具有空間三維幾何量測之八進位井字編碼結構光的設計

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姓名

周錫?(Hsi-Min Chou) 查詢紙本館藏

畢業系所

光電科學與工程學系

論文名稱

具有空間三維幾何量測之八進位井字編碼結構光的設計
(Design of Structure Light with Octal Coded Hashtag Patterns for Spatial 3D Geometric Measurement)

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摘要(中)

本實驗設計的空間編碼結構光，藉由偽隨機序列、絕對編碼方式，達到87的編碼數量，在1.6m橫向可解析最小距離為6mm，到3m橫向可解析最小距離約為1cm左右；在1.6m深度誤差為2mm，到3m深度誤差約為6mm左右。並將本實際的編碼圖樣模擬成繞射光學元件並投影在3m，針對投影光進行SNR、Contrast、MBR的可行性分析，在繞射光學元件尺寸為0.90x0.70mm，相位區分為四階，波長λ=840nm，投影距離d=3m，線寬L_width=225nm的情況下，SNR約為37，Contrast約為5，MBR約為27且該DOE遞迴傅立葉模擬達到理論極限MBR之93.48%。最後還與市面上Kinect v1作比較分析，從系統上來看，在3m下v1掃描之平面深度誤差厚度為25cm，本實驗架構為1.5cm。另外，發現v1資訊點密度為在2.75m時，共約有430個點資訊/m2，而本實驗的架構在2.75m時，約有3215個點資訊/m2；從編碼圖樣設計來說，在相同解析度和DOE尺寸的條件下，Kinect v1有較好的表現在DOE製成模擬，而透過低通模糊成像結果，觀察得到本實驗的編碼對於影像模糊有更好的容忍度。

摘要(英)

In this study, we design a coded structured light pattern whose coding number is 87 by spatial neighborhood, absolute coding strategy and pseudorandom sequences.
First, lateral resolution of our system at 1.6m and 3m is 6mm and 1cm respectively, and deep Resolution at 1.6m and 3m is 2mm and 6mm respectively.
Then, we use this coding pattern performing diffractive optical element simulation projecting at 3m, and do the feasibility analysis of SNR, contrast and MBR. The SNR is 37, contrast is 5, and MBR is 27 which is 93.48% of MBR limitation under conditions that diffractive optical element size is 0.90x0.70mm, lambda is 840nm, projecting distance is 3m, line width is 225nm and divide phase into four orders.
Also, we compare our system with the system of Kinect v1, and have a result that the error at 3m of v1 is 25cm, while only 1.5cm in our system. By the way, the density of signal points of v1 and our system at 2.75m is 430 and 3215 points/m2 respectively. Last but not least, we compare our patterns with the patterns of Kinect v1, the patterns of Kinect v1 have higher contrast and MBR in diffractive optical element simulation, but our patterns have better tolerance for blurred image under the same signal points numbers and camera resolution.

關鍵字(中)

★ 空間編碼
★ 偽隨機序列
★ 絕對編碼
★ 繞射光學元件

關鍵字(英)

★ Spatial Neighborhood
★ Pseudorandom Sequences
★ Absolute Coding Strategy
★ Diffractive Optical Element

論文目次

中文摘要 V
Abstract VI
致謝 VIII
目錄 IX
圖目錄 XIII
表目錄 XVII
第一章緒論 1
1-1 研究背景與動機 1
1-2 相關研究與回顧 2
1-3 論文架構說明 4
第二章基礎原理 6
2-1 三角測距 6
2-2 飛時測距 12
2-3 雙眼視覺 16
2-4 相機校正 17
2-5 質心法 18
2-6 最近鄰居演算法 19
2-7 統計異常值去除 19
2-8 移動最小平方法 20
2-9 繞射光學元件之遞迴傅立葉模擬 20
第三章編碼結構光 22
3-1 編碼設計策略 22
3-1-1 編碼形式 22
3-1-2 編碼序列方式 25
3-1-3 編碼重複形式 29
3-2 編碼圖樣設計 31
3-2-1 編碼模式 31
3-2-2 編碼圖樣分割與解碼 34
3-2-3 運算次數及時間複雜度 37
3-3 實驗結果 40
3-3-1 誤碼率 40
3-3-2 橫向解析度 41
3-3-3 深度解析度及標準差 44
3-3-4 平面及曲面場域 48
3-3-5 模擬成像品質對辨析率的分析 51
3-3-6 討論 54
第四章繞射光學元件設計之可行性評估 55
4-1 評估方式 55
4-1-1 信號雜訊比 55
4-1-2 對比度 56
4-1-3 平均信號背景能量比 57
4-2 相位區分階數 58
4-3 繞射光學元件尺寸 62
第五章與Kinect v1的比較 68
5-1 編碼圖樣的比較 68
5-1-1 編碼圖樣設計 68
5-1-2 DOE製成模擬評估 70
5-1-3 針對低通模糊影像的容忍度 73
5-2 投影圖樣失真探討 75
5-3 系統比較 76
第六章結論 79
參考文獻 81
中英文名詞對照表 86

