基于随机光照的双目立体测量关键技术及其系统研究
|
摘要
物体的三维外形轮廓测量技术是实施逆向工程、产品质量检测、虚拟现实等的前端基础,在飞机、汽车、船舶、模具、娱乐、生物医学等行业有广泛的应用需求。本文深入研究了基于随机光场照射的双目立体测量关键技术,包括图像处理技术、摄像机标定技术、匹配重建技术、测量数据拼合技术和系统软硬件实现技术等,自主研发了RIMS(Random Illumination Measurement System)三维点云测量原型系统。本文的主要研究内容和成果可以概括为:
1、提出了通过瞬时随机光场照射结合双CCD立体图像进行快速三维轮廓测量的总体方案。不同于大多数在一个时间序列上多次投射不同的结构化光照模式到被测物体表面的方法,本文提出的方案只需要一个自然光照下的立体图像对用于多视角测量数据拼合,以及一个瞬时随机光场照射下的立体图像对用于产生三维点云数据。其优点是随机光照立体图像对在瞬间拍摄,因此不会由于单次测量过程中被测物体的不稳定影响测量,非常适合现场测量和对非静态物体的测量。同时,该方案对光场投射装置没有严格的畸变度限制,而且不需要光栅移动(或转动)的机械装置,也无需散热结构,从而可以使投影器乃至整个三维测量系统紧凑小巧、成本降低。
2、研究了图像的轮廓提取和椭圆中心提取算法,针对研究中采用的圆形目标点,改进了一个基于亚像素边缘的灰度矩最小二乘椭圆中心定位算法,与传统的灰度矩算法相比,有效地提高了圆形目标中心的图像定位精度。同时,针对在实际测量图像中可能存在的各种类型的伪椭圆目标,提出了伪目标的多准则过滤去除算法,能够高效稳健地识别出真实的特征椭圆,为系统标定及多视角测量数据拼合奠定了基础。
3、深入研究了摄像机模型和双目立体测量系统的标定技术,通过实验,确定了RIMS测量系统的摄像机畸变模型,提出了一种简便易行的双目立体测量系统三步优化标定方法。该方法将基于平面模板的标定方法和自标定方法相结合,在传统两步优化法的基础上,进一步将标定板上特征圆点中心的空间坐标作为优化变量进行优化求解,并用已知两个点之间的实际距离精确恢复由此带来的系统绝对尺度变化。由于本文提出的三步优化标定方法考虑到了标定板几何误差的影响,并通过算法优化确定实际的标定板几何信息,从而降低了标定板的制作和计量校准要求,并且能够获得较高精度的标定结果。
4、提出并实现了面向表面点云测量的双目立体图像匹配和三维重建算法。该算法在综合灰度信息约束和几何信息约束的基础上,利用最小二乘法,在同名像点匹配的同时得到相应三维坐标。提出了采用加权匹配窗口的方法,能够一定程度上改善模型细节部分的重建效果;采用独立于最小二乘模型之外的灰度矫正策略,一方面提高了算法收敛的稳定性,另一方面有助于提高算法效率;利用几何一致性和连续性约束,提出了用于确定匹配初始值的生长法,能够明显减少算法迭代次数,提高算法效率。实验证明,在辅以随机光场照射的情况下,本文方法能够根据一个立体图像对获得高质量的物体表面点云数据。
5、提出并实现了基于圆形标记点的多视角测量数据拼合算法。首先研究了圆形标记特征在立体图像对之间的迭代松弛匹配算法,提出了“自适应部分赢者通吃”的更新策略;在多视角测量数据的拼合中,首先利用欧氏变换对长度的不变性,进行两两视图拼合,然后再对多视角测量数据的整体拼合结果进行全局优化,有效地减小了全局拼合误差。
6、在基础理论和关键算法研究的基础上,构建了基于随机光场照射的双目立体测量原型系统RIMS的硬件平台,提出了软件的总体架构、数据结构以及数据管理体系,用VC++实现了文中提出的所有算法,实现了标定、测量、拼合等系统功能。
文中提出的各项算法和系统功能均进行了翔实的实验、对比和分析,应用研发的原型系统,测量了大量具有代表性的三维实物,对测量精度和测量效率等方面进行了验证,效果良好。
3D profile measuring is a highly demanding technique for industrial applications, such as reverse engineering, product quality control and virtual reality. It can be widely used in aerospace manufacturing, automobile making, shipbuilding, machine-tool industries, biomedicine, die manufacturing, etc. Among the existing 3D measurement techniques, image-based methods have demonstrated its vitality and promising future for its characteristics of non-tactility, high precision, efficiency and practicability. One optical method based on instantaneous random illumination has been fully studied in this thesis, and so does the key techniques for binocular stereo measurement including image analysis, camera calibration, matching, reconstruction, registration, software & hardware implementation, etc. The thesis’s main contributions are as follows.
Binocular stereo measurement key techniques based on instantaneous random illumination have been researched. To acquire 3D point cloud data of a target object with low cost and simple setup, an instantaneous random illumination projection facility has been made to instantaneously project a random image pattern onto the object surface. Stereo images taken from two calibrated cameras with fixed geometry are matched and reconstructed for 3D point cloud acquisition. Since the stereo image pair is snapped simultaneously and instantaneously, the system is practicable for field measurement and moving objects’measurement because of its immunity to vibration of the object. In addition, since there are no strict constraints on light pattern projection such as distortion control, no raster movement mechanics and no cooling hardware, the structure of the projector and futher even the whole system can be compact and of low cost.
Since the inputs of the binocular measurement are all images, image processing has been researched including contour detection and ellipse location. The outputs of the image processing algorithms serve as the input of afterwards camera calibration and image matching. Circular features are used as fiducial markers. Subpixel ellipse center detection and location algorithms based on least squares methods are analyzed. Since many kinds of fake features may exist in the real images, a fake ellipse removing algorithm is proposed. This ellipse extraction algorithm is robust and of high precision, and serves well as the input for the following stereo calibration and stereo registration algorithms.
Camera model and binocular calibration have been researched. The measurement system’s camera distortion model has been carefully selected based on many experiments’analyse, and a practicable 3-step stereo calibration algorithm is proposed. In this method, planar plate based calibration is adopted. A normal 2-step calibration process is adopted which is similar to other typical calibration methods, and a further optimization procedure is included which takes feature points’3D coordinates as parameters. And after that, real metrics are obtained by scaling with respect to known distance between two fixed points. Since 3D coordinates’errors are taken into consider, calibration could be done with good precision while calibration plates’can be easily made.
Aiming at binocular stereo reconstruction, a stereo matching and reconstruction algorithm is proposed. During reconstruction and matching, image intensity and geometrical constraint information are taken into account. The least squares method is applied to iteratively resolve the affine parameters and 3D coordinates. Image grey level correction is done before the least squares resolvement for the sake of speed. And with the assumed continuity constraint, a region growing strategy has been adopted. It is demonstrated by experiments that our matching and reconstruction algorithm can acquire fairly good surface point cloud for normal usage.
Aiming at the registration of point cloud data computed from different views, registration algorithm with global optimization based on circular features has been proposed. Since the coordinate system changes between different views, a registration algorithm for transforming these point cloud data is needed. Firstly, an adaptive relaxation algorithm with“adaptive some winners take all”strategy is proposed for feature based image registration; secondly, 3D registration for two views is performed based on the fact that the Euclidean distance between any two points remains unchanged in different views; and at last, global optimization is implemented for the final 3D registration. Experiments show that the registration algorithm can robustly transfrom different views’data to a universal coordinate system.
A prototype binocular measurement system, namely RIMS (Random Illumination Measurement System) based on instantaneous random illumination is designed and made with above key algorithms and techniques. For the hardware, suitable cameras and lens are selected; an instantaneous random illumination projector is made for instantaneous random pattern projection; a circuit board is designed for synchronization of camera image capture and lamp flash. For the software, stereo calibration, image matching and 3D reconstruction, cloud polygonization and display algorithm et al. have all been coded, implemented and integrated with VC++ for the primary functionality of the system.
The proposed algorithms and system functionalities have all been fully experimented and analyzed. It has been demonstrated that the representive target objects’surface point cloud can be obtained fairly well by the prototype system. Because of its characteristics of effectiveness and good precision, the solution proposed in this paper has great potential tobe widely used in diverse applications.
