高分辨率三维测量系统
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摘要
本文首先介绍了三维测量系统在航天航空、医疗、娱乐、机器人产业的一些基本应用,然后介绍了目前国内外三维测量的方法。在这些方法中,结构光技术因为它的高速度、高分辨率的特点得到了广泛的应用。本文基于一个新的结构光技术--双目视觉和相移技术相结合的三维测量方法,建立了一个高分辨率的三维测量硬件系统,同时在原有方法的基础上,采用亚像素匹配的方法,提高了系统的分辨率。本文分析了系统分辨率的影响因素,并提出了多次采样的方法,减少系统的随机噪声。分析了本三维测量系统的精度,本文提出了系统误差补偿的方法,并通过实验证明了该方法的有效性。
本文介绍了双目视觉和相移技术相结合的三维测量方法的基本原理和优势。双目视觉和相移技术相结合的三维测量方法使用了两个相机和一个投影仪,首先左右相机分别拍摄投影条纹后的物体图片,利用相移算法得到左右图片的相位图,然后利用相位图匹配得到左右照片的对应点对,最后利用三角算法得到空间点的三维坐标。这个方法只将左右图片的相位图作为双目视觉中点云匹配的依据,可以避免因相位计算的不准确而直接造成的系统精度影响,从而可以避免投影仪标定的繁琐工序。同时,本文还介绍了结构光系统的非线性标定的原理和方法。
本文介绍了高分辨率三维测量系统的总体硬件框架,介绍了相机和投影仪的特点。本系统使用了高分辨率的相机和LED投影仪,投影仪和相机被固定在一个铝制的支架上,系统体积小巧,可作为便携式仪器使用。本文介绍了相机和投影仪的控制,以及软件同步相机和投影仪的方法。
本研究在原有算法的基础上加以改进,提出了亚像素级的双目匹配方法。原双目视觉和相移技术相结合的三维重构方法是利用左右图片的横纵相位图来匹配左右图片的像素点,但是由于图片上的像素点是有限离散的,对于右图上的像素点A,我们很难在左图上找到和它的横纵相位值完全相同的像素点,这会导致在双目匹配的过程中存在像素级别的误差。为了解决这个问题,我们采用亚像素级的双目匹配方法,用插值的方法在左图上找到和右图中像素点的横纵相位值完全相同的点,实现了亚像素级的双目匹配,减少了系统误差。研究中,我们采用了两种不同的插值方法。一种方法是两维线性插值法,两维线性插值具有简单易用的特点,但是它的缺点是插值结果一阶导数不连续。另一种方法是两维三次Hermite插值法,三次Hermite插值的优势在于它实现了插值结果一阶导数连续,使结果更加光滑。实验结果显示:亚像素级别的双目匹配算法取得了非常好的效果,显著的提高了系统的分辨率。
本研究采用了多次采样的方法来减少系统的随机误差。通过平均多次采样的结果,我们可以有效减少系统的随机误差。通过对实验结果的分析,本研究合理地选择了采样的组数。
本文介绍了系统精度和分辨率的评价方法和结果。提出了一种误差补偿的方法来减小系统误差。利用已知点的误差值进行三维线性插值估算得到重构点的三维测量误差值,重构点减去三维测量误差得到误差补偿后的三维坐标。实验结果验证了此方法的可行性和有效性。最后,本文分析了系统的精度和分辨率的影响因素。
In this thesis, we first introduce the basic applications of three-dimensional measurement systems in aeronautics, medical systems, entertainment, and robotics. Then, it introduces various methods used for three-dimensional shape measurement. Among those techniques, the structured light technique is widely used due to its high-speed and high-resolution capabilities. This research is based on a new structured light method—combined stereovision and phase shifting method. In this research, a new three-dimensional measurement system is set up with two high-resolution cameras and one LED projector. A sub-pixel stereo matching method is proposed to improve the system resolution. Factors that may influence system resolution are analyzed. A method is proposed to reduce the random noise in the system by averaging multiple sets of samples. After the system accuracy and resolution are evaluated, an error compensation method is also proposed to reduce system error. Experimental results are shown to demonstrate the method’s effectiveness.
