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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82337
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dc.contributor.advisor施吉昇(Chi-Sheng Shih)
dc.contributor.authorShih-Hsiang Chengen
dc.contributor.author鄭士驤zh_TW
dc.date.accessioned2022-11-25T07:29:24Z-
dc.date.available2025-01-01
dc.date.copyright2021-11-02
dc.date.issued2021
dc.date.submitted2021-10-26
dc.identifier.citation[1] J.-F. Seurin, G. Xu, B. Guo, A. Miglo, Q. Wang, P. Pradhan, J. D. Wynn, V. Khalfin, W.-X. Zou, C. Ghosh, and R. V. Leeuwen, “Efficient vertical-cavity surface-emitting lasers for infrared illumination applications,” in Vertical-Cavity Surface-Emitting Lasers XV, J. K. Guenter and C. Lei, Eds., vol. 7952, International Society for Optics and Photonics. SPIE, 2011, pp. 108 – 117. [Online]. Available:https://doi.org/10.1117/12.873933 [2] C. Zhang, P. Huang, and F. Chiang, “Microscopic phase-shifting profilometry based on digital micromirror device technology.” Applied optics, vol. 41 28, pp. 5896–904, 2002. [3] Z. Song, S. Tang, F. Gu, C. Shi, and J. Feng, “Doe-based structured-light method for accurate 3d sensing,” Optics and Lasers in Engineering, vol. 120, pp. 21–30, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0143816618317408 [4] Y. Zhuang, F. Yan, and H. Hu, “Automatic extrinsic self-calibration for fusing data from monocular vision and 3-d laser scanner,” Instrumentation and Measurement, IEEE Transactions on, vol. 63, pp. 1874–1876, 07 2014. [5] K. Luebke, Coordinate Measuring Machine. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 285–289. [6] R. I. Hartley and P. Sturm, “Triangulation,” Comput. Vis. Image Underst., vol. 68, no. 2, p. 146–157, Nov. 1997. [Online]. Available: https://doi.org/10.1006/cviu.1997.0547 [7] S. Ramalingam, S. K. Lodha, and P. Sturm, “A generic structure-from-motion framework,” Computer Vision and Image Understanding, vol. 103, no. 3, pp. 218–228, 2006, special issue on Omnidirectional Vision and Camera Networks. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1077314206000695 [8] D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, Nov 2004. [Online]. Available: https://doi.org/10.1023/B:VISI.0000029664.99615.94 [9] K. Dawson-Howe and D. Vernon, “Simple pinhole camera calibration,” International Journal of Imaging Systems and Technology, vol. 5, 1994. [10] Z. Zhang, “A flexible new technique for camera calibration,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, pp. 1330 – 1334, 12 2000. [11] C. G. Harris and M. Stephens, “A combined corner and edge detector.” in Alvey Vision Conference, C. J. Taylor, Ed. Alvey Vision Club, 1988, pp. 1–6. [12] G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, 2000. [13] J. J. Moré, “The levenberg-marquardt algorithm: Implementation and theory,” in Numerical Analysis, G. A. Watson, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 1978, pp. 105–116. [14] J. Salvi, J. Pagès, and J. Batlle, “Pattern codification strategies in structured light systems,” PATTERN RECOGNITION, vol. 37, pp. 827–849, 2004. [15] J. Geng, “Structured-light 3d surface imaging: a tutorial,” Adv. Opt. Photon., vol. 3, no. 2, pp. 128–160, Jun 2011. [Online]. Available: http://aop.osa.org/abstract.cfm?URI=aop-3-2-128 [16] J. Posdamer and M. Altschuler, “Surface measurement by space-encoded projected beam systems,” Computer Graphics and Image Processing, vol. 18, no. 1, pp.1–17, 1982. [Online]. Available: https://www.sciencedirect.com/science/article/pii/0146664X8290096X [17] V. Srinivasan, H. C. Liu, and M. Halioua, “Automated phase-measuring profilometry of 3-d diffuse objects,” Appl. Opt., vol. 23, no. 18, pp. 3105–3108, Sep 1984. [Online]. Available: http://ao.osa.org/abstract.cfm?URI=ao-23-18-3105 [18] P. Payeur and D. Desjardins, “Structured light stereoscopic imaging with dynamic pseudo-random patterns,” in Image Analysis and Recognition, M. Kamel and A. Campilho, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 687–696. [19] J. Boisvert, M.-A. Drouin, and P.-M. Jodoin, “High-speed transition patterns for video projection, 3d reconstruction, and copyright protection,” Pattern Recognition, vol. 48, no. 3, pp. 720–731, 2015. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0031320314002222 [20] L. Keselman, J. I. Woodfill, A. Grunnet-Jepsen, and A. Bhowmik, “Intel realsense stereoscopic depth cameras,” CoRR, vol. abs/1705.05548, 2017. [Online]. Available: http://arxiv.org/abs/1705.05548 [21] H. K. Nishihara, “Practical Real-Time Imaging Stereo Matcher,” Optical Engineering, vol. 23, no. 5, pp. 536 – 545, 1984. [Online]. Available: https://doi.org/10.1117/12.7973334 [22] S. Diamond and S. Boyd, “CVXPY: A Python-embedded modeling language for convex optimization,” Journal of Machine Learning Research, vol. 17, no. 83, pp.1–5, 2016. [23] A. Agrawal, R. Verschueren, S. Diamond, and S. Boyd, “A rewriting system for convex optimization problems,” Journal of Control and Decision, vol. 5, no. 1, pp.42–60, 2018.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82337-