1、提出了通过瞬时随机光场照射结合双CCD立体图像进行快速三维轮廓测量的总体方案。不同于大多数在一个时间序列上多次投射不同的结构化光照模式到被测物体表面的方法,本文提出的方案只需要一个自然光照下的立体图像对用于多视角测量数据拼合,以及一个瞬时随机光场照射下的立体图像对用于产生三维点云数据。其优点是随机光照立体图像对在瞬间拍摄,因此不会由于单次测量过程中被测物体的不稳定影响测量,非常适合现场测量和对非静态物体的测量。同时,该方案对光场投射装置没有严格的畸变度限制,而且不需要光栅移动(或转动)的机械装置,也无需散热结构,从而可以使投影器乃至整个三维测量系统紧凑小巧、成本降低。
2、研究了图像的轮廓提取和椭圆中心提取算法,针对研究中采用的圆形目标点,改进了一个基于亚像素边缘的灰度矩最小二乘椭圆中心定位算法,与传统的灰度矩算法相比,有效地提高了圆形目标中心的图像定位精度。同时,针对在实际测量图像中可能存在的各种类型的伪椭圆目标,提出了伪目标的多准则过滤去除算法,能够高效稳健地识别出真实的特征椭圆,为系统标定及多视角测量数据拼合奠定了基础。
3、深入研究了摄像机模型和双目立体测量系统的标定技术,通过实验,确定了RIMS测量系统的摄像机畸变模型,提出了一种简便易行的双目立体测量系统三步优化标定方法。该方法将基于平面模板的标定方法和自标定方法相结合,在传统两步优化法的基础上,进一步将标定板上特征圆点中心的空间坐标作为优化变量进行优化求解,并用已知两个点之间的实际距离精确恢复由此带来的系统绝对尺度变化。由于本文提出的三步优化标定方法考虑到了标定板几何误差的影响,并通过算法优化确定实际的标定板几何信息,从而降低了标定板的制作和计量校准要求,并且能够获得较高精度的标定结果。
4、提出并实现了面向表面点云测量的双目立体图像匹配和三维重建算法。该算法在综合灰度信息约束和几何信息约束的基础上,利用最小二乘法,在同名像点匹配的同时得到相应三维坐标。提出了采用加权匹配窗口的方法,能够一定程度上改善模型细节部分的重建效果;采用独立于最小二乘模型之外的灰度矫正策略,一方面提高了算法收敛的稳定性,另一方面有助于提高算法效率;利用几何一致性和连续性约束,提出了用于确定匹配初始值的生长法,能够明显减少算法迭代次数,提高算法效率。实验证明,在辅以随机光场照射的情况下,本文方法能够根据一个立体图像对获得高质量的物体表面点云数据。
5、提出并实现了基于圆形标记点的多视角测量数据拼合算法。首先研究了圆形标记特征在立体图像对之间的迭代松弛匹配算法,提出了“自适应部分赢者通吃”的更新策略;在多视角测量数据的拼合中,首先利用欧氏变换对长度的不变性,进行两两视图拼合,然后再对多视角测量数据的整体拼合结果进行全局优化,有效地减小了全局拼合误差。
6、在基础理论和关键算法研究的基础上,构建了基于随机光场照射的双目立体测量原型系统RIMS的硬件平台,提出了软件的总体架构、数据结构以及数据管理体系,用VC++实现了文中提出的所有算法,实现了标定、测量、拼合等系统功能。
文中提出的各项算法和系统功能均进行了翔实的实验、对比和分析,应用研发的原型系统,测量了大量具有代表性的三维实物,对测量精度和测量效率等方面进行了验证,效果良好。
3D profile measuring is a highly demanding technique for industrial applications, such as reverse engineering, product quality control and virtual reality. It can be widely used in aerospace manufacturing, automobile making, shipbuilding, machine-tool industries, biomedicine, die manufacturing, etc. Among the existing 3D measurement techniques, image-based methods have demonstrated its vitality and promising future for its characteristics of non-tactility, high precision, efficiency and practicability. One optical method based on instantaneous random illumination has been fully studied in this thesis, and so does the key techniques for binocular stereo measurement including image analysis, camera calibration, matching, reconstruction, registration, software & hardware implementation, etc. The thesis’s main contributions are as follows.
Binocular stereo measurement key techniques based on instantaneous random illumination have been researched. To acquire 3D point cloud data of a target object with low cost and simple setup, an instantaneous random illumination projection facility has been made to instantaneously project a random image pattern onto the object surface. Stereo images taken from two calibrated cameras with fixed geometry are matched and reconstructed for 3D point cloud acquisition. Since the stereo image pair is snapped simultaneously and instantaneously, the system is practicable for field measurement and moving objects’measurement because of its immunity to vibration of the object. In addition, since there are no strict constraints on light pattern projection such as distortion control, no raster movement mechanics and no cooling hardware, the structure of the projector and futher even the whole system can be compact and of low cost.
Since the inputs of the binocular measurement are all images, image processing has been researched including contour detection and ellipse location. The outputs of the image processing algorithms serve as the input of afterwards camera calibration and image matching. Circular features are used as fiducial markers. Subpixel ellipse center detection and location algorithms based on least squares methods are analyzed. Since many kinds of fake features may exist in the real images, a fake ellipse removing algorithm is proposed. This ellipse extraction algorithm is robust and of high precision, and serves well as the input for the following stereo calibration and stereo registration algorithms.
Camera model and binocular calibration have been researched. The measurement system’s camera distortion model has been carefully selected based on many experiments’analyse, and a practicable 3-step stereo calibration algorithm is proposed. In this method, planar plate based calibration is adopted. A normal 2-step calibration process is adopted which is similar to other typical calibration methods, and a further optimization procedure is included which takes feature points’3D coordinates as parameters. And after that, real metrics are obtained by scaling with respect to known distance between two fixed points. Since 3D coordinates’errors are taken into consider, calibration could be done with good precision while calibration plates’can be easily made.
Aiming at binocular stereo reconstruction, a stereo matching and reconstruction algorithm is proposed. During reconstruction and matching, image intensity and geometrical constraint information are taken into account. The least squares method is applied to iteratively resolve the affine parameters and 3D coordinates. Image grey level correction is done before the least squares resolvement for the sake of speed. And with the assumed continuity constraint, a region growing strategy has been adopted. It is demonstrated by experiments that our matching and reconstruction algorithm can acquire fairly good surface point cloud for normal usage.
Aiming at the registration of point cloud data computed from different views, registration algorithm with global optimization based on circular features has been proposed. Since the coordinate system changes between different views, a registration algorithm for transforming these point cloud data is needed. Firstly, an adaptive relaxation algorithm with“adaptive some winners take all”strategy is proposed for feature based image registration; secondly, 3D registration for two views is performed based on the fact that the Euclidean distance between any two points remains unchanged in different views; and at last, global optimization is implemented for the final 3D registration. Experiments show that the registration algorithm can robustly transfrom different views’data to a universal coordinate system.
A prototype binocular measurement system, namely RIMS (Random Illumination Measurement System) based on instantaneous random illumination is designed and made with above key algorithms and techniques. For the hardware, suitable cameras and lens are selected; an instantaneous random illumination projector is made for instantaneous random pattern projection; a circuit board is designed for synchronization of camera image capture and lamp flash. For the software, stereo calibration, image matching and 3D reconstruction, cloud polygonization and display algorithm et al. have all been coded, implemented and integrated with VC++ for the primary functionality of the system.
The proposed algorithms and system functionalities have all been fully experimented and analyzed. It has been demonstrated that the representive target objects’surface point cloud can be obtained fairly well by the prototype system. Because of its characteristics of effectiveness and good precision, the solution proposed in this paper has great potential tobe widely used in diverse applications.
引文
[1]张宏伟,张国雄,李真,刘征.飞机发动机叶片的非接触测量.航空精密制造技术, 2004, 40(4):34-36,46.
[2]韩清华,郑保,郭宏利,王鸿翔.采用激光跟踪仪测量飞机外形.航空计测技术, 2004, 24(1):15-16,33.
[3]周军.光学扫描测量技术在汽车产品质量控制中的应用.机械工人冷加工, 2006, 3:63,79.
[4]王建军,王颖.光栅法在船体横扭角测量中的应用.光学精密工程, 2005, 13(3):371-375.
[5]喻彩丽,曹淼龙.工业坐标测量系统的应用研究.计量与测试技术, 2005, 32(3):10-11.
[6]刘贵民.无损检测技术.北京,国防工业出版社, 2006.