The principle of the combined stereovision and phase shifting method is introduced along with its unique advantages as compared with conventional methods. The combined stereovision and phase shifting method uses two cameras and one projector. This method first calculates the horizontal and vertical phase maps based on the fringe images from the two cameras. It then uses these phase maps for stereo matching at the pixel level. The reconstruction of 3-D models is similar as that in a typical stereovision system. Since this method uses the phase values only in the process of stereo matching, phase measurement errors do not influence measurement accuracy directly. Therefore, this method can significantly reduce errors caused by phase measurement inaccuracy. In the mean time, it makes projector calibration unnecessary. The principle of nonlinear calibration of the measurement system is also introduced.
The hardware system of this high-resolution three-dimensional measurement system is introduced, including the characteristics of the cameras and projector used in the system. The system uses two high-resolution cameras and one LED projector. Two cameras and one projector are fixed on a metal frame and the whole system is very compact. The control of the cameras and projector and the method to synchronize the cameras and projector are also discussed.
We modify the original combined stereovision and phase shifting method and propose a sub-pixel stereo matching method. With both the left and right absolute phase maps available, pixels in the right image can be matched to pixels in the left image based on the vertical and horizontal phase values. However, because the pixels in the image are discrete and limited, it is almost impossible to find two pixels with exactly the same horizontal and vertical phase values. This leads to some digital errors at the pixel level. In order to solve this problem, a sub-pixel stereo matching method is proposed. We use a two-dimensional interpolation method to interpolate the pixels in the left image to find a point where the horizontal and vertical phase values are exactly the same as those at a pixel in the right image. This method can match the left and right images at the sub-pixel level, which greatly reduces the system error. In the research, we adopt two different interpolation methods: two-dimensional linear interpolation method and two-dimensional cubic Hermite interpolation method. Two-dimensional linear interpolation is simple and easy to use, but its first derivative is not continuous, which makes the result not smooth. Two- dimensional cubic Hermite interpolation method realizes the continuity of the first derivative and makes the result smooth. The experimental results show that the sub-pixel stereo matching method effectively enhances the system resolution.
We also propose to reduce the system random noise by averaging multiple sets of images. By analyzing the experimental results, we choose the proper number of image sets that we should collect.
We discuss about the methods to enhance system accuracy and resolution and provide the experimental results. We propose an error compensation method to reduce the system error. We construct an error map and do three-dimensional linear interpolation to estimate the errors at the measured points. Subtracting the estimated errors from the measured coordinates of these points, we obtain their three-dimensional coordinates with better accuracy. Experimental results show that this method effectively reduces the system error. Finally factors which may influence the system resolution and accuracy are analyzed.
