Micro Workpieces Manufacturing”, (2009) [11] 魏依玲等，自動光學檢測(AOI)市場及技術發展趨勢調查，工研究產業業經 濟與資訊服務中心出版 (2002) [12] D. G. Hakala, R. C. Hillyard, P. F., Malraison, and B. F. Nource, “Natural Quadrics in Mechanical Design”, SIGGRAPH/81,Seminar on Solid modeling, Dallas, Texas (1981). [13] M. M. P. A. Vermeulen , P. C. J. N. Rosielle, and P. H. J. Schellekens. “Design of a high-precision 3D-coordinate measuring machine.” CIRP Annals-Manufacturing Technology 47.1 (1998), p 447-450. [14] Bartholomew O.Nnaji, Tzong‐Shyan Kang, Shuchieh Yeh,Jang‐Ping Chen, “Feature Reasoning for Sheet Metal Components”, International Journal of Production Research (1991), p.1868–1896. [15] C.Bradley , G. W. Vickers, and M. Milroy,“Reverse engineering of quadric surfaces employing three-dimensional laser scanning.” Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture 208.1 (1994),p 21–28. [16] Besl, Paul J., and Ramesh C. Jain.,“Invariant surface characteristics for 3D object recognition in range images.” Computer vision, graphics, and image processing 33.1 (1986), p 33–80. [17] 劉志堅，王義林，李建軍，肖祥芷，鈑金零件特徵識別方法的研究， 中國機械工程（2002）， p.2115–2117 [18] Han, JungHyun, and Aristides AG Requicha. “Integration of feature based design and feature recognition.” Computer-Aided Design 29.5 (1997), p 393–403. [19] M. A. Patricio , and D. Maravall. “A novel generalization of the gray-scale histogram and its application to the automated visual measurement and inspection of wooden Pallets.” Image and Vision Computing 25.6 (2007),p 805–816. [20] D. Unay ,B. Gosselin, O. Kleynen, V. Leemans, M. F. Destain, O. Debeir, Automatic grading of Bi-colored apples by multispectral machine vision. Computers and electronics in agriculture, 75.1 (2011), p 204–212. [21] Vira Naren. “Application of microcomputer in submicron level measurements and control of a positioning device.” Journal of microcomputer applications 18.2 (1995), p 149–164. [22]Wu, Wen-Yen, Mao-Jiun J. Wang, and Chih-Ming Liu. “Automated inspection of printed circuit boards through machine vision.” Computers in industry 28.2 (1996),p 103–111. [23] F. C. Tien, C. H. Yeh, and K. H. Hsieh. “Automated visual inspection for microdrills in printed circuit board production.” International Journal of Production Research 42.12 (2004), p 2477–2495. [24] Stojanovic, Radovan, et al. “Real-time vision-based system for textile fabric inspection.”,Real-Time Imaging 7.6 (2001),p 507–518. [25] S. Kurada and C. Bradley.“A review of machine vision sensors for tool condition monitoring.” Computers in Industry 34.1 (1997),p 55–72. [26] T. Pfeifer, and L. Wiegers.“Reliable tool wear monitoring by optimized image and illumination control in machine vision.” Measurement 28.3 (2000), p 209–218. [27] Canny, John. “A computational approach to edge detection.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 6 (1986), p 679–698 [28] Sharifi, Mohsen, Mahmoud Fathy, and Maryam Tayefeh Mahmoudi. “A classified and comparative study of edge detection algorithms.” Information Technology: Coding and Computing, Proceedings. International Conference on (2002). p 1–11. [29] Peli, Tamar, and David Malah. “A study of edge detection algorithms.” Computer Graphics and Image Processing 20.1 (1982). p 1–21. [30] Juneja, Mamta, and Parvinder Singh Sandhu.