[7]查选平.计算机辅助投射条纹乳房三维形态测量系统的研制[博士学位论文].广州,第一军医大学, 2005.
[8]程伟.非接触式人体三维测量-人体特征点识别及轮廓线提取[硕士学位论文].天津,天津工业大学, 2007.
[9]刘之生.反求工程技术.北京,机械工业出版社, 1996.
[10]张丽艳.逆向工程中模型重建关键技术研究[博士学位论文].南京,南京航空航天大学, 2001.
[11] T. Vàrady, R. Martin, J. Cox. Reverse engineering of geometric models—an introduction. Computer-Aided Design, 1997, 29(4):255-268.
[12]郑亚红,廖金玉.逆向工程中三维曲面测量技术应用.沈阳工业大学学报, 2006, 28(4):446-448,470.
[13]邾继贵,王浩,任同群,吴斌,刘常杰.便携式激光扫描三维形貌测量系统.机械工程学报, 2005, 41(2):166-169.
[14]王晶,张秀山,王锋.虚拟现实技术在军校教育中的应用研究.计算机与数字工程, 2006, 34(12):97-99,100.
[15]张伟,赵新灿,徐兴民.增强现实技术及其在航空发动机维修中的应用.江苏航空, 2006, 1:21-23.
[16]吴立军,肖玉军.浅谈数字化测量技术在田野考古中的应用.江汉考古, 2006, 3:74-77.
[17]丁军,赵明泽,陈祖军.利用近景摄影测量进行安丙家族墓文物考古测绘.工程勘察, 2002, 4:54-56.
[18]金刚.三维动画制作新技术及其展望.多媒体世界, 2000, 8:79-82.
[19] D. M. Gavrila, L. S. Davis. 3-D model-based tracking of humans in action: a multi-view approach. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Franciso, CA, USA, 1996:73-80.
[20]熊文平,李锦涛,张勇东,刘金刚.一种有效的体育视频目标跟踪算法.计算机工程与应用, 2006, 26:201-203,207.
[21]吴斌.大型物体三维形貌数字化测量关键技术研究[博士学位论文].天津,天津大学, 2002.
[22]王文格.基于计算机视觉的大型复杂曲面三维测量关键技术研究[博士学位论文].长沙,湖南大学, 2003.
[23] F. J. Shiou, Y. C. Lai. Development of a non-contact multi-axis reverse engineering measurement system for small complex objects. 7th International Symposium on Measurement Technology and Intelligent Instruments, Huddersfield, UK, 2005, 13(1):178-181.
[24] F. R. Livingstone, M. Rioux. Development of a large field of view 3-D vision. SPIE, 1986, 665:188-194.
[25] F. Chen, G. M. Brown, M. Song. Overview of three-dimensional shape measurement using optical methods. Optical Engineering, 2000, 39(1):10-22.
[26]赵宏,宋元鹤,李根乾等.一种新的360°三维轮廓术.光学学报, 1999, 19(4):497-500.
[27]张广军.机器视觉.北京,科学出版社, 2005.
[28]金观昌.计算机辅助光学测量.北京,清华大学出版社, 1997.
[29]邓劲莲,屠立,王循明.基于三坐标测量机的曲面轮廓反向工程数字化测量方法.机电工程, 2006, 23(6):22-24,40.
[30]王之卓.摄影测量原理.北京,测绘科技出版社, 1979.
[31] G. Ganci, H. B. Handley. Automation in videogrammetry. International Archives of Photogrammetry and Remote Sensing, Hakodate, 1998, 32(5):53-58.
[32] G. Ganci, J. Brown. Developments in non-contact measurement videogrammetry. Boeing Large Scale Metrology Conference, Long Beach, USA, 2000.
[33] X. Wang, T. A. Clarke. Separate adjustment of close range photogrammetric measurements. ISPRS, 1998, 32(5):177-184.
[34] D. Marr.视觉计算理论.姚国正,刘磊,汪云九.北京,科学出版社, 1988.
[35]吴立德.计算机视觉.上海,复旦大学出版社, 1993.
[36]徐光祐.计算机视觉.北京,清华大学出版社, 1999.
[37] Stephen T. Barnard, Martin A. Fischler. Computational stereo. ACM Computing Surveys, 1982, 14(4):553-572.
[38] M. Brown, Z. Burschka, G. D. Hager. Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9):993-1008.
[39] T. Heikkinen, R. Ahola, M. Manninen, et al. Recent results of the performance analysis of 3-D sensor based on time-of-flight measurements. Proceedings of SPIE– The International Society for Optical Engineering, Quebec City, Que., Canada, 1986, 665:168-173.
[40] R. A. Jarvis. A laser time-of-flight range scanner for robot vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983, 5:505-512.
[41] D. M. Meadows, et al. Generation of surface contours by moire pattern. Applied Optics, 1970, 9(4):942-947.
[42]于起峰,陆宏伟,刘肖琳.基于图像的精密测量与运动测量.北京,科学出版社, 2002.
[43] S. J. Gordon, W. P. Steering. Real-time part position sensing. IEEE Transactions on Pattern Analysis and machine Intelligence, 1988, 10(3):374-386.
[44] O. Ozeki, T. Nakano, S. Yamamoto. Real-time range measurement device for three-dimensional object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(4):550-554.
[45]许智钦.便携式彩色三维激光扫描系统的研究[博士学位论文].天津,天津大学, 2002.
[46]王昉.视觉传感器的优化设计及标定方法的研究[硕士学位论文].天津,天津大学, 1999.
[47] Rongsheng Lu, Shenghua Ye, Changku Sun. Study of the visual inspection system for vehicle wheel alignment. ISIST 99, Luoyang, 1999:448-452.
[48] Weiyi Liu, Zhaoqi Wang, Guoguang Mu, et al. A novel profilometry with color-coded project grating and its application in 3D reconstruction. Proceedings of APCC/OECC’99– 5th Asia Pacific Conference on Communications/4th Optoelectronics and Communicaitons Conference, Beijing, China, 1999, 2:1039-1042.
[49] N. Shrikhande, G. Stockman. Surface orientation from a projected grid. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(6):650-655.
[50] J. Salvi, J. Pagès, J. Batlle. Pattern codification strategies in structured light systems. Pattern Recognition, 2004, 37(4):827-849.
[51] T. Miyasaka, K. Kuroda, M. Hirose, K. Araki. High speed 3-D measurement system usingincoherent light source for human performance analysis. Proceedings of the 19th Congress of the International Society for Photogrammetry and Remote Sensing, the Netherlands, Amsterdam, 2000, 65–69.
[52] Z. J. Geng. Rainbow 3-dimensional camera: new concept of high-speed 3-dimensional vision systems. Optical Engineering, 1996, 35(2):376–383.
[53] H. J. W. Spoelder, F. M. Vos, E. M. Petriu, F. C. A. Groen. Some aspects of pseudo random binary array-based surface characterization. IEEE Transactions on Instrumentation and Measurement, 2000, 49(6):1331–1336.
[54] R. A. Morano, C. Ozturk, R. Conn, et al. Structured light using pseudorandom codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(3):322–327.
[55] J. Gühring. Dense 3-D surface acquisition by structured light using off-the-shelf components. Proceedings of SPIE– the International Society for Optical Engineering, 2001, 4309:220–231.
[56] R. J. Valkenburg, A. M. McIvor. Accurate 3D measurement using a structured light system. Image and Vision Computing, 1998, 16(2):99–110.
[57] M. Sj?dahl, P. Synnergren. Measurement of shape by using projected random patterns and temporal digital speckle photography. Applied Optics, 1999, 38(10):1153-1158.
[58]代红军,苏显渝.数字散斑时间序列相关三维面形测量方法,光学学报, 2001, 21(10):1208-1213.
[59] Xianyu Su, Wenjing Chen. Fourier transform profilometry: a review. Optics and Lasers in Engineering, 2001, 35(5):263-284.
[60] Wenjing Chen, Xianyu Su, Yiping Cao, et al. Improving Fourier transform profilometry based on bicolor fringe pattern. Optical Engineering, 2004, 43(1):192-198.
[61] Xianfu Mao, Wenjing Chen, Xianyu Su. Improved Fourier transform profilometry. Applied Optics, 2007, 46(5):664-668.
[62]高文,陈熙霖.计算机视觉-算法与系统原理.北京,清华大学出版社, 1998.
[63] R. Zhang, P. S. Tsai, J. E. Cryer, et al. Shape from shading: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(8):690-706.