本文介绍了双目视觉和相移技术相结合的三维测量方法的基本原理和优势。双目视觉和相移技术相结合的三维测量方法使用了两个相机和一个投影仪,首先左右相机分别拍摄投影条纹后的物体图片,利用相移算法得到左右图片的相位图,然后利用相位图匹配得到左右照片的对应点对,最后利用三角算法得到空间点的三维坐标。这个方法只将左右图片的相位图作为双目视觉中点云匹配的依据,可以避免因相位计算的不准确而直接造成的系统精度影响,从而可以避免投影仪标定的繁琐工序。同时,本文还介绍了结构光系统的非线性标定的原理和方法。
本文介绍了高分辨率三维测量系统的总体硬件框架,介绍了相机和投影仪的特点。本系统使用了高分辨率的相机和LED投影仪,投影仪和相机被固定在一个铝制的支架上,系统体积小巧,可作为便携式仪器使用。本文介绍了相机和投影仪的控制,以及软件同步相机和投影仪的方法。
本研究在原有算法的基础上加以改进,提出了亚像素级的双目匹配方法。原双目视觉和相移技术相结合的三维重构方法是利用左右图片的横纵相位图来匹配左右图片的像素点,但是由于图片上的像素点是有限离散的,对于右图上的像素点A,我们很难在左图上找到和它的横纵相位值完全相同的像素点,这会导致在双目匹配的过程中存在像素级别的误差。为了解决这个问题,我们采用亚像素级的双目匹配方法,用插值的方法在左图上找到和右图中像素点的横纵相位值完全相同的点,实现了亚像素级的双目匹配,减少了系统误差。研究中,我们采用了两种不同的插值方法。一种方法是两维线性插值法,两维线性插值具有简单易用的特点,但是它的缺点是插值结果一阶导数不连续。另一种方法是两维三次Hermite插值法,三次Hermite插值的优势在于它实现了插值结果一阶导数连续,使结果更加光滑。实验结果显示:亚像素级别的双目匹配算法取得了非常好的效果,显著的提高了系统的分辨率。
本研究采用了多次采样的方法来减少系统的随机误差。通过平均多次采样的结果,我们可以有效减少系统的随机误差。通过对实验结果的分析,本研究合理地选择了采样的组数。
本文介绍了系统精度和分辨率的评价方法和结果。提出了一种误差补偿的方法来减小系统误差。利用已知点的误差值进行三维线性插值估算得到重构点的三维测量误差值,重构点减去三维测量误差得到误差补偿后的三维坐标。实验结果验证了此方法的可行性和有效性。最后,本文分析了系统的精度和分辨率的影响因素。
In this thesis, we first introduce the basic applications of three-dimensional measurement systems in aeronautics, medical systems, entertainment, and robotics. Then, it introduces various methods used for three-dimensional shape measurement. Among those techniques, the structured light technique is widely used due to its high-speed and high-resolution capabilities. This research is based on a new structured light method—combined stereovision and phase shifting method. In this research, a new three-dimensional measurement system is set up with two high-resolution cameras and one LED projector. A sub-pixel stereo matching method is proposed to improve the system resolution. Factors that may influence system resolution are analyzed. A method is proposed to reduce the random noise in the system by averaging multiple sets of samples. After the system accuracy and resolution are evaluated, an error compensation method is also proposed to reduce system error. Experimental results are shown to demonstrate the method’s effectiveness.
The principle of the combined stereovision and phase shifting method is introduced along with its unique advantages as compared with conventional methods. The combined stereovision and phase shifting method uses two cameras and one projector. This method first calculates the horizontal and vertical phase maps based on the fringe images from the two cameras. It then uses these phase maps for stereo matching at the pixel level. The reconstruction of 3-D models is similar as that in a typical stereovision system. Since this method uses the phase values only in the process of stereo matching, phase measurement errors do not influence measurement accuracy directly. Therefore, this method can significantly reduce errors caused by phase measurement inaccuracy. In the mean time, it makes projector calibration unnecessary. The principle of nonlinear calibration of the measurement system is also introduced.
The hardware system of this high-resolution three-dimensional measurement system is introduced, including the characteristics of the cameras and projector used in the system. The system uses two high-resolution cameras and one LED projector. Two cameras and one projector are fixed on a metal frame and the whole system is very compact. The control of the cameras and projector and the method to synchronize the cameras and projector are also discussed.