參考文獻

1. P. Agrawal, R. Girshick, and J. Malik, "Analyzing the performance of multilayer neural networks for object recognition," in European conference on computer vision, pp. 329-344(Springer2014).
2. Y. Zhang, M. Bai, P. Kohli, S. Izadi, and J. Xiao, "Deepcontext: Context-encoding neural pathways for 3d holistic scene understanding," arXiv preprint arXiv:1603.04922 (2016).
3. L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs," IEEE transactions on pattern analysis and machine intelligence 40, 834-848 (2018).
4. S. Xie, and Z. Tu, "Holistically-nested edge detection," in Proceedings of the IEEE international conference on computer vision, pp. 1395-1403(2015).
5. B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, "Learning deep features for scene recognition using places database," in Advances in neural information processing systems, pp. 487-495(2014).
6. A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. Funkhouser, "Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning," arXiv preprint arXiv:1803.09956 (2018).
7. S. Borhade, M. Shah, P. Jadhav, D. Rajurkar, and A. Bhor, "Advanced driver assistance system," in Sensing Technology (ICST), 2012 Sixth International Conference on, pp. 718-722(IEEE2012).
8. D. Geronimo, A. M. Lopez, A. D. Sappa, and T. Graf, "Survey of pedestrian detection for advanced driver assistance systems," IEEE transactions on pattern analysis and machine intelligence 32, 1239-1258 (2010).
9. L. Hong, Y. Wan, and A. Jain, "Fingerprint image enhancement: Algorithm and performance evaluation," IEEE transactions on pattern analysis and machine intelligence 20, 777-789 (1998).
10. M. A. Turk, and A. P. Pentland, "Face recognition using eigenfaces," in Computer Vision and Pattern Recognition, 1991. Proceedings CVPR′91., IEEE Computer Society Conference, pp. 586-591(IEEE1991).
11. C.-H. Chen, and K.-T. Song, "Complete coverage motion control of a cleaning robot using infrared sensors," in Mechatronics, 2005. ICM′05. IEEE International Conference, pp. 543-548(IEEE2005).
12. Z. Feng-Ji, G. Hai-Jiao, and K. Abe, "A mobile robot localization using ultrasonic sensors in indoor environment," in Robot and Human Communication, 1997. RO-MAN′97. Proceedings., 6th IEEE International Workshop on, pp. 52-57(IEEE1997).
13. P. Misra, and P. Enge, "Global Positioning System: signals, measurements and performance second edition," Massachusetts: Ganga-Jamuna Press (2006).
14. 邱俐雯, "人臉三維取像與辨識; Study of 3D Human Face Imaging and Identification," (國立中央大學, 2016).
15. 林坤政, "線性雷射掃描與結構光投影掃描於室內空間點雲建立之研究," (國立中央大學, 2016).
16. F. Gueuning, M. Varlan, C. Eugene, and P. Dupuis, "Accurate distance measurement by an autonomous ultrasonic system combining time-of-flight and phase-shift methods," in Instrumentation and Measurement Technology Conference, 1996. IMTC-96. Conference Proceedings. Quality Measurements: The Indispensable Bridge between Theory and Reality., IEEE, pp. 399-404(IEEE1996).
17. D. T. Delpy, M. Cope, P. van der Zee, S. Arridge, S. Wray, and J. Wyatt, "Estimation of optical pathlength through tissue from direct time of flight measurement," Physics in Medicine & Biology 33, 1433 (1988).
18. A. Kilpela, R. Pennala, and J. Kostamovaara, "Precise pulsed time-of-flight laser range finder for industrial distance measurements," Review of Scientific Instruments 72, 2197-2202 (2001).
19. 戴觀祖, "3D ToF 三維場景距離測量系統簡介," (2016).
20. M. Reynolds, J. Dobo?, L. Peel, T. Weyrich, and G. J. Brostow, "Capturing time-of-flight data with confidence," in Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pp. 945-952(IEEE2011).
21. D. Freedman, Y. Smolin, E. Krupka, I. Leichter, and M. Schmidt, "SRA: Fast removal of general multipath for ToF sensors," in European Conference on Computer Vision, pp. 234-249(Springer2014).
22. O. Wasenmuller, and D. Stricker, "Comparison of kinect v1 and v2 depth images in terms of accuracy and precision," in Asian Conference on Computer Vision, pp. 34-45(Springer2016).
23. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision 60, 91-110 (2004).
24. Z. Zhang, "A flexible new technique for camera calibration," IEEE Transactions on pattern analysis and machine intelligence 22 (2000).
25. J. Heikkila, and O. Silven, "A four-step camera calibration procedure with implicit image correction," in Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference, pp. 1106-1112(IEEE1997).
26. C. B. Duane, "Close-range camera calibration," Photogramm. Eng 37, 855-866 (1971).
27. B. F. Alexander, and K. C. Ng, "Elimination of systematic error in subpixel accuracy centroid estimation [also Letter 34 (11) 3347-3348 (Nov1995)]," Optical Engineering 30, 1320-1332 (1991).