[64]马颂德,张正友.计算机视觉-计算理论与算法基础.北京,科学出版社, 2003.
[65]郑南宁.计算机视觉与模式识别.北京,国防工业出版社, 1998.
[66]贾云德.机器视觉.北京,科学出版社, 2000.
[67] T. A. Clarke, T. J. Ellis, S. Robson. High accuracy 3-D measurement using multiple cameraviews. IEE Colloquium on‘3D Imaging and Analysis of Depth/Range Images’, London, UK, 1994, 1:1-54.
[68] R. Hartley, R. Gupta, T. Chang. Stereo from uncalibrated cameras. Proceedings of 1992 IEEE Computer Society Conference on Compouter Vision and Pattern Recognition, Chanpaign, IL, USA, 1992:761-764.
[69]游素亚,徐光祐.立体视觉研究的现状与进展.中国图像图形学报, 1997, 2(1):17-24.
[70] U. R. Dhond, J. K. Aggarwal. Structure from stereo - a review. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(6):1489-1510.
[71] S. D. Cochran, et al. 3-D surface description from binocular stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(10):981-994.
[72] J. Mulligan, et al. Trinocular stereo: a real-time algorithm and its evaluation. International Journal of Computer Vision, 2002, 47(1-3):51-61.
[73] R. Klette, A. Koschan, K. Schluns. Computer vision: three-dimensional data from images. Singapore. 1998.
[74]刘晶,张定华,王凯,赵歆波.基于工业CT图像的轮廓控制点优化提取.机械工程学报, 2006, 42(10):182-185.
[75]傅健,路宏年.工业CT半扫描成像技术.北京航空航天大学学报, 2005, 31(9):966-969.
[76]王英惠,赵汝嘉,栗金庆.基于逐层切削扫描测量法的实用化逆向工程.小型微型计算机系统, 2003, 24(7):1405-1408.
[77]马扬飚,钟约先,戴小林.大面积形体的三维无接触精确测量的研究.机械设计与制造, 2006, 10:24-26.
[78]叶海加,陈罡,邢渊.双目CCD结构光三维测量系统中的立体匹配.光学精密工程, 2004, 12(1):71-75.
[79]黄红强,冯华君,徐之海,李奇,戴顺林.彩色结构光三维成像技术.浙江大学学报, 2001, 35(6):588-591.
[80]许平,陈文静,苏显渝.高精度的数字光投影傅里叶变换轮廓术.光电工程, 2004, 32(11):59-62.
[81]祝世平,强锡富.基于坐标测量机的双目视觉测距误差分析.电子测量与仪器学报, 2000, 14(2):26-31.
[82] D. C. Brown. Close-range camera calibration. Photogrammetric Engineering. 1971, 37(8):855-866.
[83] J. Weng, P. Cohen, M. Herniou. Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(10):965-980.
[84] G. Q. Wei, S. D. Ma. Implicit and explicit camera calibration: theory and experiments. IEEE Transactions on Pattern and Machine Intelligence, 1994, 16(5):469-480.
[85] E. Trucco, A. Verri. Introductory techniques for 3-D computer vision. New Jersey, Prentice Hall, 1998.
[86] J. Salvi, X. Armangue, J. Batlle. A comparative review of camera calibrating methods with accuracy evaluation. Pattern Recognition, 2002, 35(7):1617-1635.
[87] J. Heikkila, O. Silven. Calibration procedure for short focal length off-the-shelf CCD cameras. Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria, 1996, 1(1):166-170.
[88] J. Heikkila, O. Silven. A four-step camera calibration procedure with implicit image correction. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 1997, 1106-1112.
[89]邱茂林,马颂德等.计算机视觉中摄像机定标综述.自动化学报, 2000, 26(1):43-55.
[90] G. Wei, S. Ma. A complete two-plane camera calibration method and experimental comparisons. Proceedings of 4th International Conference on Computer Vision, Berlin, Germany, 1993:439-446.
[91] Roger Y. Tsai. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation, 1987, 3(4):323-344.
[92] R. K. Lenz, R. Y. Tsai. Techniques for calibration of the scale factor and image center for high accuracy 3-d machine vision metrology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(5):713-720.
[93] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334.
[94] Richard I. Hartley. An algorithm for self calibration from several views. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, 1994, 908-912.
[95] Q. - T. Luong, O. D. Faugeras. Self-calibration of a moving camera from point correspondences and fundamental matrices. The International Journal of Computer Vision, 1997, 22(3):261-289.
[96] S. J. Maybank, O. D. Faugeras. A theory of self-calibration of a moving camera. The International Journal of Computer Vision, 1992, 8(2):123-152.
[97] O. D. Faugeras, Q. - T. Luong, S. J. Maybank. Camera self-calibration: theory and experiments. Proceedings of Compuer Vision– ECCV’92, Second European Conference on Computer Vision, Santa Margherita Ligure, Italy, 1992, 321-334.
[98] Q. - T. Luong, O. D. Faugeras. Self-calibration of a moving camera from point correspondences and fundamental matrices. The International Journal of Computer Vision, 1997, 22(3):261-289.
[99] Zhengyou Zhang. Camera calibration with one-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(7):892-899.
[100] Richard Hartley, Andrew Zisserman, Multiple view geometry in computer vision. Cambridge, Cambridge University Press, 2000.
[101] J. A. Sung, W. Rauh, M. Rechnagel. Ellipse fitting and parameter assessment of circular object targets for robot vision. Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyongju, South Korea, 1999, 1:525-530.
[102]管业鹏,童林夙,尹涵春.基于数码相机的三维物体空间几何位置的摄影测量.电子学报, 2002, 30(6):849-852.
[103] Rafael C. Gonzalez, Richard E. Woods. Digital image processing. Beijing, Publishing House of Electronics Industry, 2007.
[104] David Forsyth. Computer vision: a modern approach. Prentice Hall, 2002.
[105] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-698.
[106]周玲.基于多帧数字图像的三维重建关键技术研究[硕士学位论文].南京,南京航空航天大学, 2006.
[107] A. J. Tabatabai, O. R. Mitchell. Edge location to subpixel values in digital imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(2):188-201.
[108] E. P. Lyvers, O. R. Mitchell, M. L. Akey, et al. Subpixel measurements using a moment-based edge operator. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(12):1293-1309.
[109] S. Ghosal, R. Mehrotra. Orthogonal moment operators for subpixel edge detection. Pattern Recognition, 1993, 26(2):295-306.
[110] E. P. Lyvers, O. R. Mitchell. Precision edge contrast and orientation estimation. IEEETransactions on Pattern Analysis and Machine Intelligence, 1988, 10(6):927-937.
[111] C. W. Chong, P. Raveendran, R. Mukundan. A comparative analysis of algorithms for fast computation of Zernike moments. Pattern Recognition, 2003, 36(3):731-742.
[112] A. Huertas, G. Medioni. Detection of intensity changes with subpixel accuracy using laplacian-gaussian masks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(5):651-664.
[113] K. Jensen, D.Anastassiou. Subpixel edge location and the interpolation of still images. IEEE Transactions on Image Processing, 1995, 4(3):285-295.
[114] J. Heikkila. Moment and curvature preserving technique for accurate ellipse boundary detection. Proceedings of the 14th International Conference on Pattern Recognition, Brisbane, Qld., Austrialia, 1998(1):734-737.
[115] R. A. McLaughlin. Randomized Hough transform: improved ellipse detection with comparison. Pattern Recognition Letters, 1998, 19(3-4):299-305.
[116] Yao Jie, N. Kharma, P. Grogono. A fast robust GA-based ellipse detection. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK, 2004, 2:859-862.
[117] M. Piu, A. Fitzgibbon, R. Fisher. Ellipse-specific direct least-square fitting. Proceedings of 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland, 1996, 3:599-603.
[118] M. R. Shortis, T. A. Clarke, T. Short. A comparison of some techniques for the subpixel location of discrete target images. Proceedings of the SPIE– The International Society for Optical Engineering, Videometrics III, Boston, MA, USA, 1994, 2350:239-250.
[119] K. Forbes, A. Voigt, N. Bodika. An inexpensive, automatic and accurate camera calibration method. Proceedings of the 13th Annual South African Workshop on Pattern Recognition, 2002.
[120] J. More. The levenberg-marquardt algorithm, implementation and theory. In G. A. Watson editor, Numerical Analysis, Lecture Notes in Mathematics 630. Springer-Verlag, 1977.
[121] D. G. Lowe. Distinctive image features form scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2):91-101.