“Performance evaluation of edge detection techniques for images in spatial domain.” methodology 1.5 (2009).p 614–621. [31] Mokhtarian, Farzin, and Riku Suomela. “Robust image corner detection through curvature scale space.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 20.12 (1998). p 1376–1381. [32] Mehrotra, Rajiv, and James E. Gary. “Similar-shape retrieval in shape data management.” Computer 28.9 (1995). p 57–62. [33] Harris, Chris, and Mike Stephens. “A combined corner and edge detector.” Alvey vision conference(1988). Vol. 15. [34] Moravec, Hans P. “TOWARDS AUTOMATIC VISUAL BBSTACLE AVOIDANCE.” International Conference on Artificial Intelligence ,5th Massachusetts Institute of Technology(1977). [35] Hough, Paul VC. “Method and means for recognizing complex patterns.” U.S. Patent No. 3,069,654. 18 Dec. 1962. [36] Duda, Richard O., and Peter E. Hart. “Use of the Hough transformation to detect lines and curves in pictures.” Communications of the ACM 15.1 (1972).p 11–15. [37] Otsu, Nobuyuki. 'A threshold selection method from gray-level histograms.' Automatica 11.285-296 (1975). p23–27. [38]鐘國亮，影像處理與電腦視覺，第四版，p 185–189。 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59069 | - |
| dc.description.abstract | 自動光學檢測(Automated Optical Inspection，簡稱AOI)系統是一種結合了精密自動化機械、光學取像系統、邏輯演算技術等核心科技的光學影像檢測系統。
在現今講求高效率、高速化的工業產線上，AOI系統不僅能改良傳統上以人力使用光學儀器進行檢測的缺點，其應用層面包括從高科技產業之研發、製造品管，以至國防、民生、醫療、環保、電力…等領域。然而現今較具代表性的AOI系統多為以模板比對法為主的檢測系統，使得若要量測工件表面的尺寸時仍得將工件以其它儀器或是依賴人工方法來進行。為了補足尺寸量測上的不足之處，本研究則開發了一結合影像處理技術與幾何推理計算的影像重建算法，在利用圖像上的各輪廓的幾何特徵來把取得圖像之資料分割成符合各特徵定義的子資料群後，再將根據子資料群的幾何性質重組成一完整之輪廓圖，而輸出的輪廓圖不僅可以做為與工件之原設計圖進行比對之素材，分類過程中的各特徵點也能透過座標計算而生成工件各輪廓的尺寸資料，使得整個系統在測得工件缺陷之餘，還能有不錯的尺寸量測能力。 為了讓系統輸出的特徵點座標具有實際的長度意義，本研究則利用了一標準塊進行長度量測的觀察實驗，並建立了一線型的像素長度比例關係式。在以此比例關係式為長度量測基礎的狀況下，本研究所建立的影像處理方法不僅能完整地對線段、孔洞、凹槽、圓角、U型槽等常見的加工特徵進行輪廓重建，對於重建後的輪廓尺寸，本算法輸出的尺寸資料在線段部分之量測誤差平均百分比約為0.808%曲線部分之量測誤差平均百分比則為2.363%，且整個量測過程能在2.8秒內完成。顯示本研究所開發之輪廓重建算法不僅能適用於大部分的機械零組件，其運作速度亦能符合線上即時檢測的需求，讓AOI系統在檢測工件表否是否有瑕疵之餘，同時告知使用者工件的加工尺寸狀況。 | zh_TW |
| dc.description.abstract | AOI (Automated Optical Inspection) system is a combination of precision automation machine, optical image capture system, the logical calculus technology and other core technologies optical image detection system. In today's industrial production line, AOI system can not only improve the traditional optical instruments used to detect human shortcomings, the application level is also quite extensive. But most of the AOI systems today mostly depend on template matching method so that the workpiece surface to measure the size of the workpiece to still have to rely on other instruments or manual methods. To complement the dimension measurement on the inadequacies of today’s AOI systems, this study is the development of a combination of image processing techniques and geometric reasoning image reconstruction algorithm for computing. In the use of images on the geometric characteristics of the contours of the acquired image data into line with the characteristics of each group after the definition of subfolders, and then under the sub group re-composition of a complete geometric properties of the contour map.
