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標題: 結構光形貌量測系統校正與多視角形貌註冊
Fringe Projection Profilometry System Calibration and Multiview Range Image Registration
作者: 蘇鑫元
Sue, Shin-Yuan
關鍵字: Phase shifting
Gray code
Gamma correction
出版社: 機械工程學系所
引用: 1.李勇民,條紋投射法於微型曲面量測之研究,國立中興大學機械工程研究所碩士學位論文,台中,2004。 2.李建緯,格雷氏編碼與相位移在三維曲面量測之應用,國立中興大學機械工程研究所碩士學位論文,台中,2004。 3. 高玉和,微型元件多視角三維形貌疊合之研究,國立中興大學機械工程研究所碩士學位論文,台中,2005。 4.P. J. Besl and N. D. McKay, "A method for registration of 3-D shapes," IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 239–256, 1992. 5.G. Sansoni, S. Corini, S. Lazzari, R. Rodella, and F. Docchio, " Three-dimensional imaging based on Gray-code light projection: characterization of the measuring algorithm and development of a measuring system for industrial applications," Applied Optics, Vol. 36, No. 19, pp. 4463-4472, 1997. 6.G. Sansoni, M. Carocci, and R. Rodella, "Three-dimensional vision based on a combination of gray-code and phase-shift light projection: analysis and compensation of the systematic errors, " Applied Optics, Vol. 38, No. 31, pp. 6565-6573, 1999. 7.C. Quan, C. J. Tay, X. Y. He, X. Kang, and H. M. Shang, "Microscopic surface contouring by fringe projection method," Optics and Laser Technology, Vol. 34, pp. 547-552, 2002. 8.M. Chang and K. H. Lin, "Non-contact scanning measurement utilizing a space mapping method," Opt. Laser Eng., No. 30, pp. 503-512, 1999. 9.A. Goshtasby, " Three-dimensional model construction from multiview range images: survey with new results," Pattern Recog. Vol. 31, No. 11, pp. 1705-1714, 1998. 10.Y. Chen and G. Medioni, "Object Modelling by Registration of Multiple Range Images," Image and Vision Computing, Vol. 10, No. 3, pp. 145-155, 1992. 11.J. P. Thirion, "Extremal points: definition and application to 3D image registration," CVPR94, pp. 587-592, 1994. 12.A. E. Johnson and S. B. Kang ,"Registration and Integration of Textured 3D Data," Proc.Int''l Conf. on Recent Advances in 3D Digital Imaging and Modeling, IEEE Computer Society Press, Los Alamitos, Calif., pp. 234-241, 1997. 13.T. Jost, "fast geometric matching for shape registration," PhD thesis, University of Neuchâtel, 2002. 14. C. Schütz, "Geometric Point Matching of free-form 3D objects," PhD thesis,Université de Neuchâtel, 1998. 15.C. Dorai, G. Wang, A. K. Jain, and C. Mercer," Registration and integration of multiple object views for 3D model construction," IEEE Trans. Pat. Anal. and Mach. Intel., Vol. 20, No. 1, pp. 83-89, 1998. 16.G. Turk and M. Levoy, "Zippered Polygon Meshes from Range Images," Proc. ACM SIGGRAPH 94, pp. 311-318, 1994. 17.Yonghuai Liu, Baogang Wei, "Developing structural constraints for accurate registration of overlapping range images, "Robotics and Autonomous System, Vol. 47,pp.11-30,2004. 18.J. H. Friedman, J. L. Bentley, and R. A. Finkel, " An Algorithm for Finding Best Matches in Logarithmic Expected Time," ACM Trans. Math. Softw, Vol. 3, No. 3, pp. 209-226, 1977. 19.Z. Zhang, "Iterative point matching for registration of free-form curves and surfaces," International Journal of Computer Vision, Vol. 13, pp.119-152, 1994. 20.T. Jost, and H. Hügli, "Fast ICP Algorithms for Shape Registration," DAGM-Symposium, pp. 91-99, 2002. 21.D. Chetverikov , D. Svirko , D. Stepanov and P. Krsek , "The Trimmed Iterative Closest Point Algorithm," Proc. IEEE Int''l Conf. Pattern Recognition, pp.545-548, 2002. 