[122] V. Srinivassan, H. C. Liu, et al. Automated phase measuring profilometry of 3D diffuse object. Applied Optics, 1984, 23(18):3105-3108.
[123] A. Gruen Adaptive least-squares correlation - a powerful matching technique. J Photogramm Remote Sens, 1985, 53(2):167-187.
[124]陈鹰.遥感影像的数字摄影测量.上海,同济大学出版社, 2003.
[125] D. Scharstein, R. Szekiski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Comptuer Vision, 2002, 47(1/2/3):7-42.
[126] A. Gruen, E. Baltsavias. Geometrically constrained multiphoto matching. Photogrammetric Engineering and Remote Sensing, 1988, 54(5):633-641.
[127] E. Baltsavisa. Multiphoto geometrically constrained matching [Phd Dissertation]. Institut fur Geodasie und Photogrammetrice, der Eidgenossischen Technischen Hochschule, Zurich, 1991.
[128] M. A. Sutton, S. R. McNeil, J. D. Helm, Y. J. Chao. Advances in two-dimensional and three-dimensional computer vision. Photomechanics, Topics in Applied Physics, 2000, 77:323-372.
[129] Y. Xu, D. Wang, T. Feng, H. - Y. Shum. Stereo computation using radial adaptive windows. Proceedings of 16th International Conference on Pattern Recognition, Quebec City, Que., Canada, 2002, 3:595-598.
[130] K. Yoon, I. Kwoen. Adaptive support-weight approach for correspondence search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4):650-656.
[131] D. W. Eggert, A. Lorusso, R. B. Fisher. Estimating 3-D rigid body transformations: a comparison of four major algorithms. Machine Vision and Applications, 1997, 9(5-6):272-290.
[132]龙玺,钟约先,李任举等.结构光三维扫描测量的三维拼接技术.清华大学学报, 2002, 42(2):477-480.
[133] P. J. Besl, N. D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-255.
[134] H. Alt, P. Brass, M. Godau, et al. Computing the hausdorff distance of geometric patterns and shapes. Discrete and Computational Geometry, the Goodman-Pollack-Festschrift, 2003, 65-76.
[135] A. E. Johnson, S. B. Kang, Registration and integration of textured 3-D data, Proceedings of International Conference on Recent Advances in 3-D Digital Imaging and Modeling, Ottawa, Ont., Canada, 1997, 234-241.
[136] M. Greenspan, G. Godin. A nearest neighbor method for efficient ICP. Proceedings of 3rd International Conference on 3-D Digital Imaging and Modeling, Quebec City, Que., Canada, 2001, 161-168.
[137]潘小林,张丽艳,揭裕文,朱延娟.三维曲面部分匹配的算法研究.南京航空航天大学学报, 2004, 36(5):544-549.
[138] C. Langis, M. Greenspan, G. Godin. The parallel iterative closest point algorithm. Proceedingsof the 3rd International Conference on 3-D Digital Imaging and Modeling, Quebec City, Que., Canada, 2001, 195-202.
[139] Y. H. Liu Improving ICP with easy implementation for free-form surface matching. Pattern Recognition, 2004, 37(2):211-226.
[140] D. Chetwerikov, D. Stepanov, P. Krsek. Robust euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image and Vision Computing, 2005, 23(3):299-309.
[141] B. Krebs, P. Sieverding, B. Korn. A fuzzy ICP algorithm for 3D free-form object recognition. Proceedings of 13th International Conference on Pattern Recognition, Vienna, Austria, 1996, 1(1):539-543.
[142] G. Barequet, M. Sharir. Partial surface matching by using direct footprints. Computational Geometry: theory and applications, 1999, 12(1-2):45-62.
[143] G. Barequet, M. Sharir. Partial surface and volume matching in three dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(9):929-948.
[144] Sung Joon Ahn, Wolfgang Rauh. Circular coded target for automation of optical 3D-measurement and camera calibration. International Journal of Pattern Recognition and Artificial Intelligence, 2001, 15(6):905-919.
[145] J. Maciel, J. P. Costeira. A global solution to sparse correspondence problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(2):187-199.
[146] V. Kolmogorov, R. Zabih. Computing visual correspondence with occlusions using graph cuts. Proceedings of 8th IEEE International Conference on Computer Vision, Vancouver, BC, Canada, 2001, 2:508-515.
[147] Y. Ke, R. Sukthankar. PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the 2004 IEEE Computer Society Confercence on Computer Vision and Pattern Recognition, Washington, DC, USA, 2004, 2:506-513.
[148] S. B. Pollard. The PMF stereo algorithm: theory and implementation. Parallel Architectures and Computer Vision, Oxford, UK, 1988, 61-79.
[149] Z. Zhang, R. Deriche, O. Faugeras, Q. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 1995, 78(1-2):87-119.
[150] M. Galo, C. L. Tozzi. Feature-point based matching: a sequential approach based on relaxation labeling and relative orientation. Journal of WSCG, 2004, 12(1-3):1-8.
[151] W. Zhang, X. Gao, E. Sung, et al. A feature-based matching scheme: MPCD and robust matching strategy. Pattern Recognition Letters, 2007, 28(10):1222-1231.
[152] Y.– S. Chen, Y.– P. Hung, C.– S. Fuh. Fast block matching algorithm based on the winner-update strategy. IEEE Transcations on Image Processing, 2001, 10(8):1212-1222.
[153] K. S. Arun, T. S. Huang, S. D. Blostein. Least squares fitting of two 3-D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(5):698-700.
[154] B. K. P. Horn. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America, 1987, 4(4):629-642.
[155] R. Bergevin, M. Soucy, H. Gagnon, et al. Towards a general multi-view registration technique. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(5):540-547.
[156] R. Benjemaa, F. Schmitt. Fast global registration of 3D sampled surface using a multi-z-buffer technique. Proceedings of International Conference on Recent Advances in 3-D Digital Imaging and Modeling, Ottawa, Ont., Canada, 1999, 113-120.
[157] R. Benjemaa, F. Schmitt. A solution for the registration of multiple 3D point sets using Quaternions. Proceedings of Computer Vision– ECCV’98 5th European Conference on Computer Vision, Freiburg, Germany, 1998, 2:34-50.
[158] J. A. Williams, M. Bennamoun. Simultaneous registration of multiple corresponding point sets. Computer Vision and Image Understanding, 2001, 81(1):117-142.
[159] S. Krishnan, P. Y. Lee, J. B. Moore, et al. Global registration of multiple 3D point sets via optimization-on-a-manifold. Proceedings of the 3rd Eurographics symposium on Geometry processing, Vienna, Austria, 2005, 255:187-196.
[160] Micbael Barr. C/C++嵌入式系统编程.于志宏.北京,中国电力出版社, 2001.
[161]倪骁骅.形状误差测量结果不确定度的研究及应用[博士学位论文].合肥,合肥工业大学, 2002.
[162]陈敏,杨欣.仪器特性评定中的最大允差和测量不确定度.中国测试技术, 2008, 34(5):33-35.
[163]侯学锋,黄富贵,田树耀.形位误差测量不确定度研究状况.工具技术, 2008, 42(7):7-9.
[164]汪恺.形状和位置公差.北京,中国计划出版社, 2004.
[165]李晓沛,张琳娜,赵凤霞.简明公差标准应用手册.上海,上海科学技术出版社, 2005.
[166]张丽艳,周儒荣,周来水.三角网格模型孔洞修补算法研究.应用科学学报, 2002,20(3):221-224.
[167]杜佶,张丽艳,王宏涛,刘胜兰.基于径向基函数的三角网格曲面孔洞修补算法.计算机辅助设计与图形学学报, 2005, 17(9):1976-1982.
[168]韦争亮,钟约先,袁朝龙,李仁举.三角网格孔洞光顺修补算法的研究.中国机械工程, 2008, 19(8):949-954.
[2]韩清华,郑保,郭宏利,王鸿翔.采用激光跟踪仪测量飞机外形.航空计测技术, 2004, 24(1):15-16,33.
[3]周军.光学扫描测量技术在汽车产品质量控制中的应用.机械工人冷加工, 2006, 3:63,79.
[4]王建军,王颖.光栅法在船体横扭角测量中的应用.光学精密工程, 2005, 13(3):371-375.
[5]喻彩丽,曹淼龙.工业坐标测量系统的应用研究.计量与测试技术, 2005, 32(3):10-11.