We modify the original combined stereovision and phase shifting method and propose a sub-pixel stereo matching method. With both the left and right absolute phase maps available, pixels in the right image can be matched to pixels in the left image based on the vertical and horizontal phase values. However, because the pixels in the image are discrete and limited, it is almost impossible to find two pixels with exactly the same horizontal and vertical phase values. This leads to some digital errors at the pixel level. In order to solve this problem, a sub-pixel stereo matching method is proposed. We use a two-dimensional interpolation method to interpolate the pixels in the left image to find a point where the horizontal and vertical phase values are exactly the same as those at a pixel in the right image. This method can match the left and right images at the sub-pixel level, which greatly reduces the system error. In the research, we adopt two different interpolation methods: two-dimensional linear interpolation method and two-dimensional cubic Hermite interpolation method. Two-dimensional linear interpolation is simple and easy to use, but its first derivative is not continuous, which makes the result not smooth. Two- dimensional cubic Hermite interpolation method realizes the continuity of the first derivative and makes the result smooth. The experimental results show that the sub-pixel stereo matching method effectively enhances the system resolution.
We also propose to reduce the system random noise by averaging multiple sets of images. By analyzing the experimental results, we choose the proper number of image sets that we should collect.
We discuss about the methods to enhance system accuracy and resolution and provide the experimental results. We propose an error compensation method to reduce the system error. We construct an error map and do three-dimensional linear interpolation to estimate the errors at the measured points. Subtracting the estimated errors from the measured coordinates of these points, we obtain their three-dimensional coordinates with better accuracy. Experimental results show that this method effectively reduces the system error. Finally factors which may influence the system resolution and accuracy are analyzed.
引文
[1]邵双运,“光学三维测量技术与应用”,现代仪器, 2008,(3):10~13.
[2] G. Vosselman and P. Kessels,―The utilization of airborne laser scanning for mapping‖,International Journal of Applied Each Observation and Geoinformation, 2005, (6):177-186.
[3] E. Naesset and R. Nelson,―Using airborne laser scanning to monitor tree migration in the boreal——alpine transition zone‖,Remote Sensing of Environment, 2007, 110:357-369.
[4] Z. Zhang, H. Guo, G. Nejat and P. Huang,―Finding Disaster Victims: A Sensory System for Robot-Assisted 3D Mapping of Urban Search and Rescue Environments‖, 2007 IEEE International Conference on Robotics and Automation, 2007, (10-14): 3889– 3894.
[5] Y. C. Shan,―A new technique to extract range in-formation from stereo images‖, IEEE Trans on Pattern Analysis and Machine Intelligence, 1989, 11(7):768-773.
[6]黄锦,张晓兵,尹涵春,安新伟,“基于图像处理的三维测量方法”,电子器件,2002,25(4).
[7]吴立德计算机视觉,上海:复旦大学出版社,1993.
[8] T. Kanade ,―A stereo matching algorithm with an adaptive window: Theory and Experiment‖. IEEE Trans on Pattern Analysis and Machine Intelligence, 1994, 16(9):920-932.
[9]郭丰俊,“基于小波变换的多尺度匹配算法”,红外与激光工程,1999.
[10] J. Ens,―An investigation of methods for determining depth from focus‖, IEEE Trans on Pattern Analysis and Machine Intelligence, 1993, 15(2):97-108.
[11]郑南宁,“计算机视觉与模式识别”,北京:国防工业出版社,1998.
[12] T. Akuta,―Development of an automatic 3-D shape measuring system using a new
[1]邵双运,“光学三维测量技术与应用”,现代仪器, 2008,(3):10~13.
[2] G. Vosselman and P. Kessels,―The utilization of airborne laser scanning for mapping‖,International Journal of Applied Each Observation and Geoinformation, 2005, (6):177-186.
[3] E. Naesset and R. Nelson,―Using airborne laser scanning to monitor tree migration in the boreal——alpine transition zone‖,Remote Sensing of Environment, 2007, 110:357-369.
[4] Z. Zhang, H. Guo, G. Nejat and P. Huang,―Finding Disaster Victims: A Sensory System for Robot-Assisted 3D Mapping of Urban Search and Rescue Environments‖, 2007 IEEE International Conference on Robotics and Automation, 2007, (10-14): 3889– 3894.
[5] Y. C. Shan,―A new technique to extract range in-formation from stereo images‖, IEEE Trans on Pattern Analysis and Machine Intelligence, 1989, 11(7):768-773.