28. 吳季樺, "光學質心法應用於光電量測系統之研究; The application of centroid method on electro-optical measurement systems," (國立中央大學圖書館, 2005).
29. K. Fukunaga, and P. M. Narendra, "A branch and bound algorithm for computing k-nearest neighbors," IEEE transactions on computers 100, 750-753 (1975).
30. M. Muja, and D. G. Lowe, "Fast approximate nearest neighbors with automatic algorithm configuration," VISAPP (1) 2, 2 (2009).
31. S. A. Dudani, "The distance-weighted k-nearest-neighbor rule," IEEE Transactions on Systems, Man, and Cybernetics, 325-327 (1976).
32. Z. Zhang, "Iterative point matching for registration of free-form curves and surfaces," International journal of computer vision 13, 119-152 (1994).
33. S. D. Blostein, and T. S. Huang, "Error analysis in stereo determination of 3-D point positions," IEEE Transactions on Pattern Analysis and Machine Intelligence, 752-765 (1987).
34. R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz, "Towards 3D point cloud based object maps for household environments," Robotics and Autonomous Systems 56, 927-941 (2008).
35. M. Alexa, J. Behr, D. Cohen-Or, S. Fleishman, D. Levin, and C. T. Silva, "Computing and rendering point set surfaces," IEEE Transactions on visualization and computer graphics 9, 3-15 (2003).
36. R. B. Rusu, N. Blodow, Z. Marton, A. Soos, and M. Beetz, "Towards 3D object maps for autonomous household robots," in Intelligent Robots and Systems, 2007.
37. K. S. Arun, T. S. Huang, and S. D. Blostein, "Least-squares fitting of two 3-D point sets," IEEE Transactions on Pattern Analysis & Machine Intelligence, 698-700 (1987).
38. H. P. Herzig, Micro-optics: elements, systems and applications (CRC Press, 2014).
39. T. Hessler, M. Rossi, R. E. Kunz, and M. T. Gale, "Analysis and optimization of fabrication of continuous-relief diffractive optical elements," Applied optics 37, 4069-4079 (1998).
40. J. Salvi, J. Pages, and J. Batlle, "Pattern codification strategies in structured light systems," Pattern recognition 37, 827-849 (2004).
41. D. Kim, M. Ryu, and S. Lee, "Antipodal gray codes for structured light," in Robotics and Automation, 2008. ICRA 2008. IEEE International Conference, pp. 3016-3021(IEEE2008).
42. R. Furukawa, and H. Kawasaki, "Uncalibrated multiple image stereo system with arbitrarily movable camera and projector for wide range scanning," in null, pp. 302-309(IEEE2005).
43. D. Scharstein, and R. Szeliski, "High-accuracy stereo depth maps using structured light," in Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference, pp. I-I(IEEE2003).
44. C. Albitar, P. Graebling, and C. Doignon, "Design of a monochromatic pattern for a robust structured light coding," in Image Processing, 2007. ICIP 2007. IEEE International Conference, pp. VI-529-VI-532(IEEE2007).
45. N. G. Durdle, J. Thayyoor, and V. Raso, "An improved structured light technique for surface reconstruction of the human trunk," in Electrical and Computer Engineering, 1998. IEEE Canadian Conference, pp. 874-877(IEEE1998).
46. M. Ito, and A. Ishii, "A three-level checkerboard pattern (TCP) projection method for curved surface measurement," Pattern Recognition 28, 27-40 (1995).
47. H. Fredricksen, "The lexicographically least de Bruijn cycle," Journal of Combinatorial Theory 9, 1-5 (1970).
48. L. Zhang, B. Curless, and S. M. Seitz, "Rapid shape acquisition using color structured light and multi-pass dynamic programming," in 3D Data Processing Visualization and Transmission, 2002. Proceedings. First International Symposium, pp. 24-36(IEEE2002).
49. F. J. MacWilliams, and N. J. Sloane, "Pseudo-random sequences and arrays," Proceedings of the IEEE 64, 1715-1729 (1976).
50. T. Etzion, "Constructions for perfect maps and pseudorandom arrays," IEEE Transactions on information theory 34, 1308-1316 (1988).
51. N. Otsu, "A threshold selection method from gray-level histograms," IEEE transactions on systems, man, and cybernetics 9, 62-66 (1979).
52. M. H. J. Vala, and A. Baxi, "A review on Otsu image segmentation algorithm," International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) 2, pp: 387-389 (2013).
53. P.-S. Liao, T.-S. Chen, and P.-C. Chung, "A fast algorithm for multilevel thresholding," J. Inf. Sci. Eng. 17, 713-727 (2001).
54. S. W. Smith, "The scientist and engineer′s guide to digital signal processing," (1997).
55. R. Tyson, Principles of adaptive optics (CRC press, 2010).
56. I. M. Vellekoop, "Controlling the propagation of light in disordered scattering media," arXiv preprint arXiv:0807.1087 (2008).

指導教授

鍾德元(Te-Yuan Chung)

審核日期

2018-8-20

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