[6]刘贵民.无损检测技术.北京,国防工业出版社, 2006.
[7]查选平.计算机辅助投射条纹乳房三维形态测量系统的研制[博士学位论文].广州,第一军医大学, 2005.
[8]程伟.非接触式人体三维测量-人体特征点识别及轮廓线提取[硕士学位论文].天津,天津工业大学, 2007.
[9]刘之生.反求工程技术.北京,机械工业出版社, 1996.
[10]张丽艳.逆向工程中模型重建关键技术研究[博士学位论文].南京,南京航空航天大学, 2001.
[11] T. Vàrady, R. Martin, J. Cox. Reverse engineering of geometric models—an introduction. Computer-Aided Design, 1997, 29(4):255-268.
[12]郑亚红,廖金玉.逆向工程中三维曲面测量技术应用.沈阳工业大学学报, 2006, 28(4):446-448,470.
[13]邾继贵,王浩,任同群,吴斌,刘常杰.便携式激光扫描三维形貌测量系统.机械工程学报, 2005, 41(2):166-169.
[14]王晶,张秀山,王锋.虚拟现实技术在军校教育中的应用研究.计算机与数字工程, 2006, 34(12):97-99,100.
[15]张伟,赵新灿,徐兴民.增强现实技术及其在航空发动机维修中的应用.江苏航空, 2006, 1:21-23.
[16]吴立军,肖玉军.浅谈数字化测量技术在田野考古中的应用.江汉考古, 2006, 3:74-77.
[17]丁军,赵明泽,陈祖军.利用近景摄影测量进行安丙家族墓文物考古测绘.工程勘察, 2002, 4:54-56.
[18]金刚.三维动画制作新技术及其展望.多媒体世界, 2000, 8:79-82.
[19] D. M. Gavrila, L. S. Davis. 3-D model-based tracking of humans in action: a multi-view approach. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, San Franciso, CA, USA, 1996:73-80.
[20]熊文平,李锦涛,张勇东,刘金刚.一种有效的体育视频目标跟踪算法.计算机工程与应用, 2006, 26:201-203,207.
[21]吴斌.大型物体三维形貌数字化测量关键技术研究[博士学位论文].天津,天津大学, 2002.
[22]王文格.基于计算机视觉的大型复杂曲面三维测量关键技术研究[博士学位论文].长沙,湖南大学, 2003.
[23] F. J. Shiou, Y. C. Lai. Development of a non-contact multi-axis reverse engineering measurement system for small complex objects. 7th International Symposium on Measurement Technology and Intelligent Instruments, Huddersfield, UK, 2005, 13(1):178-181.
[24] F. R. Livingstone, M. Rioux. Development of a large field of view 3-D vision. SPIE, 1986, 665:188-194.
[25] F. Chen, G. M. Brown, M. Song. Overview of three-dimensional shape measurement using optical methods. Optical Engineering, 2000, 39(1):10-22.
[26]赵宏,宋元鹤,李根乾等.一种新的360°三维轮廓术.光学学报, 1999, 19(4):497-500.
[27]张广军.机器视觉.北京,科学出版社, 2005.
[28]金观昌.计算机辅助光学测量.北京,清华大学出版社, 1997.
[29]邓劲莲,屠立,王循明.基于三坐标测量机的曲面轮廓反向工程数字化测量方法.机电工程, 2006, 23(6):22-24,40.
[30]王之卓.摄影测量原理.北京,测绘科技出版社, 1979.
[31] G. Ganci, H. B. Handley. Automation in videogrammetry. International Archives of Photogrammetry and Remote Sensing, Hakodate, 1998, 32(5):53-58.
[32] G. Ganci, J. Brown. Developments in non-contact measurement videogrammetry. Boeing Large Scale Metrology Conference, Long Beach, USA, 2000.
[33] X. Wang, T. A. Clarke. Separate adjustment of close range photogrammetric measurements. ISPRS, 1998, 32(5):177-184.
[34] D. Marr.视觉计算理论.姚国正,刘磊,汪云九.北京,科学出版社, 1988.
[35]吴立德.计算机视觉.上海,复旦大学出版社, 1993.
[36]徐光祐.计算机视觉.北京,清华大学出版社, 1999.
[37] Stephen T. Barnard, Martin A. Fischler. Computational stereo. ACM Computing Surveys, 1982, 14(4):553-572.
[38] M. Brown, Z. Burschka, G. D. Hager. Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9):993-1008.
[39] T. Heikkinen, R. Ahola, M. Manninen, et al. Recent results of the performance analysis of 3-D sensor based on time-of-flight measurements. Proceedings of SPIE– The International Society for Optical Engineering, Quebec City, Que., Canada, 1986, 665:168-173.
[40] R. A. Jarvis. A laser time-of-flight range scanner for robot vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983, 5:505-512.
[41] D. M. Meadows, et al. Generation of surface contours by moire pattern. Applied Optics, 1970, 9(4):942-947.
[42]于起峰,陆宏伟,刘肖琳.基于图像的精密测量与运动测量.北京,科学出版社, 2002.
[43] S. J. Gordon, W. P. Steering. Real-time part position sensing. IEEE Transactions on Pattern Analysis and machine Intelligence, 1988, 10(3):374-386.
[44] O. Ozeki, T. Nakano, S. Yamamoto. Real-time range measurement device for three-dimensional object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(4):550-554.
[45]许智钦.便携式彩色三维激光扫描系统的研究[博士学位论文].天津,天津大学, 2002.
[46]王昉.视觉传感器的优化设计及标定方法的研究[硕士学位论文].天津,天津大学, 1999.
[47] Rongsheng Lu, Shenghua Ye, Changku Sun. Study of the visual inspection system for vehicle wheel alignment. ISIST 99, Luoyang, 1999:448-452.
[48] Weiyi Liu, Zhaoqi Wang, Guoguang Mu, et al. A novel profilometry with color-coded project grating and its application in 3D reconstruction. Proceedings of APCC/OECC’99– 5th Asia Pacific Conference on Communications/4th Optoelectronics and Communicaitons Conference, Beijing, China, 1999, 2:1039-1042.
[49] N. Shrikhande, G. Stockman. Surface orientation from a projected grid. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(6):650-655.
[50] J. Salvi, J. Pagès, J. Batlle. Pattern codification strategies in structured light systems. Pattern Recognition, 2004, 37(4):827-849.
[51] T. Miyasaka, K. Kuroda, M. Hirose, K. Araki. High speed 3-D measurement system usingincoherent light source for human performance analysis. Proceedings of the 19th Congress of the International Society for Photogrammetry and Remote Sensing, the Netherlands, Amsterdam, 2000, 65–69.
[52] Z. J. Geng. Rainbow 3-dimensional camera: new concept of high-speed 3-dimensional vision systems. Optical Engineering, 1996, 35(2):376–383.
[53] H. J. W. Spoelder, F. M. Vos, E. M. Petriu, F. C. A. Groen. Some aspects of pseudo random binary array-based surface characterization. IEEE Transactions on Instrumentation and Measurement, 2000, 49(6):1331–1336.
[54] R. A. Morano, C. Ozturk, R. Conn, et al. Structured light using pseudorandom codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(3):322–327.
[55] J. Gühring. Dense 3-D surface acquisition by structured light using off-the-shelf components. Proceedings of SPIE– the International Society for Optical Engineering, 2001, 4309:220–231.
[56] R. J. Valkenburg, A. M. McIvor. Accurate 3D measurement using a structured light system. Image and Vision Computing, 1998, 16(2):99–110.
[57] M. Sj?dahl, P. Synnergren. Measurement of shape by using projected random patterns and temporal digital speckle photography. Applied Optics, 1999, 38(10):1153-1158.
[58]代红军,苏显渝.数字散斑时间序列相关三维面形测量方法,光学学报, 2001, 21(10):1208-1213.
[59] Xianyu Su, Wenjing Chen. Fourier transform profilometry: a review. Optics and Lasers in Engineering, 2001, 35(5):263-284.
[60] Wenjing Chen, Xianyu Su, Yiping Cao, et al. Improving Fourier transform profilometry based on bicolor fringe pattern. Optical Engineering, 2004, 43(1):192-198.
[61] Xianfu Mao, Wenjing Chen, Xianyu Su. Improved Fourier transform profilometry. Applied Optics, 2007, 46(5):664-668.
[62]高文,陈熙霖.计算机视觉-算法与系统原理.北京,清华大学出版社, 1998.