[6]黄锦,张晓兵,尹涵春,安新伟,“基于图像处理的三维测量方法”,电子器件,2002,25(4).
[7]吴立德计算机视觉,上海:复旦大学出版社,1993.
[8] T. Kanade ,―A stereo matching algorithm with an adaptive window: Theory and Experiment‖. IEEE Trans on Pattern Analysis and Machine Intelligence, 1994, 16(9):920-932.
[9]郭丰俊,“基于小波变换的多尺度匹配算法”,红外与激光工程,1999.
[10] J. Ens,―An investigation of methods for determining depth from focus‖, IEEE Trans on Pattern Analysis and Machine Intelligence, 1993, 15(2):97-108.
[11]郑南宁,“计算机视觉与模式识别”,北京:国防工业出版社,1998.
[12] T. Akuta,―Development of an automatic 3-D shape measuring system using a newBinary-Encoded Light Pattern‖, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(2): 148-164.
[26] J. L. Posdamer and M. D. Altschuler,―Surface Measurement by Space-encoded Projected Beam Systems‖, Computer Graphics and Image Processing, 1982, 18(1): 1-17.
[27] J. Salvi, J. Pages, and J. Batlle,―Pattern codification strategies in structured light systems‖, Pattern Recognition, 2004, 37(4): 827-849.
[28] K. G. Harding,―Color encoded moirécontouring, in Optics, Illumination, and image sensing for Machine Vision III‖, 1988, Proc. SPIE 1005:169-178.
[29] L. Zhang, B. Curless and S. M. Seitz,―Rapid shape acquisition using color structured light and multi-pass Dynamic programming‖, First International Symposium on 3D Data Processing Visualization and Transmission, 2002 , (3DPVT'02): 24.
[30] M. S. Jeong and S. W. Kim,―Color grating projection moiréwith time-integral fringe capturing for high speed 3D imaging‖, Opt. Eng., 2002, 41(8): 1912–1917.
[31] J. Pan, P. Huang and F. Chiang,―Color phase shifting technique for three-dimensional shape measurement‖, Opt. Eng., 2006, 45(1): 013602.
[32] P. Huang, Q. Hu, F. Jin, and F. Chiang,―Color-encoded digital fringe projection technique for high speed three-dimensional surface contouring‖, Opt. Eng., 1999, 38(6): 1065-1071.
[33] X. Han and P. Huang,―Combined stereovision and phase shifting method: a new approach for 3D shape measurement‖, Proceedings of SPIE, 2009, 7389: 7389-125.
[34] X. Han and P. Huang,―Combined stereovision and phase shifting method: use of a visibility-modulated fringe pattern‖, Proc. SPIE, 2009, 7389: 7389-131.
[35] X. Han, P. Huang, Z. Deng,―A portable 3D shape measurement system based on the combined stereovision and phase shifting method‖, Proceedings of SPIE, 2009,7432: 743211.
[36] X. Su, and W. Chen,―Fourier transform profilometry: a review‖, Optics and laser in Engineering, 2001, 35: 263-284.
[37] H. Guo, and P. Huang,―3D shape measurement by use of a modified Fourier transform method‖, Proc. SPIE, 2008, 7066: 70660E.
[38] B. Carrihill and R. Hummel,―Experiments with the Intensity Ratio Depth 124 Sensor‖, Computer Vision, Graphics and Image Processing, 1985, 32: 337-358.
[39] M. Sjodahl and P. Synnergren,―Measurement of shape by using projected random patterns and temporal digital speckle photography‖, Appl. Opt., 1999, 38(10): 1990–1997.
[40] P. Huang, S. Zhang and F. Chiang,―Trapezoidal phase shifting method for three-dimensional shape measurement‖, Opt. Eng., 2005, 44(12): 123601.
[41] R. Legarda-Sáenz, T. Bothe, and W. P. J?uptner,―Accurate Procedure for the Calibration of a Structured Light System‖, Opt. Eng., 2004, 43(2): 464-471.