dc.description.abstract在常見的三維量測光源中,VCSEL陣列光源具有小尺寸的優勢。這使得它可以在狹小空間被用作三維量測的光源。若能在狹小空間達到次毫米的量測精度,將會有很多應用。例如可用於量測工業管線的內壁,或者用於內視鏡手術上。儘管以DLP projector光源的三維量測裝置可以達到微米級,但它的體積相對於這些應用而言太大。目前大部份使用VCSEL array量測裝置的精度都在1毫米以上。不過,我們認為VCSEL array事實上有機會達到次毫米級的量測精度,因為它的發光點間距在數十微米的等級。本研究提出了一個利用VCSEL陣列光源進行三維量測的架構,包含校正方式以及三維重建的演算法,其複雜度為O(z2n + n2),其中n為光點數,z為一個小常數5。這個方法透過階高塊驗證,最大高度估計誤差在0:017毫米以內。另外,本研究也提出了如何應用此架構於LGA 1155處理器腳座檢測問題上,而這項檢測需要檢查腳座上金屬球的位置是否正常範圍內:垂直方向偏移0:250毫米及水平方向偏移0:125毫米以內。這證實了本研究的方法具有工業應用價值。zh_TW
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Previous issue date: 2021
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dc.description.tableofcontents口試委員會審定書i 致謝ii 摘要iii Abstract iv 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 2 Background and Related Works 3 2.1 3D Sensing Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.1.1 Contact Based 3D Sensing . . . . . . . . . . . . . . . . . . . . . 3 2.1.2 Time of Flight Method . . . . . . . . . . . . . . . . . . . . . . . 4 2.1.3 Triangulation-Based Methods . . . . . . . . . . . . . . . . . . . 5 2.2 Camera Model and Calibration Method . . . . . . . . . . . . . . . . . . 8 2.2.1 Pinhole Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.2 Zhang’s Calibration Method . . . . . . . . . . . . . . . . . . . . 9 2.3 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3 System Architecture and Problem Definition 13 3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.1.2 Specifications of Hardware Devices . . . . . . . . . . . . . . . . 14 3.1.3 Proposed Lighting Model for VCSEL Array . . . . . . . . . . . . 18 3.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.1 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.2.2 3D Surface Reconstruction . . . . . . . . . . . . . . . . . . . . . 22 4 Design and Implementation 24 4.1 3D VCSEL Image Calibration . . . . . . . . . . . . . . . . . . . . . . . 24 4.1.1 Camera Calibration . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.1.2 VCSEL Array Calibration . . . . . . . . . . . . . . . . . . . . . 26 4.2 3D Surface Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2.1 Correspondence Solving Algorithm . . . . . . . . . . . . . . . . 28 4.2.2 Complexity Reduction of the Correspondence Solving Algorithm 31 4.2.3 Model Refinement . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 5 Performance Evaluation 39 5.1 Accuracy Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 5.1.1 Estimation Generation for Gauge Block . . . . . . . . . . . . . . 42 5.1.2 Height Difference Estimation . . . . . . . . . . . . . . . . . . . 43 5.1.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 5.2 Socket Inspection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2.1 Formal Definition . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.2.2 Algorithm Overview . . . . . . . . . . . . . . . . . . . . . . . . 46 5.2.3 The Low-angle Light Method . . . . . . . . . . . . . . . . . . . 47 5.2.4 Estimation Generation for Socket . . . . . . . . . . . . . . . . . 48 5.2.5 Merging Initial Guess . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6 Conclusion 59 Bibliography 60
dc.language.isoen
dc.subject次毫米zh_TW
dc.subjectVCSEL陣列zh_TW
dc.subject三維感測zh_TW
dc.subject結構光zh_TW
dc.subject三維重建zh_TW
dc.subject3D reconstructionen
dc.subject3D sensingen
dc.subjectsubmillimeter accuracyen
dc.subjectVCSEL arrayen
dc.subjectstructured lighten
dc.title以微型陣列光源進行次毫米級三維量測之視覺演算法設計與實作zh_TW
dc.titleThe Design and Implementation of Submillimeter 3-dimensional Inspection Framework of LGA Socket Using VCSEL Arrayen
dc.date.schoolyear109-2
dc.description.degree碩士
dc.contributor.oralexamcommittee黃建璋(Hsin-Tsai Liu),蔡欣穆(Chih-Yang Tseng),陳炳宇,簡韶逸
dc.subject.keyword三維感測,次毫米,VCSEL陣列,結構光,三維重建,zh_TW
dc.subject.keyword3D sensing,submillimeter accuracy,VCSEL array,structured light,3D reconstruction,en
dc.relation.page62
dc.identifier.doi10.6342/NTU202101713
dc.rights.note同意授權(全球公開)
dc.date.accepted2021-10-27
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊工程學研究所zh_TW
dc.date.embargo-lift2025-01-01-
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