[63] R. Zhang, P. S. Tsai, J. E. Cryer, et al. Shape from shading: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21(8):690-706.
[64]马颂德,张正友.计算机视觉-计算理论与算法基础.北京,科学出版社, 2003.
[65]郑南宁.计算机视觉与模式识别.北京,国防工业出版社, 1998.
[66]贾云德.机器视觉.北京,科学出版社, 2000.
[67] T. A. Clarke, T. J. Ellis, S. Robson. High accuracy 3-D measurement using multiple cameraviews. IEE Colloquium on‘3D Imaging and Analysis of Depth/Range Images’, London, UK, 1994, 1:1-54.
[68] R. Hartley, R. Gupta, T. Chang. Stereo from uncalibrated cameras. Proceedings of 1992 IEEE Computer Society Conference on Compouter Vision and Pattern Recognition, Chanpaign, IL, USA, 1992:761-764.
[69]游素亚,徐光祐.立体视觉研究的现状与进展.中国图像图形学报, 1997, 2(1):17-24.
[70] U. R. Dhond, J. K. Aggarwal. Structure from stereo - a review. IEEE Transactions on Systems, Man, and Cybernetics, 1989, 19(6):1489-1510.
[71] S. D. Cochran, et al. 3-D surface description from binocular stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(10):981-994.
[72] J. Mulligan, et al. Trinocular stereo: a real-time algorithm and its evaluation. International Journal of Computer Vision, 2002, 47(1-3):51-61.
[73] R. Klette, A. Koschan, K. Schluns. Computer vision: three-dimensional data from images. Singapore. 1998.
[74]刘晶,张定华,王凯,赵歆波.基于工业CT图像的轮廓控制点优化提取.机械工程学报, 2006, 42(10):182-185.
[75]傅健,路宏年.工业CT半扫描成像技术.北京航空航天大学学报, 2005, 31(9):966-969.
[76]王英惠,赵汝嘉,栗金庆.基于逐层切削扫描测量法的实用化逆向工程.小型微型计算机系统, 2003, 24(7):1405-1408.
[77]马扬飚,钟约先,戴小林.大面积形体的三维无接触精确测量的研究.机械设计与制造, 2006, 10:24-26.
[78]叶海加,陈罡,邢渊.双目CCD结构光三维测量系统中的立体匹配.光学精密工程, 2004, 12(1):71-75.
[79]黄红强,冯华君,徐之海,李奇,戴顺林.彩色结构光三维成像技术.浙江大学学报, 2001, 35(6):588-591.
[80]许平,陈文静,苏显渝.高精度的数字光投影傅里叶变换轮廓术.光电工程, 2004, 32(11):59-62.
[81]祝世平,强锡富.基于坐标测量机的双目视觉测距误差分析.电子测量与仪器学报, 2000, 14(2):26-31.
[82] D. C. Brown. Close-range camera calibration. Photogrammetric Engineering. 1971, 37(8):855-866.
[83] J. Weng, P. Cohen, M. Herniou. Camera calibration with distortion models and accuracy evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(10):965-980.
[84] G. Q. Wei, S. D. Ma. Implicit and explicit camera calibration: theory and experiments. IEEE Transactions on Pattern and Machine Intelligence, 1994, 16(5):469-480.
[85] E. Trucco, A. Verri. Introductory techniques for 3-D computer vision. New Jersey, Prentice Hall, 1998.
[86] J. Salvi, X. Armangue, J. Batlle. A comparative review of camera calibrating methods with accuracy evaluation. Pattern Recognition, 2002, 35(7):1617-1635.
[87] J. Heikkila, O. Silven. Calibration procedure for short focal length off-the-shelf CCD cameras. Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria, 1996, 1(1):166-170.
[88] J. Heikkila, O. Silven. A four-step camera calibration procedure with implicit image correction. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Juan, Puerto Rico, 1997, 1106-1112.
[89]邱茂林,马颂德等.计算机视觉中摄像机定标综述.自动化学报, 2000, 26(1):43-55.
[90] G. Wei, S. Ma. A complete two-plane camera calibration method and experimental comparisons. Proceedings of 4th International Conference on Computer Vision, Berlin, Germany, 1993:439-446.
[91] Roger Y. Tsai. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation, 1987, 3(4):323-344.
[92] R. K. Lenz, R. Y. Tsai. Techniques for calibration of the scale factor and image center for high accuracy 3-d machine vision metrology. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(5):713-720.
[93] Zhengyou Zhang. A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11):1330-1334.
[94] Richard I. Hartley. An algorithm for self calibration from several views. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, 1994, 908-912.
[95] Q. - T. Luong, O. D. Faugeras. Self-calibration of a moving camera from point correspondences and fundamental matrices. The International Journal of Computer Vision, 1997, 22(3):261-289.
[96] S. J. Maybank, O. D. Faugeras. A theory of self-calibration of a moving camera. The International Journal of Computer Vision, 1992, 8(2):123-152.
[97] O. D. Faugeras, Q. - T. Luong, S. J. Maybank. Camera self-calibration: theory and experiments. Proceedings of Compuer Vision– ECCV’92, Second European Conference on Computer Vision, Santa Margherita Ligure, Italy, 1992, 321-334.
[98] Q. - T. Luong, O. D. Faugeras. Self-calibration of a moving camera from point correspondences and fundamental matrices. The International Journal of Computer Vision, 1997, 22(3):261-289.
[99] Zhengyou Zhang. Camera calibration with one-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(7):892-899.
[100] Richard Hartley, Andrew Zisserman, Multiple view geometry in computer vision. Cambridge, Cambridge University Press, 2000.
[101] J. A. Sung, W. Rauh, M. Rechnagel. Ellipse fitting and parameter assessment of circular object targets for robot vision. Proceedings of the 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems, Kyongju, South Korea, 1999, 1:525-530.
[102]管业鹏,童林夙,尹涵春.基于数码相机的三维物体空间几何位置的摄影测量.电子学报, 2002, 30(6):849-852.
[103] Rafael C. Gonzalez, Richard E. Woods. Digital image processing. Beijing, Publishing House of Electronics Industry, 2007.
[104] David Forsyth. Computer vision: a modern approach. Prentice Hall, 2002.
[105] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6):679-698.
[106]周玲.基于多帧数字图像的三维重建关键技术研究[硕士学位论文].南京,南京航空航天大学, 2006.
[107] A. J. Tabatabai, O. R. Mitchell. Edge location to subpixel values in digital imagery. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(2):188-201.
[108] E. P. Lyvers, O. R. Mitchell, M. L. Akey, et al. Subpixel measurements using a moment-based edge operator. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(12):1293-1309.
[109] S. Ghosal, R. Mehrotra. Orthogonal moment operators for subpixel edge detection. Pattern Recognition, 1993, 26(2):295-306.
[110] E. P. Lyvers, O. R. Mitchell. Precision edge contrast and orientation estimation. IEEETransactions on Pattern Analysis and Machine Intelligence, 1988, 10(6):927-937.
[111] C. W. Chong, P. Raveendran, R. Mukundan. A comparative analysis of algorithms for fast computation of Zernike moments. Pattern Recognition, 2003, 36(3):731-742.
[112] A. Huertas, G. Medioni. Detection of intensity changes with subpixel accuracy using laplacian-gaussian masks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(5):651-664.
[113] K. Jensen, D.Anastassiou. Subpixel edge location and the interpolation of still images. IEEE Transactions on Image Processing, 1995, 4(3):285-295.
[114] J. Heikkila. Moment and curvature preserving technique for accurate ellipse boundary detection. Proceedings of the 14th International Conference on Pattern Recognition, Brisbane, Qld., Austrialia, 1998(1):734-737.
[115] R. A. McLaughlin. Randomized Hough transform: improved ellipse detection with comparison. Pattern Recognition Letters, 1998, 19(3-4):299-305.
[116] Yao Jie, N. Kharma, P. Grogono. A fast robust GA-based ellipse detection. Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK, 2004, 2:859-862.
[117] M. Piu, A. Fitzgibbon, R. Fisher. Ellipse-specific direct least-square fitting. Proceedings of 3rd IEEE International Conference on Image Processing, Lausanne, Switzerland, 1996, 3:599-603.
[118] M. R. Shortis, T. A. Clarke, T. Short. A comparison of some techniques for the subpixel location of discrete target images. Proceedings of the SPIE– The International Society for Optical Engineering, Videometrics III, Boston, MA, USA, 1994, 2350:239-250.