[42] Q. Hu, P. Huang, Q. Fu and F. Chiang,―Calibration of a 3D Shape Measurement System‖, Opt. Eng., 2003, 42(2): 487-493.
[43] C. C. Slama, C. Theurer and S. W. Henriksen,―Manual of Photogrammetry‖, VA: American Society of Photogrammetry, 1980.
[44] C. S. Fraser,―Photogrammetric Camera Component Calibration: A Review of Analytical Techniques‖, Calibration and Orientation of Camera in Computer Vision, Berlin Heidelberg, 2001, 95-136.
[45] F. J. Cuevas, M. Servin, O. N. Stavroudis and R. Rodriguez-Vera,―Multilayer Neural Networks Applied to Phase and Depth Recovery from Fringe Patterns‖, Opt. Commun., 2000, 181(4): 239-259.
[46] D. C. Brown,―Close-range Camera Calibration‖, Photogrametric Engineering, 1971, 37(8): 855-866.
[47] R. Y. Tsai,―A Versatile Camera Calibration Technique for High-accuracy 3D Machine Vision Metrology using Off-the-shelf TV Camera and Lenses‖, IEEE Int. J. Robot. Automat., 1987, 3(4): 323-344.
[48] G. Wei and S. Ma,―Implicit and Explicit Camera Calibration: Theory and Experiments‖, IEEE Trans. on Pattern Analysis and Machine Intelligence, 1994, 16(5): 469-480.
[49] S. Zhang and P. Huang,―Novel method for structured light system calibration‖, Opt. Eng., 2006, 45(8): 083601.
[50] P. Huang and X. Han,―On improving the accuracy of structured light systems‖, Proc. SPIE, 2006, 6382: 63820H.
[51] D. Malacara,―Optical Shop Testing‖, NY: John Wiley and Songs, 1992.
[52] X. Han,―3D Shape Measurement Based on the Phase Shifting and Stereovision Methods‖, [PH.D. Dissertation], NY: Stony Brook University, 2010.
[53] S. Zhang,―High-resolution, Real-time 3-D Shape Measurement‖, [PH.D. Dissertation], NY: Stony Brook University, 2005.
[54] R. Y. Tsai,―A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses‖, IEEE Int. J. Robot. Automat., 1987, RA-3: 323-344.
[55] P. Huang and X. Han,―On improving the accuracy of structured light systems‖, Proc. SPIE, 2006, 6382: 63820H.
[56] J. Y. Bouguet, Camera calibration toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc.
[2] G. Vosselman and P. Kessels,―The utilization of airborne laser scanning for mapping‖,International Journal of Applied Each Observation and Geoinformation, 2005, (6):177-186.
[3] E. Naesset and R. Nelson,―Using airborne laser scanning to monitor tree migration in the boreal——alpine transition zone‖,Remote Sensing of Environment, 2007, 110:357-369.
[4] Z. Zhang, H. Guo, G. Nejat and P. Huang,―Finding Disaster Victims: A Sensory System for Robot-Assisted 3D Mapping of Urban Search and Rescue Environments‖, 2007 IEEE International Conference on Robotics and Automation, 2007, (10-14): 3889– 3894.
[5] Y. C. Shan,―A new technique to extract range in-formation from stereo images‖, IEEE Trans on Pattern Analysis and Machine Intelligence, 1989, 11(7):768-773.
[6]黄锦,张晓兵,尹涵春,安新伟,“基于图像处理的三维测量方法”,电子器件,2002,25(4).
[7]吴立德计算机视觉,上海:复旦大学出版社,1993.
[8] T. Kanade ,―A stereo matching algorithm with an adaptive window: Theory and Experiment‖. IEEE Trans on Pattern Analysis and Machine Intelligence, 1994, 16(9):920-932.
[9]郭丰俊,“基于小波变换的多尺度匹配算法”,红外与激光工程,1999.