22.D. Chetverikov, D. Stepanov, and P. Krsek, "Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm," Image Vision Comput, Vol. 23, No. 3, pp. 299-309, 2005. 23.N. Li, "Accurate integration of surface profile data with quantitative error analysis," Experimental Mechanics, Vol. 41, No. 1, pp. 77-83, 2001. 24.D. Akca, " A New Algorithm for 3D Surface Matching," Int. Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences(ISPRS) , pp. 960-965, 2004. 25.A. J. Stoddart, and A. Hilton, "Registration of multiple point sets," Proc. 13th Int. Conf. on Pat. Recog., Vol II, pp. 40-44, 1996. 26.John Williams ,Mohammed Bennamoun , "Simultaneous Registration of Multiple Corresponding Point Set," Computer vision and Image Understanding, Vol. 81, pp.117-142, 2001. 27. R. Y. Tsai, "A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology using of-the-shelf TV cameras and Lenses," IEEE. J. of Robotics and Automation,RA-3(4),pp.323-344,1987. 28. Zhengyou Zhang, "A flexible New Technique for Camera Calibration," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, pp.1330-1333 , 2000. 29.J. Moré, B. Garbow and K. Hillstrom,"MINPACK," Argonne National Laboratory, 30. Open Source Computer Vision Library , OpenCV,
摘要: 在傳統上使用三次元量床做輪廓量測,其機器成本較高,且耗時。隨著電腦的發展,使用電腦視覺方式作為量測工具已非常方便且成本低廉。三維模型重建需要很大的計算量,快速又可靠的來重建三維模型是可行的。本研究目的就在於建立一套經濟的三維模型重建系統,利用本系統可輕易的重建三維模型。 本研究主要是利用液晶投影機(LCD),製造條紋結構光並投射至物體表面,並利用三角量測法取得輪廓的形貌資訊。針對系統中可能造成量測誤差的部份,包括條紋投射與攝影機取像間亮度之非線性問題,及鏡頭畸變影響進行校正。但是一般顯示設備如LCD投影機,皆有做Gamma曲線的調校來適合人類的視覺,但這會造成亮度的非線性,影響相位計算的準確,需要透過校正來改善亮度的非線性問題。 利用此量測系統,搭配相位移及格雷式編碼計算絕對相位,再用映射函數校正,便可以得到單一視角的輪廓形貌資訊。接著執行形貌註冊演算法將各視角間的轉換關係計算出來,再使用最短路徑演算法得到每個視角整合至基準視角的路徑,根據路徑就可以把所有視角整合成單一座標系,最後透過形貌整合技術就可以得到完整的三維模型。
The CMM is used for surface profilometry traditionally, but it is not only expensive but also time consuming. With the computer technique promotes, the measurement uses the computer vision became convenient and inexpensive. The purpose of this paper is to establish a three-dimensional contours rebuilding system, which is economical and simple. This paper describes the usage the liquid crystal display (LCD) which projects the fringe projection onto the object's surface. Then the triangulation principle is used to get the surface profile. For improving the accuracy of the system, we have to modify the system error, which results from lens distortion and the nonlinearity of the VGA display card, LCD projector and image grab. Generally speaking, there is always a Gamma correction for display equipment to accommodate the human's visual system, LCD for example. This correction makes brightness nonlinear problem and influences the phase accuracy, so we need calibration to improve the brightness nonlinear problem. The measurement system obtains the single view profile information by means of the Gray-code method and phase-shifting technique to calculate the absolute phase, and the mapping relationship between the measured points and image pixel. Then we execute the range image registration and shortest path algorithm for all views. According to the shortest path and the transformation of each view, we can acquire the initial model. Finally, we execute the simultaneous regulating produce to obtain the whole contours of component.
其他識別: U0005-2807200611013100
Appears in Collections:機械工程學系所



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