[119] K. Forbes, A. Voigt, N. Bodika. An inexpensive, automatic and accurate camera calibration method. Proceedings of the 13th Annual South African Workshop on Pattern Recognition, 2002.
[120] J. More. The levenberg-marquardt algorithm, implementation and theory. In G. A. Watson editor, Numerical Analysis, Lecture Notes in Mathematics 630. Springer-Verlag, 1977.
[121] D. G. Lowe. Distinctive image features form scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2):91-101.
[122] V. Srinivassan, H. C. Liu, et al. Automated phase measuring profilometry of 3D diffuse object. Applied Optics, 1984, 23(18):3105-3108.
[123] A. Gruen Adaptive least-squares correlation - a powerful matching technique. J Photogramm Remote Sens, 1985, 53(2):167-187.
[124]陈鹰.遥感影像的数字摄影测量.上海,同济大学出版社, 2003.
[125] D. Scharstein, R. Szekiski. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Comptuer Vision, 2002, 47(1/2/3):7-42.
[126] A. Gruen, E. Baltsavias. Geometrically constrained multiphoto matching. Photogrammetric Engineering and Remote Sensing, 1988, 54(5):633-641.
[127] E. Baltsavisa. Multiphoto geometrically constrained matching [Phd Dissertation]. Institut fur Geodasie und Photogrammetrice, der Eidgenossischen Technischen Hochschule, Zurich, 1991.
[128] M. A. Sutton, S. R. McNeil, J. D. Helm, Y. J. Chao. Advances in two-dimensional and three-dimensional computer vision. Photomechanics, Topics in Applied Physics, 2000, 77:323-372.
[129] Y. Xu, D. Wang, T. Feng, H. - Y. Shum. Stereo computation using radial adaptive windows. Proceedings of 16th International Conference on Pattern Recognition, Quebec City, Que., Canada, 2002, 3:595-598.
[130] K. Yoon, I. Kwoen. Adaptive support-weight approach for correspondence search. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4):650-656.
[131] D. W. Eggert, A. Lorusso, R. B. Fisher. Estimating 3-D rigid body transformations: a comparison of four major algorithms. Machine Vision and Applications, 1997, 9(5-6):272-290.
[132]龙玺,钟约先,李任举等.结构光三维扫描测量的三维拼接技术.清华大学学报, 2002, 42(2):477-480.
[133] P. J. Besl, N. D. McKay. A method for registration of 3-d shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2):239-255.
[134] H. Alt, P. Brass, M. Godau, et al. Computing the hausdorff distance of geometric patterns and shapes. Discrete and Computational Geometry, the Goodman-Pollack-Festschrift, 2003, 65-76.
[135] A. E. Johnson, S. B. Kang, Registration and integration of textured 3-D data, Proceedings of International Conference on Recent Advances in 3-D Digital Imaging and Modeling, Ottawa, Ont., Canada, 1997, 234-241.
[136] M. Greenspan, G. Godin. A nearest neighbor method for efficient ICP. Proceedings of 3rd International Conference on 3-D Digital Imaging and Modeling, Quebec City, Que., Canada, 2001, 161-168.
[137]潘小林,张丽艳,揭裕文,朱延娟.三维曲面部分匹配的算法研究.南京航空航天大学学报, 2004, 36(5):544-549.
[138] C. Langis, M. Greenspan, G. Godin. The parallel iterative closest point algorithm. Proceedingsof the 3rd International Conference on 3-D Digital Imaging and Modeling, Quebec City, Que., Canada, 2001, 195-202.
[139] Y. H. Liu Improving ICP with easy implementation for free-form surface matching. Pattern Recognition, 2004, 37(2):211-226.
[140] D. Chetwerikov, D. Stepanov, P. Krsek. Robust euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image and Vision Computing, 2005, 23(3):299-309.
[141] B. Krebs, P. Sieverding, B. Korn. A fuzzy ICP algorithm for 3D free-form object recognition. Proceedings of 13th International Conference on Pattern Recognition, Vienna, Austria, 1996, 1(1):539-543.
[142] G. Barequet, M. Sharir. Partial surface matching by using direct footprints. Computational Geometry: theory and applications, 1999, 12(1-2):45-62.
[143] G. Barequet, M. Sharir. Partial surface and volume matching in three dimensions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(9):929-948.
[144] Sung Joon Ahn, Wolfgang Rauh. Circular coded target for automation of optical 3D-measurement and camera calibration. International Journal of Pattern Recognition and Artificial Intelligence, 2001, 15(6):905-919.
[145] J. Maciel, J. P. Costeira. A global solution to sparse correspondence problems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(2):187-199.
[146] V. Kolmogorov, R. Zabih. Computing visual correspondence with occlusions using graph cuts. Proceedings of 8th IEEE International Conference on Computer Vision, Vancouver, BC, Canada, 2001, 2:508-515.
[147] Y. Ke, R. Sukthankar. PCA-SIFT: A more distinctive representation for local image descriptors. Proceedings of the 2004 IEEE Computer Society Confercence on Computer Vision and Pattern Recognition, Washington, DC, USA, 2004, 2:506-513.
[148] S. B. Pollard. The PMF stereo algorithm: theory and implementation. Parallel Architectures and Computer Vision, Oxford, UK, 1988, 61-79.
[149] Z. Zhang, R. Deriche, O. Faugeras, Q. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Artificial Intelligence, 1995, 78(1-2):87-119.
[150] M. Galo, C. L. Tozzi. Feature-point based matching: a sequential approach based on relaxation labeling and relative orientation. Journal of WSCG, 2004, 12(1-3):1-8.
[151] W. Zhang, X. Gao, E. Sung, et al. A feature-based matching scheme: MPCD and robust matching strategy. Pattern Recognition Letters, 2007, 28(10):1222-1231.
[152] Y.– S. Chen, Y.– P. Hung, C.– S. Fuh. Fast block matching algorithm based on the winner-update strategy. IEEE Transcations on Image Processing, 2001, 10(8):1212-1222.
[153] K. S. Arun, T. S. Huang, S. D. Blostein. Least squares fitting of two 3-D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, 9(5):698-700.
[154] B. K. P. Horn. Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America, 1987, 4(4):629-642.
[155] R. Bergevin, M. Soucy, H. Gagnon, et al. Towards a general multi-view registration technique. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(5):540-547.
[156] R. Benjemaa, F. Schmitt. Fast global registration of 3D sampled surface using a multi-z-buffer technique. Proceedings of International Conference on Recent Advances in 3-D Digital Imaging and Modeling, Ottawa, Ont., Canada, 1999, 113-120.
[157] R. Benjemaa, F. Schmitt. A solution for the registration of multiple 3D point sets using Quaternions. Proceedings of Computer Vision– ECCV’98 5th European Conference on Computer Vision, Freiburg, Germany, 1998, 2:34-50.
[158] J. A. Williams, M. Bennamoun. Simultaneous registration of multiple corresponding point sets. Computer Vision and Image Understanding, 2001, 81(1):117-142.
[159] S. Krishnan, P. Y. Lee, J. B. Moore, et al. Global registration of multiple 3D point sets via optimization-on-a-manifold. Proceedings of the 3rd Eurographics symposium on Geometry processing, Vienna, Austria, 2005, 255:187-196.
[160] Micbael Barr. C/C++嵌入式系统编程.于志宏.北京,中国电力出版社, 2001.
[161]倪骁骅.形状误差测量结果不确定度的研究及应用[博士学位论文].合肥,合肥工业大学, 2002.
[162]陈敏,杨欣.仪器特性评定中的最大允差和测量不确定度.中国测试技术, 2008, 34(5):33-35.
[163]侯学锋,黄富贵,田树耀.形位误差测量不确定度研究状况.工具技术, 2008, 42(7):7-9.
[164]汪恺.形状和位置公差.北京,中国计划出版社, 2004.
[165]李晓沛,张琳娜,赵凤霞.简明公差标准应用手册.上海,上海科学技术出版社, 2005.
[166]张丽艳,周儒荣,周来水.三角网格模型孔洞修补算法研究.应用科学学报, 2002,20(3):221-224.
[167]杜佶,张丽艳,王宏涛,刘胜兰.基于径向基函数的三角网格曲面孔洞修补算法.计算机辅助设计与图形学学报, 2005, 17(9):1976-1982.
[168]韦争亮,钟约先,袁朝龙,李仁举.三角网格孔洞光顺修补算法的研究.中国机械工程, 2008, 19(8):949-954.