[10] J. Ens,―An investigation of methods for determining depth from focus‖, IEEE Trans on Pattern Analysis and Machine Intelligence, 1993, 15(2):97-108.
[11]郑南宁,“计算机视觉与模式识别”,北京:国防工业出版社,1998.
[12] T. Akuta,―Development of an automatic 3-D shape measuring system using a new
[1]邵双运,“光学三维测量技术与应用”,现代仪器, 2008,(3):10~13.
[2] G. Vosselman and P. Kessels,―The utilization of airborne laser scanning for mapping‖,International Journal of Applied Each Observation and Geoinformation, 2005, (6):177-186.
[3] E. Naesset and R. Nelson,―Using airborne laser scanning to monitor tree migration in the boreal——alpine transition zone‖,Remote Sensing of Environment, 2007, 110:357-369.
[4] Z. Zhang, H. Guo, G. Nejat and P. Huang,―Finding Disaster Victims: A Sensory System for Robot-Assisted 3D Mapping of Urban Search and Rescue Environments‖, 2007 IEEE International Conference on Robotics and Automation, 2007, (10-14): 3889– 3894.
[5] Y. C. Shan,―A new technique to extract range in-formation from stereo images‖, IEEE Trans on Pattern Analysis and Machine Intelligence, 1989, 11(7):768-773.
[6]黄锦,张晓兵,尹涵春,安新伟,“基于图像处理的三维测量方法”,电子器件,2002,25(4).
[7]吴立德计算机视觉,上海:复旦大学出版社,1993.
[8] T. Kanade ,―A stereo matching algorithm with an adaptive window: Theory and Experiment‖. IEEE Trans on Pattern Analysis and Machine Intelligence, 1994, 16(9):920-932.
[9]郭丰俊,“基于小波变换的多尺度匹配算法”,红外与激光工程,1999.
[10] J. Ens,―An investigation of methods for determining depth from focus‖, IEEE Trans on Pattern Analysis and Machine Intelligence, 1993, 15(2):97-108.
[11]郑南宁,“计算机视觉与模式识别”,北京:国防工业出版社,1998.
[12] T. Akuta,―Development of an automatic 3-D shape measuring system using a newBinary-Encoded Light Pattern‖, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(2): 148-164.
[26] J. L. Posdamer and M. D. Altschuler,―Surface Measurement by Space-encoded Projected Beam Systems‖, Computer Graphics and Image Processing, 1982, 18(1): 1-17.
[27] J. Salvi, J. Pages, and J. Batlle,―Pattern codification strategies in structured light systems‖, Pattern Recognition, 2004, 37(4): 827-849.
[28] K. G. Harding,―Color encoded moirécontouring, in Optics, Illumination, and image sensing for Machine Vision III‖, 1988, Proc. SPIE 1005:169-178.
[29] L. Zhang, B. Curless and S. M. Seitz,―Rapid shape acquisition using color structured light and multi-pass Dynamic programming‖, First International Symposium on 3D Data Processing Visualization and Transmission, 2002 , (3DPVT'02): 24.
[30] M. S. Jeong and S. W. Kim,―Color grating projection moiréwith time-integral fringe capturing for high speed 3D imaging‖, Opt. Eng., 2002, 41(8): 1912–1917.
[31] J. Pan, P. Huang and F. Chiang,―Color phase shifting technique for three-dimensional shape measurement‖, Opt. Eng., 2006, 45(1): 013602.
[32] P. Huang, Q. Hu, F. Jin, and F. Chiang,―Color-encoded digital fringe projection technique for high speed three-dimensional surface contouring‖, Opt. Eng., 1999, 38(6): 1065-1071.
[33] X. Han and P. Huang,―Combined stereovision and phase shifting method: a new approach for 3D shape measurement‖, Proceedings of SPIE, 2009, 7389: 7389-125.