Furthermore, in order to make the feature point coordinates of the system output with the actual length significance, the studies carried out using a standard block length measurements observation experiment, and the establishment of the line type of relationship of the pixel length ratio. In this proportional relationship between the amount of foundation for the long position, the institute created image algorithm can complete on line, holes, grooves, rounded, U-shaped groove machined features such as common contour reconstruction, while the size measurements, but also can achieve line error 0.808%, curve error 2.363% of measurement results. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T08:48:06Z (GMT). No. of bitstreams: 1 ntu-102-R00522738-1.pdf: 7051572 bytes, checksum: 4dac8618478b80125bfbd3b20a1682c9 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | 目 錄
口試委員會審定書………………………………………………… I 誌謝………………………………………………………………… II 中文摘要…………………………………………………………… III 英文摘要…………………………………………………………… IV 目錄………………………………………………………………… V 圖目錄……………………………………………………………. VIII 表目錄……………………………………………………………… XII 第一章 緒論……………………………………………………… 1 1.1 研究背景………………………………………………… 1 1.2 文獻回顧………………………………………………… 5 1.2.1 特徵分群技術回顧..……………………………… 5 1.2.2 機器視覺技術之應用……………………………… 7 1.3 研究動機與目的………………………………………… 9 1.4 論文大綱………………………………………………… 10 第二章 相關技術理論介紹……………………………………… 11 2.1 邊緣檢測………………………………………………… 11 2.1.1 邊緣檢測原理……………………………………… 11 2.1.2 常見的邊緣檢測運算子…………………………… 14 2.1.3 各邊緣檢測運算子之比較與選擇………………… 21 2.2 角點偵測………………………………………………… 24 2.2.1 Moravec演算法…………………………………… 25 2.2.2 Harris角點偵測法………………………………… 26 2.3 霍氏轉換………………………………………………… 30 2.3.1 霍氏轉換應用於直線偵測………………………… 30 2.3.2 以霍氏轉換法測圓………………………………… 33 2.4 影像中的門檻值決定…………………………………… 34 第三章 特徵重建算法之開發………………………………………37 3.1 演算法概念……………………………………………… 38 3.2 特徵分類運算之前置處理……………………………… 40 3.2.1 雜訊抑制…………………………………………… 40 3.2.2 特徵點提取………………………………………… 41 3.2.3 特徵點貼合………………………………………… 43 3.3特徵分類算法…………………………………………… 46 3.3.1 線段………………………………………………… 46 3.3.2 圓…………………………………………………… 51 3.3.3 弧…………………………………………………… 53 3.4輪廓重建流程………………………………………………57 第四章 特徵重建算法之系統建立與實驗方法………………… 63 4.1 特徵重建系統…………………………………………… 63 4.2 輪廓重建實驗…………………………………………… 66 4.3 尺寸估算實驗…………………………………………… 69 4.3.1 像素長度比例式之建立…………………………… 69 4.3.2 尺寸估算結果之誤差探討………………………… 70 4.4 實驗設備………………………………………………… 73 4.4.1 實驗基本設備……………………………………… 74 第五章 實驗結果與討論…………………………………………79 5.1 輪廓重建實驗…………………………………………… 79 5.1.1 理想圖像重建……………………………………… 79 5.1.2 實物重建…………………………………………… 84 5.2影響重建結果之因素探討…………………………………90 5.2.1 Harris算法………………………………………… 90 5.2.2 灰度………………………………………………… 97 5.2.3 溝槽………………………………………………… 100 5.3尺寸估算實驗…………………………………………… 103 5.3.1像素長度比例式之建立…………………………… 103 5.3.2角度對估算結果之影響…………………………… 106 5.3.3物距對估算結果之影響…………………………… 114 5.3.4亮度對估算結果之影響…………………………… 114 第六章 結論與未來展望……………………………………… 123 6.1 結論……………………………………………………… 123 6.2 未來展望………………………………………………… 126 參考文獻 ……………………………………………………… 127 | |
| dc.language.iso | zh-TW | |
| dc.subject | 機器視覺 | zh_TW |
| dc.subject | 影像處理 | zh_TW |
| dc.subject | 輪廓重建 | zh_TW |
| dc.subject | Contour reconstruction | en |
| dc.subject | Machine Vision | en |
| dc.subject | Image processing | en |
| dc.title | 以幾何特徵分析為基礎的工件認識算法之開發 | zh_TW |
| dc.title | The Development of Workpiece Understanding Algorithms
Based On Geometrical Analysis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳湘鳳(Shana Smith),張文桐 | |
| dc.subject.keyword | 機器視覺,影像處理,輪廓重建, | zh_TW |
| dc.subject.keyword | Machine Vision,Image processing,Contour reconstruction, | en |
| dc.relation.page | 130 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-08-20 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 機械工程學研究所 | zh_TW |
| 顯示於系所單位： | 機械工程學系 | |
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