[34] X. Han and P. Huang,―Combined stereovision and phase shifting method: use of a visibility-modulated fringe pattern‖, Proc. SPIE, 2009, 7389: 7389-131.
[35] X. Han, P. Huang, Z. Deng,―A portable 3D shape measurement system based on the combined stereovision and phase shifting method‖, Proceedings of SPIE, 2009,7432: 743211.
[36] X. Su, and W. Chen,―Fourier transform profilometry: a review‖, Optics and laser in Engineering, 2001, 35: 263-284.
[37] H. Guo, and P. Huang,―3D shape measurement by use of a modified Fourier transform method‖, Proc. SPIE, 2008, 7066: 70660E.
[38] B. Carrihill and R. Hummel,―Experiments with the Intensity Ratio Depth 124 Sensor‖, Computer Vision, Graphics and Image Processing, 1985, 32: 337-358.
[39] M. Sjodahl and P. Synnergren,―Measurement of shape by using projected random patterns and temporal digital speckle photography‖, Appl. Opt., 1999, 38(10): 1990–1997.
[40] P. Huang, S. Zhang and F. Chiang,―Trapezoidal phase shifting method for three-dimensional shape measurement‖, Opt. Eng., 2005, 44(12): 123601.
[41] R. Legarda-Sáenz, T. Bothe, and W. P. J?uptner,―Accurate Procedure for the Calibration of a Structured Light System‖, Opt. Eng., 2004, 43(2): 464-471.
[42] Q. Hu, P. Huang, Q. Fu and F. Chiang,―Calibration of a 3D Shape Measurement System‖, Opt. Eng., 2003, 42(2): 487-493.
[43] C. C. Slama, C. Theurer and S. W. Henriksen,―Manual of Photogrammetry‖, VA: American Society of Photogrammetry, 1980.
[44] C. S. Fraser,―Photogrammetric Camera Component Calibration: A Review of Analytical Techniques‖, Calibration and Orientation of Camera in Computer Vision, Berlin Heidelberg, 2001, 95-136.
[45] F. J. Cuevas, M. Servin, O. N. Stavroudis and R. Rodriguez-Vera,―Multilayer Neural Networks Applied to Phase and Depth Recovery from Fringe Patterns‖, Opt. Commun., 2000, 181(4): 239-259.
[46] D. C. Brown,―Close-range Camera Calibration‖, Photogrametric Engineering, 1971, 37(8): 855-866.
[47] R. Y. Tsai,―A Versatile Camera Calibration Technique for High-accuracy 3D Machine Vision Metrology using Off-the-shelf TV Camera and Lenses‖, IEEE Int. J. Robot. Automat., 1987, 3(4): 323-344.
[48] G. Wei and S. Ma,―Implicit and Explicit Camera Calibration: Theory and Experiments‖, IEEE Trans. on Pattern Analysis and Machine Intelligence, 1994, 16(5): 469-480.
[49] S. Zhang and P. Huang,―Novel method for structured light system calibration‖, Opt. Eng., 2006, 45(8): 083601.
[50] P. Huang and X. Han,―On improving the accuracy of structured light systems‖, Proc. SPIE, 2006, 6382: 63820H.
[51] D. Malacara,―Optical Shop Testing‖, NY: John Wiley and Songs, 1992.
[52] X. Han,―3D Shape Measurement Based on the Phase Shifting and Stereovision Methods‖, [PH.D. Dissertation], NY: Stony Brook University, 2010.
[53] S. Zhang,―High-resolution, Real-time 3-D Shape Measurement‖, [PH.D. Dissertation], NY: Stony Brook University, 2005.
[54] R. Y. Tsai,―A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses‖, IEEE Int. J. Robot. Automat., 1987, RA-3: 323-344.
[55] P. Huang and X. Han,―On improving the accuracy of structured light systems‖, Proc. SPIE, 2006, 6382: 63820H.
[56] J. Y. Bouguet, Camera calibration toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc.