基于摄像机和二维激光测距仪的三维建模关键技术研究
|
摘要
基于摄像机和激光测距仪数据采集方式的三维建模技术一直被研究人员所关注,并在移动机器人的环境感知系统、工业制造、虚拟现实和虚拟增强等众多领域广泛应用。本文系统的分析了不同测量方法的三维建模技术,选择了环境要求低、价格适中的光斑不可见二维激光测距仪和摄像机构建了硬件系统,并建立了相应的传感器控制和数据采集处理软件系统。通过该系统采集数据,对图像特征提取技术、图像匹配技术、系统标定技术和彩色三维建模技术分别进行了方法研究及实际应用。
本文目的是:在理论上对基于二维激光测距仪和摄像机的三维建模关键技术进行研究;在实际应用上利用相应技术构建一个三维信息采集重建系统,对室内景物进行彩色三维建模。
本文工作主要创新之处如下:
1.提出了一个基于模糊阈值的直线提取算法。该算法可以有效解决复杂背景下有畸变图像中的直线提取问题。在铁路沿线的柱状体检测和其距铁轨距离估计的应用中验证了该算法的有效性。
2.提出了一个基于Delaunay三角网格的角点匹配算法。该算法主要利用Delaunay三角网格的唯一性,以及相同景物角点对应网格的相似性建立角点匹配。通过对实际图像的实验,证明了该方法对尺度变化和一定程度的旋转和平移变化图象有效。
3.对二维激光测距仪标定基本特征进行了分析,提出并设计了基于线特征直角三角形外形的标定物。通过应用于标定二维激光测距仪和摄像机,以及标定二维激光测距仪和旋转云台的实验,证明了设计分析的正确性。
4.提出了光斑不可见二维激光测距仪和摄像机的标定算法。有效解决测距数据和图像对应困难的问题。通过与现有算法的仿真和实际采集数据实验比较,证明该方法可以更精确标定设备和更有效融合两种传感器数据。
5.提出了二维激光测距仪和旋转云台标定算法。利用标定物正交边测距数据的共线性、正交性和共面性建立约束方程,有效解决了不同转角下测距数据对应困难的问题,有效标定设备。
6.提出了异步数据融合的彩色建模算法。解决实际工程应用中高频测距数据和高分辨率图像数据不能同步采集融合的问题。有效融合不同传感器信息,建立了真实感强的室内景物彩色三维点云模型。
3D construction technology based on the information collection by camera and laser range finder has attracted great attention all along. And this technology has been widely used in many fields such as the environment sensing using mobile robots, industry manufacture, virtual reality, and augmented reality. By detailed analysis of 3D construction technology based on the different measure method, we chose the 2D laser range finder with invisible stripe and the camera to establish the hardware system, because they have more suitable price and higher adaptability for the environment than other sensors of the 3D measurement. We had also established the software system to control the sensors, to obtain the information, and process the information. Using the measurement of the system, we focused on the methods study and practical application of the image feature extraction, the image matching, the system calibration and the color 3D modeling respectively.
Using the camera and the 2D laser range finder, our object is to study the key technologies of 3D model construction in theory, and to establish a 3D color reconstruction system to construct the 3D color model of the indoor scene in practice.
The main creativities of this paper are as follows:
1. A line extraction algorithm is presented based on the fuzzy threshold. The algorithm can effectively extract the line from the image with the distortion and the complex background. The effectiveness of the algorithm was proved in the application of the colunm detection and the distance estimation along the railway.
2. A corner matching algorithm is presented based on the uniqueness of Delaunay triangulated grid and the similarity of the grids for two matching corner sets in the same scene. Experiments on real images demonstrated the good performance of our method, and it was not affected by scale change, less affected by rotation and translation to some extent.
3. Based on the main features analysis for the 2D laser range finder calibration, we designed a right-angled triangular checkerboard using the line feature. The efficiency of the checkerboard was demonstrated in the calibration between the laser range finder and the camera. And the calibration between the 2D laser range finder and the pan-tilt platform also proved the checkerboard's efficiency.
4. An algorithm for the extrinsic parameter calibration of the camera and the 2D laser range finder with invisible stripe is presented which effectively solve the data correspondence problem of different sensors. Through the experiment of the simulated data and the real data captured by our system, it was proved that our method could more precisely calibrate the sensors and more effectively fuse the data than the existed calibration methods.
5. For the extrinsic parameter calibration of the pan-tilt platform and the 2D laser range finder with invisible stripe, an algorithm is presented based on the colinearity, coplanarity, and orthogonality of the range information. The experimental result proved that it could effectively solve the correspondence problem of the range information with the different pan-tilt platform's rotation angle.
6. An asynchronous information fusion algorithm is presented for 3D color model construction, which could solve the information fusion desynchronizing problem of high frequent range information and high resolution image in the applications. This algorithm could effectively fuse the information of different sensors and construct vivid 3D color point cloud model of indoor sense.
本文目的是:在理论上对基于二维激光测距仪和摄像机的三维建模关键技术进行研究;在实际应用上利用相应技术构建一个三维信息采集重建系统,对室内景物进行彩色三维建模。
本文工作主要创新之处如下:
1.提出了一个基于模糊阈值的直线提取算法。该算法可以有效解决复杂背景下有畸变图像中的直线提取问题。在铁路沿线的柱状体检测和其距铁轨距离估计的应用中验证了该算法的有效性。
2.提出了一个基于Delaunay三角网格的角点匹配算法。该算法主要利用Delaunay三角网格的唯一性,以及相同景物角点对应网格的相似性建立角点匹配。通过对实际图像的实验,证明了该方法对尺度变化和一定程度的旋转和平移变化图象有效。
3.对二维激光测距仪标定基本特征进行了分析,提出并设计了基于线特征直角三角形外形的标定物。通过应用于标定二维激光测距仪和摄像机,以及标定二维激光测距仪和旋转云台的实验,证明了设计分析的正确性。
4.提出了光斑不可见二维激光测距仪和摄像机的标定算法。有效解决测距数据和图像对应困难的问题。通过与现有算法的仿真和实际采集数据实验比较,证明该方法可以更精确标定设备和更有效融合两种传感器数据。
5.提出了二维激光测距仪和旋转云台标定算法。利用标定物正交边测距数据的共线性、正交性和共面性建立约束方程,有效解决了不同转角下测距数据对应困难的问题,有效标定设备。
6.提出了异步数据融合的彩色建模算法。解决实际工程应用中高频测距数据和高分辨率图像数据不能同步采集融合的问题。有效融合不同传感器信息,建立了真实感强的室内景物彩色三维点云模型。
3D construction technology based on the information collection by camera and laser range finder has attracted great attention all along. And this technology has been widely used in many fields such as the environment sensing using mobile robots, industry manufacture, virtual reality, and augmented reality. By detailed analysis of 3D construction technology based on the different measure method, we chose the 2D laser range finder with invisible stripe and the camera to establish the hardware system, because they have more suitable price and higher adaptability for the environment than other sensors of the 3D measurement. We had also established the software system to control the sensors, to obtain the information, and process the information. Using the measurement of the system, we focused on the methods study and practical application of the image feature extraction, the image matching, the system calibration and the color 3D modeling respectively.
Using the camera and the 2D laser range finder, our object is to study the key technologies of 3D model construction in theory, and to establish a 3D color reconstruction system to construct the 3D color model of the indoor scene in practice.
The main creativities of this paper are as follows:
1. A line extraction algorithm is presented based on the fuzzy threshold. The algorithm can effectively extract the line from the image with the distortion and the complex background. The effectiveness of the algorithm was proved in the application of the colunm detection and the distance estimation along the railway.
2. A corner matching algorithm is presented based on the uniqueness of Delaunay triangulated grid and the similarity of the grids for two matching corner sets in the same scene. Experiments on real images demonstrated the good performance of our method, and it was not affected by scale change, less affected by rotation and translation to some extent.
3. Based on the main features analysis for the 2D laser range finder calibration, we designed a right-angled triangular checkerboard using the line feature. The efficiency of the checkerboard was demonstrated in the calibration between the laser range finder and the camera. And the calibration between the 2D laser range finder and the pan-tilt platform also proved the checkerboard's efficiency.
4. An algorithm for the extrinsic parameter calibration of the camera and the 2D laser range finder with invisible stripe is presented which effectively solve the data correspondence problem of different sensors. Through the experiment of the simulated data and the real data captured by our system, it was proved that our method could more precisely calibrate the sensors and more effectively fuse the data than the existed calibration methods.
5. For the extrinsic parameter calibration of the pan-tilt platform and the 2D laser range finder with invisible stripe, an algorithm is presented based on the colinearity, coplanarity, and orthogonality of the range information. The experimental result proved that it could effectively solve the correspondence problem of the range information with the different pan-tilt platform's rotation angle.
6. An asynchronous information fusion algorithm is presented for 3D color model construction, which could solve the information fusion desynchronizing problem of high frequent range information and high resolution image in the applications. This algorithm could effectively fuse the information of different sensors and construct vivid 3D color point cloud model of indoor sense.
引文
[1]叶声华,王仲,曲星华.精密测试技术展望.中国机械工程,2000,11(3):262-264.
[2]熊有伦.数字装备与数字制造.中国国际机电产品博览会组委会,2003.
[3]徐智钦,孙长库.3D逆向工程技术.中国计量出版社,2002.
[4]叶声华,邾继贵等.视觉检测技术及应用.中国工程科学,1999,1(1):262-263.
[5]黄桂平,毋新房.经纬仪大尺寸三坐标测量系统MetroIn用于长河闸门的安装与测试.计量技术,2002,10:8-12.
[6]张顺德,魏正英,卢庚恒.逆向工程技术及其应用.甘肃工业大学学报,2001,27(2):25-28.
[7]谢华锟.现代精密量仪现状及水平—从近几界中国国际机床展览会看量具量仪发展趋势.现代切削与测量工程(国际)研讨会,2004,10.
[8]张国雄,裘祖荣.测试技术的作用与发展方向.现代切削与测量工程(国际)研讨会,2004,10.
[9]黄桂平.数字近景工业摄影测量关键技术研究与应用[博士学位论文].天津:天津大学精密仪器与光电子工程学院,2005,5.
[10]李燕,谭鸥,段会龙.三维医学图象可视化技术综述.中国图象图形学报,2001,6(2):103-110.
[11]E.Coche,S.Pawlak,S.Dechambre,et al.Peripheral pulmonary arteries:identification at multi-slice spiral CT with 3D reconstructions.Eur Radiol.2003,13(4):815-822.
[12]吴剑波,骆清铭.激光三角法在生物医学领域中的若干应用.激光技术,2006,30(1):1-4.
[13]S.R.Addleman.Whole-body 3-D scanner and scan data report.Proc SPIE,1997,3023:2-5.
[14]H.A.Daanen,S.E.Taylor,A.Brunsmanm,et al.Absolute accuracy of cyberware WB4 whole body scanner.Proc SPIE,1997,3023:6-12.
[15]A.Z.Vened Iktov.The laser triangulation meter for a system ofmeasurement of geometrical parameters of prosthesis of a stump,http://webcenter.ru/～iprzhr/jr_e/biomed.html,2004.
[16]R.S.Jin,Y.L.Yao,B.Gao.An experimental study of three-dimensional reconstruction of the molar metal full crown with laser scan.Chinese Journal of Prosthodontics,2003,4(1):47-48(in Chinese).
[17]L.C.Chen,Q.Z.Xu.Development of innovative 3-D dental measurement technique and manpower for tooth model restoration through university-industru collaboration programs,http://www.cc.ntut.edu.tw/～wwwatc/aoi/html2/AO I%20 Introduction.pdf,2004.
[18]L.PAUL,K.B(u|¨)CHNER,R.GAST.Digital documentation of individual human jaw and tooth forms for applications in orthodontics,oral surgery and forensic medicine.Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society.Aachen Germany:Pisacataway IEEE,1998.1098-1102.
[19]B.K.Cha,J.Y.Lee,T.H.Cho.Orthodontic applications using 3-D reverse engineering technique:32Dimensional facial profile evaluation by national dentistry association of korea.http://www.rapid-form.com/success/success_3_2.htm,2004.
[20]J.A.Harr Iso,A.Nixonm,R.Fr Ightw,et al.Use of hand-held laser scanning in the assessment of facial swelling:a preliminary study.British Journal of Oral and Maxillofacial Surgery,2004,42(1):8-17.
[21]J.B.Wu,B.Sang,H.Zhao,et al.3-D measurement system for human head in reverse engineering.Proceedings of 2001 International Conference on Commerce Engineering:New Challenges for Global Manufacturing in the 21st Century.Beijing:China Machine Press,2001.
[22]K.Yamamoto,S.Hayashi,H.Nishikawa,et al.Measurements of dental cast p rofile and three2dimensional tooth movement during orthodontic treatment.IEEE Transaction on Biomedical Engineering,1991,38(4):360-365.
[23]M.J.Araj,J.S.Wayne.Surface aspiration system:a new testing methodology to nondestructively assess themechanical behavior of articular surfaces.Proceeding of 2003 Summer Bioengineering Conference.Florida:IEEE,2003,559-560.
[24]G.Manneberg,S.Hertegard,O.Li Ljencrantz.Measurement of human vocal folds vibrations with laser triangulation.Optical Engineering,2001,40(9):2041-2044.
[25]M.I.Miga,T.K.Sinha,D.M.Cash,et al.Cortical surface registration for image-guided neurosurgery using laser-range scanning.IEEE Transactions on Medical Imaging,2003,22(8):973-985.
[26]D.Mah,M.Stecner,A.Hanlon,et al.MR I simulation:effect of gradient distortions on three-dimensional prostate cancer plans.International Journal of Radiation Ontology Biological Physics,2002,53(3):757-765.
[27]C.MOORE,F.Lilley,V.Sauret,et al.Opto-electronic sensing of body surface topology changes during radiotherapy for rectal cancer.International Journal of Radiation Oncology Biological Physics,2003,56(1):248-258.
[28]周克勤,赵煦,杨苗.基于数字摄影测量技术的三维数字景观生成与应用,北京建筑工程学院学报.2005,21(1):31-34.
[29]林世祥.激光扫描仪结合GPS/IMU在地面三维形状测量中的应用,防护林科技.2006,2.
[30]张国文.激光视觉三维建模技术在煤场自动计量中的应用.江西煤炭科技,2004.3:19-20.
[31]项初庆,吴泽新,单金耀,盛永军.三维坐标测量在隧道施工中的应用,测试技术.2006,2:60-62.
[32]鲁东明,刁常宇,刘德智.数字化及保护国际研讨会.文物数字化技术与应用.第二届中华文化遗产,杭州,2004,27-36.
[33]S.J.Martha,and J.B.Joseph.More Pieces in the Petra Great Temple Puzzle.Bulletin of the American Schools of Oriental Research(BASOR) Number 324,November 2001,43-58.
[34]J.Cosmas,T.Itagaki,D.Green,et al.3D MURALE:A Multimedia System for Archaeology.Proc.VAST2001(Virtual Reality,Archaeology,and Cultural Heritage),ACM Press,297-306.
[35]潘云鹤,鲁东明.古代敦煌壁画的数字化保护与修复.系统仿真学报,2003,15(3):310-314.
[36]姬利艳,周明全,耿国华.基于内容的研究在虚拟文物复原中的应用,计算机工程.2004,30(22):62-63,83.
[37]樊少荣,周明全,姬利艳.考古文物的数字化过程研究.微机发展,2004,14(12):21-23.
[38]庄严.移动机器人基于多传感器数据融合的定位及地图创建研究[博士学位论文].大连:大连理工大学.2004,7.
[39]陈白帆.移动机器人立体视觉三维重建系统的研究与开发[硕士学位论文].湖南长沙:中南大学.
[40]龙志林.数控激光三维建模关键技术研究(硕士学位论文).华中科技大学,2004.
[41]付丽琴,陈树越.反求工程中的实的数据获取技术.华北工学院测试技术学报,2001,15(4):248-252.
[42]任雅萍,杨玉孝,赵明涛等.层析式三维激光测量技术.红外与激光,1997,27(4):226-227.
[43]屈玉福,浦昭邦,王亚爱等.多传感三维运动坐标测量技术研究.光电子·激光,2004,15(9):1091-1094.
[44]王宁玲.立体视觉在非接触三维建模中的研究与应用(硕士学位论文).华北电力大学.
[45]吴晓刚,胡汉平,李德华.三维激光彩色扫描系统的研究.计算机与数字工 程.2000,28(3):47-51.
[46]项志宇.基于激光雷达和移动机器人障碍检测和自定位[博士学位论文].浙江:浙江大学,2002.
[47]http://www.cyro.com.
[48]http://www.sick.com.
[49]J.G.Fryer.Introduction(of photogrammetry).In:K.B.Atkinson(Ed.),Close range photogrammetry and machine vision.Caithness:Whittles.1996,1-8.
[50]Meadows D M,Johnso W O,Allen J B.Generation of surface contours by Moirepatterns.Appl Opt.1970,9(4):942-947.
[51]Jiunn Hshu Lim,Mani Maran Ratnam.Optical signal processing for shape measurement.Sixth International Symposium on Signal Processing and its Applications.Kuala Lumpur,Malaysia.IEEE,2001,2:454-457.
[52]Spagnolo G S,Guattari G,et al.Contouring of artwork surface by range projection and FFT analysis.Opt.Laser Engineering.2000,33(2):141-156.
[53]Marques L,Nunes U,et al.A new 3D optical triangulation sensor for robotics.International Workshop on AMC'98-Coimbra,Portugal,IEEE,1998:512-517.
[54]胡汉平,李德华,金刚等.三维激光彩色扫描系统.高技术通讯,2001,3:25-30.
[55]徐园,张广军,魏振忠.线结构光传感器标定点获取方法研究.北京航空航天大学学报,30(1):9-13.
[56]刘钢,彭群生,鲍虎军.基于图像建模技术研究综述与展望.计算机辅助设计与图形学学报,2005,17(1):18-27.
[57]B.Caprile,V.Torre.Using vanishing points for camera calibration.International Journal of Computer Vision,1990,4(2):127-140.
[58]B.Horn.Height and gradient from shading.International Journal of Computer Vision,1990,5(1):37-75.
[59]B.J.Super,A.C.Bovik.Shape from texture using local spectral moments.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(4):333-343.
[60]S.K.Nayar,Y.Nakagawa.Shape from focus.IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(8):824-831.
[61]L.Zhang,G.Dugas-Phocion,J.S.Samson,et al.Single view modeling of free-form scenes.Journal of Visualization and Computer Animation,2002,13(4):225-235.
[62]U.R.Dhond,J.K.Aggarwal.Structure from stereo-A review.IEEE Transactions on Systems,Man,and Cybernetics,1989,19(6):1489-1510.
[63]D.Scharstein,R.Szeliski.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms.International Journal of Comnuter Vision.2002.47 (1) : 7-42.
[64] O. D. Faugeras. Stratification of 3-D vision: Projective, affine, and metric representations. Journal of the Optical Society of America A, 1995, 12 (3):465-484.
[65] O. D. Faugeras, S. Laveau, L. Robert, et al. 3-D reconstructions of urban scenes from sequence of images. Rocquencourt , France : INRIA , Technical Report 2572 ,1995.
[66] P. Beardsley, A. Zisserman, D. Murray. Sequential updating of projective and affine structure from motion. International Journal of Computer Vision , 1997 ,23(3): 235-259.
[67] W. N. Martin, J. K. Aggarwal. Volumetric descriptions of objects from multiple views. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983 ,5(2): 150-58.
[68] M. Potmesil. Generating octree models of 3D objects from their silhouettes in a sequence of images. Computer Vision, Graphics, and Image Processing, 1987,40(1): 1-29.
[69] G. Liu , R. Wang, H. J. Bao, et al. An algorithm to generate visual hulls. Journal of Software, 2002, 13 ( 9): 1823-1829 (in Chinese).
[70] S. Moezzi, A. Katkere, D. Y. Kuramura, et al. Reality modeling and visualization from multiple video sequences. IEEE Computer Graphics and Applications, 1996, 16 (6): 58-63.
[71] R. Chatila. Mobile robot navigation: Space modeling and decisional processes.Proc. of 3rd International Symposium on Robotics Research, Gouvieux, France,1985, 373-378.
[72] P. Hoppen, T. Knieriemen, and E. von Puttkamer, Laser-radar based mapping and navigation for an autonomous mobile robot. Proc. of the IEEE International Conference on Robotics and Automation, 1990, 2:948-953.
[73] M. Buchberger, K. W. Jorg, and E. von Puttkamer. Laser radar and sonar based world modeling and motion control for fast obstacle avoidance of an autonomous robot. Proc. of the IEEE International Conference on Robotics and Automation, 1993, 1: 534-540.
[74] S. Borthwick, M. Stevens, and H. Durrant-Whyte. Position estimation and tracking using optical range data. Proc. of the IEEEIRSJ International Conference on Intelligent Robots and Systems, 1993, 2172-2177.
[75] P. Jensfelt, Approaches to Mobile Robot Localization in Indoor Environments.PhD thesis, Automatic Control Dept. Royal Institute of Technology, Stockholm,Sweden, 2001.
[76] J. Vandorpe, H. Xu, H. van Brussel, and E. Aertbelien. Positioning of the mobile robot liar using natural landmarks and a 2d rangender. In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Washington DC, 1996,257-264.
[78] S. Atiya, G. D. Hager. Real-time vision-based robot localization. IEEE Trans.On Robotics and Automation, 1993, 9: 785-800.
[79] P. Daniel, M. N. Eduardo and H. Durrant-Whyte. An evidential approach to map-building for autonomous vehicles. IEEE Trans. On Robotics and Automation, 1998, 4: 623-629.
[80] J. Guivant, E. Nebot, and S. Baiker. Localization and map building using laser range sensors in outdoor applications. Journal of Robotic Systems, 2000,1:565-583.
[81] H. P. Moravec. Towards automatic visual obstacle avoidance. Proc. International Joint Conference on Artificial Intelligent, 1977, 584.
[82] C. Harris, M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, 1988, 147-151.
[83] P. R. Beaudet. Rotationally invariant image operators. Internet Joint Conference Pattern Recognition, 1978, 579-583.
[84] L. Kitchen, A. Rosenfeld. Gray-level corner detection. Pattern Recognition Letters, 1982,1:95-102.
[85] H. Wang, M. Brady. Real-time corner detection algorithm for motion estimation.Image and Vision Computing, 1995,13 (9): 695-703
[86] R. Mehrotra, S. Nichanl, N. Ranganathan. Corner detection. Pattern Recognition,1990,23(11): 1,223-1,233.
[87] Dongxiang Zhou, Ganhua Li, Yunhui Liu. Effective corner matching based on DELAUNAY triangulation. The 2004 IEEE International Conference on Robotics and Automation (ICRA 2004) April 26-May 1, 2004. New Orleans,LA, USA, 2730-2736.
[88] K. Kohlmann. Comer detection in natural images based on the 2D Hilbert transform. Signal Processing, 1996, 48(3): 225-234.
[89] C. M. Orange, F. C. A. Groen. Model Based Corner Detection. Proc IEEE Conf.Computer Vision and Pattern Recognition, 1993, 690-691.
[90] K. Rangarajan, M. Shah, V. D. Brackle. Optimal corner detector. Computer Vision, Graphics, and Image Processing, 1989,48(2):230~245.
[91] F. Mokhtarian, R. Suomela. Robust image corner detection through curvature scale space. IEEE Trans. Pattern Analysis and Machine Intelligence, 1998,20(12):1367-1381.
[92] M. Trajkovic, M. Hedley. Fast corner detection. Image and Vision Computing,1998, 16(1):75-87.
[93] S. M. Smith, J. M. Brady. SUSAN - A new approach to low level image processing. Int. Journal of Computer Vision, 1997,23(1): 45-78._____________
[94]彭晓明,张启衡.一种基于小波变换的角点提取方法.光电工程,2002,27(1):18-51.
[95]张坤华,王敬儒,张启衡.多特征复合的角点提取方法.中国图像图形学报,2002,7(4):319-324.
[96]王建琦,邓雁萍,李介谷.一种改进的角点提取方法.上海交通大学学报,2000,34(7):913-916.
[97]M.Sonka,V.Hlavac,and R.Boyle.Image processing.Analysis and Machine Vision.Pacific Grove,CA:PWS,1999.
[98]梁路宏,艾海舟等.基于多模版匹配的单人脸检测.中国图像图形学报,1999,7A(10):825-829.
[99]李赣华,周东翔,刘云辉,蔡宣平.基于DELAUNAY三角化的有效角点匹配算法.信号处理,2007.
[100]R.O.Gorman and M.B.Clowes.Finding picture edges collinearity of feature points.IEEE Trans.Compute,April,1976,25:449-456.
[101]C.W.Kang,R.H.Park,and K.H.Lee.Extraction of straight line segments using rotation trans-formation:Generalized Hough Transformation.Pattern Recognition,1991,24:633-641.
[102]S.A.Dudani and A.L.Luk.Locating straight-line edge segments outdoor scenes.Patten Recognition,1978,10:145-157.
[103]J.B.Burns,A.R.Hanson,and E.M.Riseman.Extracting straight lines.IEEE Trans,Patt.Anal.Machine Intell,July.1986,PAMI-8(4):425-455.
[104]F.Canny.A computational approach to edge detection.IEEE Trans Patt.Anal.Machine Intell,1986,PAMI-8:679-698.
[105]D.Marr,E.Hildreth.Theory of edge detection.Proc.R Soc,Lond,1980,B (207):187-217.
[106]P.Meet,B.Georgescu.Edge detection with embedded confidence.IEEE transactions on pattern analysis and machine intelligence,December 2001,23(12).
[107]C.Harris and M.Stephens.A combined corner and edge detector.In Alvey Vision Conference,1988,147-151.
[108]S.M.Smith and J.M.Brady.SUSAN-A New Approach to Low Level Image Processing.Int.Journal of Computer Vision,May 1997,23(1):45-78.
[109]F.Mokhtarian and R.Suomela.Robust Image Corner Detection through Curvature Scale Space.IEEE Trans.Pattern Analysis and Machine Intelligence,1998,20(12):1367-1381.
[110]Ganhua Li,Xuanpin Cai,Dongxiang Zhou.The distance detection between railway and poles based on straight-line Extraction with distortion and complex background.The IEEE VIP2 2004,November 28-December 2,2004,Macao, China, 239-244.
[111]H. J. E and M. D. Q. Monte Carlo techniques to estimate the conditional expectation in multistage non-linear filtering. International Journal of Control,1969,9: 547-559.
[112]N. Gordon, D. Salmond, and A. F. M. Smith. Novel approach to non-linear and non-Gaussian Bayesian state estimation. Proc. Inst. Elect. Eng., F, 1993, 140:107-113.
[113]A. Doucet, J. F. G. de Freitas, and N. J. Gordon. An introduction to Sequence Monte Carlo methods. in Sequence Monte Carlo Methods in Practice, A.Doucet, J. F. G. de Freitas, and N. J. Gorden, Eds. New York: Springer-Verlag,2001.
[114]J. S. Liu and R. Chen. Sequential Monte Carlo Methods for Dynamic Systems",Journal of the American Statistical Association, 1998, 93(443): 1032-1044.
[115]Ganhua Li, Yunhui Liu, Xuanpin Cai. Efficient target detection from infrared image sequences using the sequential monte carlo method. Proceedings of the IEEE International Conference on Mechatronics and Automation, Luoyang,China, June 25-28, 2006, 549-554.
[116]B. D. O. Anderson and J. B. Moore. Optimal filtering. Prentice Hall,Englewood Cliffs,NJ., 1979
[117]M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density", ECCV96, 1996, 343-356.
[118]M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, February 2002, 50(2): 174—189.
[119]R. Chen, X.Wang, and J. S. Liu. Adaptive Detection in Fading Channels via Monte Carlo Filtering. Proceedings of the 33nd Annual Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, October 24-27,1999,1149-1153.
[120]G. Dudek and C. Zhang. Vision-based robot localization without explicit object models. In Proc. of the International Conference on Robotics & Automation(ICRA), 1996.
[121]S. Thrun, D. Fox, F. Dellaert, and W. Burgard. Particle filters for mobile robot localization. In A. Doucet, N. de Freitas, and N. Gordon, editors, Sequential Monte Carlo Methods in Practice. Springer-Verlag, 2001.
[122]P-J.Nordlund and F. Gustafsson. Sequential Monte Carlo filtering techniques applied to integrated navigation systems. In Proc. Of the American Control Conference, Arlington, Virginia, U.S.A, June 2001.
[123]M. Isard and A. Blake. Condensation-condensational density propagation for Vision tracking. International Journal of Computer Vision, 1998, 29 (1):5-28.
[124]J. M. Hammersley and K. W. Morton. Poor man's Monte Carlo. Journal of the Royal stochastic processes, Automatic, 1954, 6:555-563.
[125]M. N. Rosenbluth and A. W. Rosenbluth. Monte Carlo calculation of the average extension of molecular chains. Journal of Chemical Physics. 23:356-359, 1955.
[126]A. D. Lanterman, M. I. Miller, and D. L. Snyde. Representations of shape for structural inference in infrared scenes. In SPIE Vol. 3069 Automatic Object Reorganization VIII (F. Sadjadi, ed.), April 1997, 257-268.
[127]J. R. Wertmann. A step by step description of a computationally efficient version of multiple hypothesis tracking. SPIE, 1992, 698: 289-230.
[128]David S. K. Chan, Daniel D. Harrison, and A. David. Langan tracking in a high clutter environment: simulation results characterizing a bi-level MHT algorithm.SPIE, Dec. 1999, 1954: 540-551.
[129]V. Vapnik. The nature of statistical learning theory. New York: ringer-verlag,1995.
[130]J. C. Platt. Fast training of support vector machines using sequential minimal optimization. Cambridge, MA: MIT press, Dec. 1999, 185-208.
[131]P. Salembier and J. Serra. Flat zones filtering, connected operators, and filters by reconstruction. IEEE Transactions on Image Processing, 1995, 4(8):1153-1160.
[132]P. Salembier, A. Oliveras, and L. Carrido. Antiextensive connected operators for image and sequence processing. IEEE Transactions on Image Processing,1998, 7(4): 555-570.
[133]L. Vincent. Morphological greyscale reconstruction in image analysis:Applications and efficient algorithms. IEEE Transactions on Image Processing,1993,2(4): 176-201.
[134]贾永红.数字图像处理[M].武汉:武汉大学出版社, 2003, 81-86.
[135]S. Barnard and M. Fischler. Computational stereo. ACM Comput. Surveys, Dec.1982, 14(4): 553-572.
[136]P. Fua. Parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications, 1993, 6: 35-49.
[137]Z. D. Lan and M. Roger. Robust matching by partial correlation. Proceedings of the sixth British Machine Vision Conference. Birmingham, England. September,1995,651-660.
[138]S. Cochran and G. Medioni. 3-D surface description from binocular stereo.IEEE Trans, on PAMI, Oct. 1992, 14(10): 981-994.
[139]C. M. Sun. A Fast Stereo Matching Method. Digital Image Computing and Applications, New Zealand, December 10-12, 1997, 95-100.
[140]C. M. Sun. Multi-Resolution Stereo Matching Using Maximum-Surface techniques. Digital Image Computing: Techniques and Applications, Perth,Australia, December 7-8,1999,195-200.
[141]M. J. Hannah. A system for Digital Stereo Image Matching. Photogrammetric Engineering and Remote Sensing, 1989,1765-1770.
[142]O. Fauugeras et al. Real time correlation-based stereo: algorithm,implementation and applications. Technical Report N2013,1993.
[143]T. Kanade and M. Okutomi. A stereo Matching Algorithm with an Adaptive Window: Theory and Experiments. IEEE Trans. On Pattern Anal. Machine Intell, Sep. 1994,16(9): 920-932.
[144]J. L. Lord and G. Giraudon. Adaptive Window Algorithm for Aerial Image Stereo. In Proceedings of International Conference on Pattern Recognition,1994.
[145]J. L. Lotti and G. Giraudon. Correlation algorithm with adaptive window for aerial image in stereo vision. In European Symposium on Satellite Remote Sensing(EUROPTO), (Rome,Italy), Sept. 1994, 2315-10.
[146]M. Okutomi and T. Kanade. A Multiple-Baseline Stereo. IEEE Trans. On Pattern Anal. Machine Intell, April 1993,15(4): 353-363.
[147]K. Satoh and Y. Ohta. Passive Depth Acquisition for 3D Image Displays. IEICE Trans. On Information and Systems, 1994, E77-D(9): 949-959.
[148]H. K. Nishiara. Real-time implementation of a sign-correlation algorithm for image-matching. (Draft)Teleos Research, February 1990.
[149]L. H. Matthies. Stereo vision for planerary rovers: stochastic modeling to near real-time implementation. International Journal Journal of Computer vision,1992, 8(1):71-91.
[150]K. Yoshida and H. Shigeo. Real-time stereo vision with multiple arrayed camera. Tokyo. Tokyo Institute of Technology,Department of Mechanical Engineering Science,199x.
[151]T. Kanade. Development of a Video-Rate Stereo Machine. Proceedings of 94 ARPA Image Understanding Workshop, Nov 14-16, 1994, Monttey Ca.549-5558.
[152]S. K. Wei. Three-dimensional View Synthesis from Multiple Images. Technical Reports CITR-TR-42, Computer Science Department of the University of Auckland CITR at Tamaki Campus, March 1999.
[153]D. Marr and T. Poggio. A theory of human stereo vision. Tech. Rep. AI Memo 451, Artificial Inell. Lab., Mass. Inst. Technol., Cambridge, Nov. 1977.
[154]W. Grimson and L. Eric. Computational Experiments with a Feature Based Stereo Algorithm. IEEE Trans. On Pattern Anal. Machine Intell., Jan. 1985, 7(1):17-34.
[155]R. D. Arnold. Automated stereo perception. Ph.D. thesis, Stanford Univ.,Sanford, CA, Mar. 1983; Tech. Rep. AIM-351 and STAN-CS-83-961.
[156]Baker H. H. Depth from edge and intensity based stereo. Tech. Rep. AIM-347 and STAN-CS-82-930,Stanford Univ., Stanford, CA, Sept. 1982.
[157]Y. Ohta and T Kanade. Stereo by two-level dynamic programming. IEEE Trans. Part. Annal. Machine Intell., Apr. 1985, 7(2): 139-154.
[158]G. Medioni and R. Nevatia. Segment-based stereo matching. Comput. Vision Graphics Image Processing, 1985, 31:2-18.
[159]S. L. Chou and W. H. Tsai. Line segment matching for 3D computer vision using a new iteration scheme. Machine Vision and Applications, 1993, 6:191-205.
[160]N. Ayache and B. Faverjon. Efficient Registration of Stereo Images by Matching Graph Descriptions of Edge Segments. Internationl Journal of Computer Vision, 1987, 1(2): 107-131.
[161]D. Terzopoulos. The computation of visible surface representation. IEEE Trans.PAMI, July 1988, 10(4): 417-438.
[162]W. Hoff and N. Ahuja. Surfaces from Stereo: Integrating Feature Maching,Disparity Estimation, and Contour Detection. IEEE PAMI, 1989,11(2):121-136.
[163]T. D. Sanger. Stereo Disparity Computations Using Gabor Filter. Biol. Cybern,1988,59:405-418.
[164]M. R. M. Jenkin and A. D. Jepson. Recovering Local Surface Structure through Local Phase Difference Methods. CVGIP, 1994, 59: 72-93.
[165]Maki A.,Uhlin T. and Eklundh J.-O. Phase-Based Disparity Estimation in Binocular Tracking. In Proc. 8th Scandinavian Conference on Image Analysis,1993,1145-1152.
[166]B. Crespi, A. G. Cozzi and L. Raffo, et al. Analog Computation for phase-based disparity estimation:continuous and discrete models. Machine Vision and Applications, 1998, 11: 83-95.
[167]T. Frohlinghaus and J. M. Buhmann. Regularizing Phase-Based Stereo. in International Conference on Pattern Recognition(ICPR'96), 1996, 451-455.
[168]T. Frohlinghaus and J. M. Buhmann. Real-Time Phase-Based Stereo for a Mobile Robot. In Proceedings of the First Euromicro Workshop on Advanced Mobile Robots, 1996, 178-185.
[169]D. J. Fleet. Disparity from Local Weighted Phase-Correlation. IEEE International Conference on SMC,San Antonio, Oct. 1994, 48-56.
[170]S. Birchfield and C. Tomasi. Depth Discontinuities by Pixel-to-Pixel Stereo.Proc. Of the 1998 IEEE International Conference on Computer Vision,Bombay,India.
[171]C. Tomasi and R. Manduchi. Stereo Without Search. European Conference on Computer Vision (ECCV) 1996.
[172]H. Farid and E. P. Simoncelli. Range Estimation by Optical Differentiation.Journal of the Optical Society of American, 1998.
[173]D. Scharstein and R. Szeliski. Stereo Matching with Nonlinear Diffusion. International Journal of Computer Vision, 1998,28(2): 155-174.
[174]H. H. Baker and T. O. Binford. Depth from edge and intensity based stereo. In Proceeding Seventh International Joint Conference Artificial Intelligent, Aug.1981,631-636.
[175]Y. Ohta, T. Kanade. Stereo by intra- and inter-scanline search using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence,1985, 7(2): 139-154.
[176]G. Medioni and R. Nevatia. Segment Based Stereo Matching. Computer Vision,Graphics and Image Processing, 1985, 31(1): 2-18.
[177]A. T. Brint and M. Brady. Stereo matching of curves. Image and Vision Computing, 1990, 8(1): 50-56.
[178]O. Faugeras, et al. Real time correlation based stereo: algorithm implementations and applications. INRIA, Technical Report. RR2013, 1993
[179]P. Fua. A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications, Winter 1993, 6(1):35-49. Available as INRIA research report 1369.
[180]J. J. Weng. Image matching using the windowed fourier phase. International Journal of Computer Vision, 1993, 11: 211-239.
[181]D. Comaniciu and P. Meer. Mean Shift: A robust approach toward feature space analysis. IEEE Transaction on PAMI, 2002, 24(5): 603-619.
[182]L. R. Rabiner and R. W. Schafter. Digital processing of speech signals.Published by Prentice Hall, United States, 1978.
[183]D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. Working Notes of the Knowledge Discovery in Databases Workshop,1994, 359-370.
[184]K. Muhlmann, et al. Calculating dense disparity maps from color stereo images:an Efficient Implementation. International Journal of Computer Vision, 2002,47(1-3): 79-88.
[185]A. Koschan. Chromatic block matching for dense stereo correspondence.Proceeding 7th International Conference on Image Analysis and Processing 7ICIAP, Capitolo, Monopoly, Italy, 1993, 641-648.
[186]Z. R. Chi, H. Yan and T. Pham. Fuzzy algorithms: with application to image processing and pattern recognition. World Scientific Publishing Co. Pte. Ltd.Singapore, 1996.____________________________________________________
[187]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):17-22.
[188]J. Martin and J. L. Crowley. Comparison of correlation techniques. In Intelligent Autonomous Systems, 1995, 86-93.
[189]L. S. Shapiro, H. Wang and J.M. Brady. A matching and tracking strategy for independently-moving, non-rigid objects. In Proc. 3rd British Machine Vision Conference, Springer Verlag, Sep. 1992, 306-315.
[190]X. Hu and N. Ahuja. Matching point features with ordered geometric, rigidity,and disparity constraints. PAMI, 1994, 16(10): 1041-1049.
[191]R. Deriche, Z. Zhang, Q.-T. Luong and O. Faugeras. Robust recovery of the epipolar geometry for an uncalibrated stereo rigion. ECCV, 1994, 567-576.
[192]Z. Zhang, R. Deriche, O. Faugeras and Q.-T. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Technical Report 2273, INRIA, May 1994.
[193]P. Long Limozin and G Giraudon. Stereo matching based on contextual line-region primitives. In Proceedings of the 8th International Conference on Pattern Recognition, Paris, France, 1986, 974-977.
[194]R. Horaud and T. Skordas. Stereo correspondence through feature grouping and maximal cliques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(11): 1168-1180.
[195]B. B. Hansen and B. S. Morse. Multiscale image registration using scale trace correlation. In CVPR, 1999, 202-208.
[196]X. Hu and N. Ahuja. Feature extraction and matching as signal detection.International Journal of Pattern Recognition and Artiffcial Intelligence, 1994,8(6): 1343-1379.
[197]D. H. Ballard and L. E. Wixson. Object recognition using steerable filters at multiple scales. In IEEE Workshop on Qualitative Vision, 1993, 2-10.
[198]Y. Dufournaud, C. Schmid, and R. Horaud, Matching images with different resolutions, In CVPR, 2000, 612-618.
[199]T. Lindeberg. Scale-Space Theory in Computer Vision. Kluwer Academic Publishers, 1994.
[200]T. Lindeberg. Feature detection with automatic scale selection. IJCV, 1998,30(2):79-116.
[201]C. Schmid and R. Mohr. Local grayvalue invariants for image and Machine Vision. Pacific Grove, CA: PWS, 1999.
[202]D. G. Lowe. Object recognition from local scale-invariant features. In Proc. of the International Conference on Computer Vision, 1999, 1150-1157.
[203]Ganhua Li, Dongxiang Zhou, Yunhui Liu. View synthesis based on texture mapping.IEEE 5th World Congress on Intelligent Control and Automation (WCICA 2004) No.A51,June 14-18,2004,3911-3917.
[204]R.Mehrotra,S.Nichani,and N.Ranganathan.Comer detection.Pattern Recognition,1990,23:1223-1233.
[205]E.Shilat,M.Werman,and Y.Gdalyahu.Ridge's comer detection and correspondence.In IEEE Conference on Computer Vision and Pattern Recognition,1997,976-981.
[206]H.Wang and M.Brady.Real-time coner detection algorithm for motion estimation.Image and Vision Computing,1995,13(9):695-703.
[207]C.Harris and M.Stephens.A combined comer and edge detector.In Alvey Vision Conference,1988,147-151.
[208]N.Georgis,M.Petrou,and J.Kittler.On the correspondence problem for wide angular separation of non-coplanar points.Image and Vision Computing,1998,16:35-41.
[209]S.M.Smith and J.M.Brady.SUSAN-A new approach to low level image processing.Int.Journal of Computer Vision,May 1997,23(1):45-78.
[210]S.Nassif,D.Capson and A.Vaz.Robust real-time comer location measurement.IEEE Instrumentation and Measurement Technology Conference,1997,1:106-111.
[211]X.H.ZHANG,et al.Techniques for modeling and rendering a video image-based virtual environment for driving simulation system.International Conference on Artificial Reality and Telexistence,Tokyo,Japan,1998,63-70.
[212]何友,王国宏,陆大多金,彭应宁.多传感器信息融合及应用.北京:电子工业出版社,2000.
[213]何友,唐劲松,王国宏.多雷达综合跟踪.电子科学学刊,1996(3):225-229.
[214]A.Farina,S.Pardini.Multiple platform sensor integration model:MULSIM Computer Program.ADA 079951,Naval Research Laboratory,31,Dec.1999:1-61.
[215]孙长库,叶声华.激光测量技术.天津:天津大学出版社,2001.
[216]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.
[217]Y.Shrai and M.Suwa.Recognition of polyhedrons with a range finder.In International Joint Conferrence on Artificial Intelligence,1971:80-87.
[218]G.J.Agin and T.O.Binford.Computer description of curved objects.In Internetional Joint Conferrence on Artificial Intelligence,1973:629-630.
[219]C.H.Chen and A.C.Kak.Modeling and calibration of a structure light scanner for 3-d robot vision.In Proceedings of the IEEE Conference on Robotics and Automation,1987:807-815.
[220]O. Faugeras. Three-dimentional computer vision: A Geometric Viewpoint. The MIT Press, 1993.
[221]R. Hartley and A. Zisserman. Multiple view geometry. Cambridge University Press, 2000.
[222]于金霞,蔡自兴,邹小兵,段琢华.基于动态自适应滤波的移动机器人障碍检测.自然科学进展, 2006. 16(5): 618-624.
[223]K. R. Beevers, W. H. Huang. SLAM With Sparse Sensing. In IEEE Intenetinal Confonrence on Robotics and Automation. 2006: 2285-2290.
[224]Y. Liu, R. Emery, D. Chakrabarti, W. Bulgard, and S. Thrun. Using EM to Learn 3D Models of Indoor Environments with Mobile Robots. Intenetinal Confonrence Machine Learning, June 28-July 1, 2001:329-336.
[225]V. Brajovic, K. Mori, and N. Jankovic.100 Frames/s cmos range image sensor.Digest of Technical Papers, 2001 IEEE International Solid-State Circuits Conference, February 2001, 256-257 and 453.
[226]L. D. Reid. Projective calibration of a laser-stripe range finder. Image and Vision Computing, October 1996, 14(9): 659-666.
[227]H. Baltzakis, A. Argyros, and P. Trahanias. Fusion of laser and visual data for robot motion planning and collision avoidance. International Journal of Machine Vision and Applications, 2003, 15(2): 92-100.
[228]J. Forest, J. Salvi. A review of laser scanning three-dimensional digitizers. In IEEE/RSJ International Conference on Intelligent Robots and System. EPFL Lausanne, Switzerland, 2002, 1: 73-78.
[229]J. Davis and X. Chen. A laser range scanner designed for minimum calibration complexity. In 2001 IEEE Third International Conference on 3D Digital Imaging and Modeling, Canada, 2001, 91-98.
[230]Q. L. Zhang, R. Pless. Extrinsic calibration of a camera and laser range finder (improves camera calibration). In 2004 IEEE/RSJ International Conference on Intelligent Robots and systems, Sendai, Japan, 2004, 2301-2306.
[231]T. Hong, R. Bostelman, and R. Madhavan. Obstacle detection using a TOF range camera for indoor AGV. Navigation, PerMIS 2004, Gaithersburg, MD,June 2004.
[232]I. Bauermann, E. Steinbach. Joint calibration of a range and visual sensor for the acquisition of RGBZ concentric mosaics. VMV2005, November 2005.
[233]S. Wasielewski, and O. Strauss. Calibration of a multi-sensor system laser rangefinder/camera. Proceedings of the Intelligent Vehicles '95 Symposium,Sep. 25-Sep. 26, 1995, Detroit, USA Sponsored by IEEE Industrial Electronics Society, 1995,472-477.
[234]G. Q. Wei, and S. D. Ma. Implicit and explicit camera calibration: theory and experiments.IEEE Transaction Pattern Analysis and Machine Intelligence,1994,16(5):169-180.
[235]J.Stoer,and R.Burlisch.Introduction to Numerical Analysis.Springer Verlag New York Inc,1980.
[236]C.Mei,and I.Sophia-Antipolis.Camera calibration toolbox for matlab.Available:http://www.vision.caltech.edu/bouguetj/ealib_doc/index.html.
[237]H.Wang.The calculation of the distance between beeline and dot.Journal of Shangluo Teachers College of China,June 2005,19(2):16-19,121.
[238]H.Wang.The calculation of the distance between beeline and dot.Journal of Shangluo Teachers College of China,June 2005,19(2):16-19,121.
[238]许智钦,孙长库,陶立,郑义忠.彩色三维激光扫描测量方法的研究.光学学报,2003,23(8):1008-1012.
[239]张宗华,彭翔,胡小唐.一种新型彩色三维光学成像系统.光学学报.2002,22(8):994-998.
[240]胡汉平,李德华,金刚,王祖喜.三维激光彩色扫描系统.高技术通讯,2001,3:25-30.
[241]范海英.Cyra三维激光扫描系统在精准林业中的应用研究.硕士论文,辽宁工程技术大学.
[2]熊有伦.数字装备与数字制造.中国国际机电产品博览会组委会,2003.
[3]徐智钦,孙长库.3D逆向工程技术.中国计量出版社,2002.
[4]叶声华,邾继贵等.视觉检测技术及应用.中国工程科学,1999,1(1):262-263.
[5]黄桂平,毋新房.经纬仪大尺寸三坐标测量系统MetroIn用于长河闸门的安装与测试.计量技术,2002,10:8-12.
[6]张顺德,魏正英,卢庚恒.逆向工程技术及其应用.甘肃工业大学学报,2001,27(2):25-28.
[7]谢华锟.现代精密量仪现状及水平—从近几界中国国际机床展览会看量具量仪发展趋势.现代切削与测量工程(国际)研讨会,2004,10.
[8]张国雄,裘祖荣.测试技术的作用与发展方向.现代切削与测量工程(国际)研讨会,2004,10.
[9]黄桂平.数字近景工业摄影测量关键技术研究与应用[博士学位论文].天津:天津大学精密仪器与光电子工程学院,2005,5.
[10]李燕,谭鸥,段会龙.三维医学图象可视化技术综述.中国图象图形学报,2001,6(2):103-110.
[11]E.Coche,S.Pawlak,S.Dechambre,et al.Peripheral pulmonary arteries:identification at multi-slice spiral CT with 3D reconstructions.Eur Radiol.2003,13(4):815-822.
[12]吴剑波,骆清铭.激光三角法在生物医学领域中的若干应用.激光技术,2006,30(1):1-4.
[13]S.R.Addleman.Whole-body 3-D scanner and scan data report.Proc SPIE,1997,3023:2-5.
[14]H.A.Daanen,S.E.Taylor,A.Brunsmanm,et al.Absolute accuracy of cyberware WB4 whole body scanner.Proc SPIE,1997,3023:6-12.
[15]A.Z.Vened Iktov.The laser triangulation meter for a system ofmeasurement of geometrical parameters of prosthesis of a stump,http://webcenter.ru/～iprzhr/jr_e/biomed.html,2004.
[16]R.S.Jin,Y.L.Yao,B.Gao.An experimental study of three-dimensional reconstruction of the molar metal full crown with laser scan.Chinese Journal of Prosthodontics,2003,4(1):47-48(in Chinese).
[17]L.C.Chen,Q.Z.Xu.Development of innovative 3-D dental measurement technique and manpower for tooth model restoration through university-industru collaboration programs,http://www.cc.ntut.edu.tw/～wwwatc/aoi/html2/AO I%20 Introduction.pdf,2004.
[18]L.PAUL,K.B(u|¨)CHNER,R.GAST.Digital documentation of individual human jaw and tooth forms for applications in orthodontics,oral surgery and forensic medicine.Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society.Aachen Germany:Pisacataway IEEE,1998.1098-1102.
[19]B.K.Cha,J.Y.Lee,T.H.Cho.Orthodontic applications using 3-D reverse engineering technique:32Dimensional facial profile evaluation by national dentistry association of korea.http://www.rapid-form.com/success/success_3_2.htm,2004.
[20]J.A.Harr Iso,A.Nixonm,R.Fr Ightw,et al.Use of hand-held laser scanning in the assessment of facial swelling:a preliminary study.British Journal of Oral and Maxillofacial Surgery,2004,42(1):8-17.
[21]J.B.Wu,B.Sang,H.Zhao,et al.3-D measurement system for human head in reverse engineering.Proceedings of 2001 International Conference on Commerce Engineering:New Challenges for Global Manufacturing in the 21st Century.Beijing:China Machine Press,2001.
[22]K.Yamamoto,S.Hayashi,H.Nishikawa,et al.Measurements of dental cast p rofile and three2dimensional tooth movement during orthodontic treatment.IEEE Transaction on Biomedical Engineering,1991,38(4):360-365.
[23]M.J.Araj,J.S.Wayne.Surface aspiration system:a new testing methodology to nondestructively assess themechanical behavior of articular surfaces.Proceeding of 2003 Summer Bioengineering Conference.Florida:IEEE,2003,559-560.
[24]G.Manneberg,S.Hertegard,O.Li Ljencrantz.Measurement of human vocal folds vibrations with laser triangulation.Optical Engineering,2001,40(9):2041-2044.
[25]M.I.Miga,T.K.Sinha,D.M.Cash,et al.Cortical surface registration for image-guided neurosurgery using laser-range scanning.IEEE Transactions on Medical Imaging,2003,22(8):973-985.
[26]D.Mah,M.Stecner,A.Hanlon,et al.MR I simulation:effect of gradient distortions on three-dimensional prostate cancer plans.International Journal of Radiation Ontology Biological Physics,2002,53(3):757-765.
[27]C.MOORE,F.Lilley,V.Sauret,et al.Opto-electronic sensing of body surface topology changes during radiotherapy for rectal cancer.International Journal of Radiation Oncology Biological Physics,2003,56(1):248-258.
[28]周克勤,赵煦,杨苗.基于数字摄影测量技术的三维数字景观生成与应用,北京建筑工程学院学报.2005,21(1):31-34.
[29]林世祥.激光扫描仪结合GPS/IMU在地面三维形状测量中的应用,防护林科技.2006,2.
[30]张国文.激光视觉三维建模技术在煤场自动计量中的应用.江西煤炭科技,2004.3:19-20.
[31]项初庆,吴泽新,单金耀,盛永军.三维坐标测量在隧道施工中的应用,测试技术.2006,2:60-62.
[32]鲁东明,刁常宇,刘德智.数字化及保护国际研讨会.文物数字化技术与应用.第二届中华文化遗产,杭州,2004,27-36.
[33]S.J.Martha,and J.B.Joseph.More Pieces in the Petra Great Temple Puzzle.Bulletin of the American Schools of Oriental Research(BASOR) Number 324,November 2001,43-58.
[34]J.Cosmas,T.Itagaki,D.Green,et al.3D MURALE:A Multimedia System for Archaeology.Proc.VAST2001(Virtual Reality,Archaeology,and Cultural Heritage),ACM Press,297-306.
[35]潘云鹤,鲁东明.古代敦煌壁画的数字化保护与修复.系统仿真学报,2003,15(3):310-314.
[36]姬利艳,周明全,耿国华.基于内容的研究在虚拟文物复原中的应用,计算机工程.2004,30(22):62-63,83.
[37]樊少荣,周明全,姬利艳.考古文物的数字化过程研究.微机发展,2004,14(12):21-23.
[38]庄严.移动机器人基于多传感器数据融合的定位及地图创建研究[博士学位论文].大连:大连理工大学.2004,7.
[39]陈白帆.移动机器人立体视觉三维重建系统的研究与开发[硕士学位论文].湖南长沙:中南大学.
[40]龙志林.数控激光三维建模关键技术研究(硕士学位论文).华中科技大学,2004.
[41]付丽琴,陈树越.反求工程中的实的数据获取技术.华北工学院测试技术学报,2001,15(4):248-252.
[42]任雅萍,杨玉孝,赵明涛等.层析式三维激光测量技术.红外与激光,1997,27(4):226-227.
[43]屈玉福,浦昭邦,王亚爱等.多传感三维运动坐标测量技术研究.光电子·激光,2004,15(9):1091-1094.
[44]王宁玲.立体视觉在非接触三维建模中的研究与应用(硕士学位论文).华北电力大学.
[45]吴晓刚,胡汉平,李德华.三维激光彩色扫描系统的研究.计算机与数字工 程.2000,28(3):47-51.
[46]项志宇.基于激光雷达和移动机器人障碍检测和自定位[博士学位论文].浙江:浙江大学,2002.
[47]http://www.cyro.com.
[48]http://www.sick.com.
[49]J.G.Fryer.Introduction(of photogrammetry).In:K.B.Atkinson(Ed.),Close range photogrammetry and machine vision.Caithness:Whittles.1996,1-8.
[50]Meadows D M,Johnso W O,Allen J B.Generation of surface contours by Moirepatterns.Appl Opt.1970,9(4):942-947.
[51]Jiunn Hshu Lim,Mani Maran Ratnam.Optical signal processing for shape measurement.Sixth International Symposium on Signal Processing and its Applications.Kuala Lumpur,Malaysia.IEEE,2001,2:454-457.
[52]Spagnolo G S,Guattari G,et al.Contouring of artwork surface by range projection and FFT analysis.Opt.Laser Engineering.2000,33(2):141-156.
[53]Marques L,Nunes U,et al.A new 3D optical triangulation sensor for robotics.International Workshop on AMC'98-Coimbra,Portugal,IEEE,1998:512-517.
[54]胡汉平,李德华,金刚等.三维激光彩色扫描系统.高技术通讯,2001,3:25-30.
[55]徐园,张广军,魏振忠.线结构光传感器标定点获取方法研究.北京航空航天大学学报,30(1):9-13.
[56]刘钢,彭群生,鲍虎军.基于图像建模技术研究综述与展望.计算机辅助设计与图形学学报,2005,17(1):18-27.
[57]B.Caprile,V.Torre.Using vanishing points for camera calibration.International Journal of Computer Vision,1990,4(2):127-140.
[58]B.Horn.Height and gradient from shading.International Journal of Computer Vision,1990,5(1):37-75.
[59]B.J.Super,A.C.Bovik.Shape from texture using local spectral moments.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(4):333-343.
[60]S.K.Nayar,Y.Nakagawa.Shape from focus.IEEE Transactions on Pattern Analysis and Machine Intelligence,1994,16(8):824-831.
[61]L.Zhang,G.Dugas-Phocion,J.S.Samson,et al.Single view modeling of free-form scenes.Journal of Visualization and Computer Animation,2002,13(4):225-235.
[62]U.R.Dhond,J.K.Aggarwal.Structure from stereo-A review.IEEE Transactions on Systems,Man,and Cybernetics,1989,19(6):1489-1510.
[63]D.Scharstein,R.Szeliski.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms.International Journal of Comnuter Vision.2002.47 (1) : 7-42.
[64] O. D. Faugeras. Stratification of 3-D vision: Projective, affine, and metric representations. Journal of the Optical Society of America A, 1995, 12 (3):465-484.
[65] O. D. Faugeras, S. Laveau, L. Robert, et al. 3-D reconstructions of urban scenes from sequence of images. Rocquencourt , France : INRIA , Technical Report 2572 ,1995.
[66] P. Beardsley, A. Zisserman, D. Murray. Sequential updating of projective and affine structure from motion. International Journal of Computer Vision , 1997 ,23(3): 235-259.
[67] W. N. Martin, J. K. Aggarwal. Volumetric descriptions of objects from multiple views. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983 ,5(2): 150-58.
[68] M. Potmesil. Generating octree models of 3D objects from their silhouettes in a sequence of images. Computer Vision, Graphics, and Image Processing, 1987,40(1): 1-29.
[69] G. Liu , R. Wang, H. J. Bao, et al. An algorithm to generate visual hulls. Journal of Software, 2002, 13 ( 9): 1823-1829 (in Chinese).
[70] S. Moezzi, A. Katkere, D. Y. Kuramura, et al. Reality modeling and visualization from multiple video sequences. IEEE Computer Graphics and Applications, 1996, 16 (6): 58-63.
[71] R. Chatila. Mobile robot navigation: Space modeling and decisional processes.Proc. of 3rd International Symposium on Robotics Research, Gouvieux, France,1985, 373-378.
[72] P. Hoppen, T. Knieriemen, and E. von Puttkamer, Laser-radar based mapping and navigation for an autonomous mobile robot. Proc. of the IEEE International Conference on Robotics and Automation, 1990, 2:948-953.
[73] M. Buchberger, K. W. Jorg, and E. von Puttkamer. Laser radar and sonar based world modeling and motion control for fast obstacle avoidance of an autonomous robot. Proc. of the IEEE International Conference on Robotics and Automation, 1993, 1: 534-540.
[74] S. Borthwick, M. Stevens, and H. Durrant-Whyte. Position estimation and tracking using optical range data. Proc. of the IEEEIRSJ International Conference on Intelligent Robots and Systems, 1993, 2172-2177.
[75] P. Jensfelt, Approaches to Mobile Robot Localization in Indoor Environments.PhD thesis, Automatic Control Dept. Royal Institute of Technology, Stockholm,Sweden, 2001.
[76] J. Vandorpe, H. Xu, H. van Brussel, and E. Aertbelien. Positioning of the mobile robot liar using natural landmarks and a 2d rangender. In Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, Washington DC, 1996,257-264.
[78] S. Atiya, G. D. Hager. Real-time vision-based robot localization. IEEE Trans.On Robotics and Automation, 1993, 9: 785-800.
[79] P. Daniel, M. N. Eduardo and H. Durrant-Whyte. An evidential approach to map-building for autonomous vehicles. IEEE Trans. On Robotics and Automation, 1998, 4: 623-629.
[80] J. Guivant, E. Nebot, and S. Baiker. Localization and map building using laser range sensors in outdoor applications. Journal of Robotic Systems, 2000,1:565-583.
[81] H. P. Moravec. Towards automatic visual obstacle avoidance. Proc. International Joint Conference on Artificial Intelligent, 1977, 584.
[82] C. Harris, M. Stephens. A combined corner and edge detector. In Alvey Vision Conference, 1988, 147-151.
[83] P. R. Beaudet. Rotationally invariant image operators. Internet Joint Conference Pattern Recognition, 1978, 579-583.
[84] L. Kitchen, A. Rosenfeld. Gray-level corner detection. Pattern Recognition Letters, 1982,1:95-102.
[85] H. Wang, M. Brady. Real-time corner detection algorithm for motion estimation.Image and Vision Computing, 1995,13 (9): 695-703
[86] R. Mehrotra, S. Nichanl, N. Ranganathan. Corner detection. Pattern Recognition,1990,23(11): 1,223-1,233.
[87] Dongxiang Zhou, Ganhua Li, Yunhui Liu. Effective corner matching based on DELAUNAY triangulation. The 2004 IEEE International Conference on Robotics and Automation (ICRA 2004) April 26-May 1, 2004. New Orleans,LA, USA, 2730-2736.
[88] K. Kohlmann. Comer detection in natural images based on the 2D Hilbert transform. Signal Processing, 1996, 48(3): 225-234.
[89] C. M. Orange, F. C. A. Groen. Model Based Corner Detection. Proc IEEE Conf.Computer Vision and Pattern Recognition, 1993, 690-691.
[90] K. Rangarajan, M. Shah, V. D. Brackle. Optimal corner detector. Computer Vision, Graphics, and Image Processing, 1989,48(2):230~245.
[91] F. Mokhtarian, R. Suomela. Robust image corner detection through curvature scale space. IEEE Trans. Pattern Analysis and Machine Intelligence, 1998,20(12):1367-1381.
[92] M. Trajkovic, M. Hedley. Fast corner detection. Image and Vision Computing,1998, 16(1):75-87.
[93] S. M. Smith, J. M. Brady. SUSAN - A new approach to low level image processing. Int. Journal of Computer Vision, 1997,23(1): 45-78._____________
[94]彭晓明,张启衡.一种基于小波变换的角点提取方法.光电工程,2002,27(1):18-51.
[95]张坤华,王敬儒,张启衡.多特征复合的角点提取方法.中国图像图形学报,2002,7(4):319-324.
[96]王建琦,邓雁萍,李介谷.一种改进的角点提取方法.上海交通大学学报,2000,34(7):913-916.
[97]M.Sonka,V.Hlavac,and R.Boyle.Image processing.Analysis and Machine Vision.Pacific Grove,CA:PWS,1999.
[98]梁路宏,艾海舟等.基于多模版匹配的单人脸检测.中国图像图形学报,1999,7A(10):825-829.
[99]李赣华,周东翔,刘云辉,蔡宣平.基于DELAUNAY三角化的有效角点匹配算法.信号处理,2007.
[100]R.O.Gorman and M.B.Clowes.Finding picture edges collinearity of feature points.IEEE Trans.Compute,April,1976,25:449-456.
[101]C.W.Kang,R.H.Park,and K.H.Lee.Extraction of straight line segments using rotation trans-formation:Generalized Hough Transformation.Pattern Recognition,1991,24:633-641.
[102]S.A.Dudani and A.L.Luk.Locating straight-line edge segments outdoor scenes.Patten Recognition,1978,10:145-157.
[103]J.B.Burns,A.R.Hanson,and E.M.Riseman.Extracting straight lines.IEEE Trans,Patt.Anal.Machine Intell,July.1986,PAMI-8(4):425-455.
[104]F.Canny.A computational approach to edge detection.IEEE Trans Patt.Anal.Machine Intell,1986,PAMI-8:679-698.
[105]D.Marr,E.Hildreth.Theory of edge detection.Proc.R Soc,Lond,1980,B (207):187-217.
[106]P.Meet,B.Georgescu.Edge detection with embedded confidence.IEEE transactions on pattern analysis and machine intelligence,December 2001,23(12).
[107]C.Harris and M.Stephens.A combined corner and edge detector.In Alvey Vision Conference,1988,147-151.
[108]S.M.Smith and J.M.Brady.SUSAN-A New Approach to Low Level Image Processing.Int.Journal of Computer Vision,May 1997,23(1):45-78.
[109]F.Mokhtarian and R.Suomela.Robust Image Corner Detection through Curvature Scale Space.IEEE Trans.Pattern Analysis and Machine Intelligence,1998,20(12):1367-1381.
[110]Ganhua Li,Xuanpin Cai,Dongxiang Zhou.The distance detection between railway and poles based on straight-line Extraction with distortion and complex background.The IEEE VIP2 2004,November 28-December 2,2004,Macao, China, 239-244.
[111]H. J. E and M. D. Q. Monte Carlo techniques to estimate the conditional expectation in multistage non-linear filtering. International Journal of Control,1969,9: 547-559.
[112]N. Gordon, D. Salmond, and A. F. M. Smith. Novel approach to non-linear and non-Gaussian Bayesian state estimation. Proc. Inst. Elect. Eng., F, 1993, 140:107-113.
[113]A. Doucet, J. F. G. de Freitas, and N. J. Gordon. An introduction to Sequence Monte Carlo methods. in Sequence Monte Carlo Methods in Practice, A.Doucet, J. F. G. de Freitas, and N. J. Gorden, Eds. New York: Springer-Verlag,2001.
[114]J. S. Liu and R. Chen. Sequential Monte Carlo Methods for Dynamic Systems",Journal of the American Statistical Association, 1998, 93(443): 1032-1044.
[115]Ganhua Li, Yunhui Liu, Xuanpin Cai. Efficient target detection from infrared image sequences using the sequential monte carlo method. Proceedings of the IEEE International Conference on Mechatronics and Automation, Luoyang,China, June 25-28, 2006, 549-554.
[116]B. D. O. Anderson and J. B. Moore. Optimal filtering. Prentice Hall,Englewood Cliffs,NJ., 1979
[117]M. Isard and A. Blake. Contour tracking by stochastic propagation of conditional density", ECCV96, 1996, 343-356.
[118]M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking. IEEE Transactions on Signal Processing, February 2002, 50(2): 174—189.
[119]R. Chen, X.Wang, and J. S. Liu. Adaptive Detection in Fading Channels via Monte Carlo Filtering. Proceedings of the 33nd Annual Asilomar Conference on Signals, Systems and Computers, Pacific Grove, California, October 24-27,1999,1149-1153.
[120]G. Dudek and C. Zhang. Vision-based robot localization without explicit object models. In Proc. of the International Conference on Robotics & Automation(ICRA), 1996.
[121]S. Thrun, D. Fox, F. Dellaert, and W. Burgard. Particle filters for mobile robot localization. In A. Doucet, N. de Freitas, and N. Gordon, editors, Sequential Monte Carlo Methods in Practice. Springer-Verlag, 2001.
[122]P-J.Nordlund and F. Gustafsson. Sequential Monte Carlo filtering techniques applied to integrated navigation systems. In Proc. Of the American Control Conference, Arlington, Virginia, U.S.A, June 2001.
[123]M. Isard and A. Blake. Condensation-condensational density propagation for Vision tracking. International Journal of Computer Vision, 1998, 29 (1):5-28.
[124]J. M. Hammersley and K. W. Morton. Poor man's Monte Carlo. Journal of the Royal stochastic processes, Automatic, 1954, 6:555-563.
[125]M. N. Rosenbluth and A. W. Rosenbluth. Monte Carlo calculation of the average extension of molecular chains. Journal of Chemical Physics. 23:356-359, 1955.
[126]A. D. Lanterman, M. I. Miller, and D. L. Snyde. Representations of shape for structural inference in infrared scenes. In SPIE Vol. 3069 Automatic Object Reorganization VIII (F. Sadjadi, ed.), April 1997, 257-268.
[127]J. R. Wertmann. A step by step description of a computationally efficient version of multiple hypothesis tracking. SPIE, 1992, 698: 289-230.
[128]David S. K. Chan, Daniel D. Harrison, and A. David. Langan tracking in a high clutter environment: simulation results characterizing a bi-level MHT algorithm.SPIE, Dec. 1999, 1954: 540-551.
[129]V. Vapnik. The nature of statistical learning theory. New York: ringer-verlag,1995.
[130]J. C. Platt. Fast training of support vector machines using sequential minimal optimization. Cambridge, MA: MIT press, Dec. 1999, 185-208.
[131]P. Salembier and J. Serra. Flat zones filtering, connected operators, and filters by reconstruction. IEEE Transactions on Image Processing, 1995, 4(8):1153-1160.
[132]P. Salembier, A. Oliveras, and L. Carrido. Antiextensive connected operators for image and sequence processing. IEEE Transactions on Image Processing,1998, 7(4): 555-570.
[133]L. Vincent. Morphological greyscale reconstruction in image analysis:Applications and efficient algorithms. IEEE Transactions on Image Processing,1993,2(4): 176-201.
[134]贾永红.数字图像处理[M].武汉:武汉大学出版社, 2003, 81-86.
[135]S. Barnard and M. Fischler. Computational stereo. ACM Comput. Surveys, Dec.1982, 14(4): 553-572.
[136]P. Fua. Parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications, 1993, 6: 35-49.
[137]Z. D. Lan and M. Roger. Robust matching by partial correlation. Proceedings of the sixth British Machine Vision Conference. Birmingham, England. September,1995,651-660.
[138]S. Cochran and G. Medioni. 3-D surface description from binocular stereo.IEEE Trans, on PAMI, Oct. 1992, 14(10): 981-994.
[139]C. M. Sun. A Fast Stereo Matching Method. Digital Image Computing and Applications, New Zealand, December 10-12, 1997, 95-100.
[140]C. M. Sun. Multi-Resolution Stereo Matching Using Maximum-Surface techniques. Digital Image Computing: Techniques and Applications, Perth,Australia, December 7-8,1999,195-200.
[141]M. J. Hannah. A system for Digital Stereo Image Matching. Photogrammetric Engineering and Remote Sensing, 1989,1765-1770.
[142]O. Fauugeras et al. Real time correlation-based stereo: algorithm,implementation and applications. Technical Report N2013,1993.
[143]T. Kanade and M. Okutomi. A stereo Matching Algorithm with an Adaptive Window: Theory and Experiments. IEEE Trans. On Pattern Anal. Machine Intell, Sep. 1994,16(9): 920-932.
[144]J. L. Lord and G. Giraudon. Adaptive Window Algorithm for Aerial Image Stereo. In Proceedings of International Conference on Pattern Recognition,1994.
[145]J. L. Lotti and G. Giraudon. Correlation algorithm with adaptive window for aerial image in stereo vision. In European Symposium on Satellite Remote Sensing(EUROPTO), (Rome,Italy), Sept. 1994, 2315-10.
[146]M. Okutomi and T. Kanade. A Multiple-Baseline Stereo. IEEE Trans. On Pattern Anal. Machine Intell, April 1993,15(4): 353-363.
[147]K. Satoh and Y. Ohta. Passive Depth Acquisition for 3D Image Displays. IEICE Trans. On Information and Systems, 1994, E77-D(9): 949-959.
[148]H. K. Nishiara. Real-time implementation of a sign-correlation algorithm for image-matching. (Draft)Teleos Research, February 1990.
[149]L. H. Matthies. Stereo vision for planerary rovers: stochastic modeling to near real-time implementation. International Journal Journal of Computer vision,1992, 8(1):71-91.
[150]K. Yoshida and H. Shigeo. Real-time stereo vision with multiple arrayed camera. Tokyo. Tokyo Institute of Technology,Department of Mechanical Engineering Science,199x.
[151]T. Kanade. Development of a Video-Rate Stereo Machine. Proceedings of 94 ARPA Image Understanding Workshop, Nov 14-16, 1994, Monttey Ca.549-5558.
[152]S. K. Wei. Three-dimensional View Synthesis from Multiple Images. Technical Reports CITR-TR-42, Computer Science Department of the University of Auckland CITR at Tamaki Campus, March 1999.
[153]D. Marr and T. Poggio. A theory of human stereo vision. Tech. Rep. AI Memo 451, Artificial Inell. Lab., Mass. Inst. Technol., Cambridge, Nov. 1977.
[154]W. Grimson and L. Eric. Computational Experiments with a Feature Based Stereo Algorithm. IEEE Trans. On Pattern Anal. Machine Intell., Jan. 1985, 7(1):17-34.
[155]R. D. Arnold. Automated stereo perception. Ph.D. thesis, Stanford Univ.,Sanford, CA, Mar. 1983; Tech. Rep. AIM-351 and STAN-CS-83-961.
[156]Baker H. H. Depth from edge and intensity based stereo. Tech. Rep. AIM-347 and STAN-CS-82-930,Stanford Univ., Stanford, CA, Sept. 1982.
[157]Y. Ohta and T Kanade. Stereo by two-level dynamic programming. IEEE Trans. Part. Annal. Machine Intell., Apr. 1985, 7(2): 139-154.
[158]G. Medioni and R. Nevatia. Segment-based stereo matching. Comput. Vision Graphics Image Processing, 1985, 31:2-18.
[159]S. L. Chou and W. H. Tsai. Line segment matching for 3D computer vision using a new iteration scheme. Machine Vision and Applications, 1993, 6:191-205.
[160]N. Ayache and B. Faverjon. Efficient Registration of Stereo Images by Matching Graph Descriptions of Edge Segments. Internationl Journal of Computer Vision, 1987, 1(2): 107-131.
[161]D. Terzopoulos. The computation of visible surface representation. IEEE Trans.PAMI, July 1988, 10(4): 417-438.
[162]W. Hoff and N. Ahuja. Surfaces from Stereo: Integrating Feature Maching,Disparity Estimation, and Contour Detection. IEEE PAMI, 1989,11(2):121-136.
[163]T. D. Sanger. Stereo Disparity Computations Using Gabor Filter. Biol. Cybern,1988,59:405-418.
[164]M. R. M. Jenkin and A. D. Jepson. Recovering Local Surface Structure through Local Phase Difference Methods. CVGIP, 1994, 59: 72-93.
[165]Maki A.,Uhlin T. and Eklundh J.-O. Phase-Based Disparity Estimation in Binocular Tracking. In Proc. 8th Scandinavian Conference on Image Analysis,1993,1145-1152.
[166]B. Crespi, A. G. Cozzi and L. Raffo, et al. Analog Computation for phase-based disparity estimation:continuous and discrete models. Machine Vision and Applications, 1998, 11: 83-95.
[167]T. Frohlinghaus and J. M. Buhmann. Regularizing Phase-Based Stereo. in International Conference on Pattern Recognition(ICPR'96), 1996, 451-455.
[168]T. Frohlinghaus and J. M. Buhmann. Real-Time Phase-Based Stereo for a Mobile Robot. In Proceedings of the First Euromicro Workshop on Advanced Mobile Robots, 1996, 178-185.
[169]D. J. Fleet. Disparity from Local Weighted Phase-Correlation. IEEE International Conference on SMC,San Antonio, Oct. 1994, 48-56.
[170]S. Birchfield and C. Tomasi. Depth Discontinuities by Pixel-to-Pixel Stereo.Proc. Of the 1998 IEEE International Conference on Computer Vision,Bombay,India.
[171]C. Tomasi and R. Manduchi. Stereo Without Search. European Conference on Computer Vision (ECCV) 1996.
[172]H. Farid and E. P. Simoncelli. Range Estimation by Optical Differentiation.Journal of the Optical Society of American, 1998.
[173]D. Scharstein and R. Szeliski. Stereo Matching with Nonlinear Diffusion. International Journal of Computer Vision, 1998,28(2): 155-174.
[174]H. H. Baker and T. O. Binford. Depth from edge and intensity based stereo. In Proceeding Seventh International Joint Conference Artificial Intelligent, Aug.1981,631-636.
[175]Y. Ohta, T. Kanade. Stereo by intra- and inter-scanline search using dynamic programming. IEEE Transactions on Pattern Analysis and Machine Intelligence,1985, 7(2): 139-154.
[176]G. Medioni and R. Nevatia. Segment Based Stereo Matching. Computer Vision,Graphics and Image Processing, 1985, 31(1): 2-18.
[177]A. T. Brint and M. Brady. Stereo matching of curves. Image and Vision Computing, 1990, 8(1): 50-56.
[178]O. Faugeras, et al. Real time correlation based stereo: algorithm implementations and applications. INRIA, Technical Report. RR2013, 1993
[179]P. Fua. A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications, Winter 1993, 6(1):35-49. Available as INRIA research report 1369.
[180]J. J. Weng. Image matching using the windowed fourier phase. International Journal of Computer Vision, 1993, 11: 211-239.
[181]D. Comaniciu and P. Meer. Mean Shift: A robust approach toward feature space analysis. IEEE Transaction on PAMI, 2002, 24(5): 603-619.
[182]L. R. Rabiner and R. W. Schafter. Digital processing of speech signals.Published by Prentice Hall, United States, 1978.
[183]D. Berndt and J. Clifford. Using dynamic time warping to find patterns in time series. Working Notes of the Knowledge Discovery in Databases Workshop,1994, 359-370.
[184]K. Muhlmann, et al. Calculating dense disparity maps from color stereo images:an Efficient Implementation. International Journal of Computer Vision, 2002,47(1-3): 79-88.
[185]A. Koschan. Chromatic block matching for dense stereo correspondence.Proceeding 7th International Conference on Image Analysis and Processing 7ICIAP, Capitolo, Monopoly, Italy, 1993, 641-648.
[186]Z. R. Chi, H. Yan and T. Pham. Fuzzy algorithms: with application to image processing and pattern recognition. World Scientific Publishing Co. Pte. Ltd.Singapore, 1996.____________________________________________________
[187]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):17-22.
[188]J. Martin and J. L. Crowley. Comparison of correlation techniques. In Intelligent Autonomous Systems, 1995, 86-93.
[189]L. S. Shapiro, H. Wang and J.M. Brady. A matching and tracking strategy for independently-moving, non-rigid objects. In Proc. 3rd British Machine Vision Conference, Springer Verlag, Sep. 1992, 306-315.
[190]X. Hu and N. Ahuja. Matching point features with ordered geometric, rigidity,and disparity constraints. PAMI, 1994, 16(10): 1041-1049.
[191]R. Deriche, Z. Zhang, Q.-T. Luong and O. Faugeras. Robust recovery of the epipolar geometry for an uncalibrated stereo rigion. ECCV, 1994, 567-576.
[192]Z. Zhang, R. Deriche, O. Faugeras and Q.-T. Luong. A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry. Technical Report 2273, INRIA, May 1994.
[193]P. Long Limozin and G Giraudon. Stereo matching based on contextual line-region primitives. In Proceedings of the 8th International Conference on Pattern Recognition, Paris, France, 1986, 974-977.
[194]R. Horaud and T. Skordas. Stereo correspondence through feature grouping and maximal cliques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(11): 1168-1180.
[195]B. B. Hansen and B. S. Morse. Multiscale image registration using scale trace correlation. In CVPR, 1999, 202-208.
[196]X. Hu and N. Ahuja. Feature extraction and matching as signal detection.International Journal of Pattern Recognition and Artiffcial Intelligence, 1994,8(6): 1343-1379.
[197]D. H. Ballard and L. E. Wixson. Object recognition using steerable filters at multiple scales. In IEEE Workshop on Qualitative Vision, 1993, 2-10.
[198]Y. Dufournaud, C. Schmid, and R. Horaud, Matching images with different resolutions, In CVPR, 2000, 612-618.
[199]T. Lindeberg. Scale-Space Theory in Computer Vision. Kluwer Academic Publishers, 1994.
[200]T. Lindeberg. Feature detection with automatic scale selection. IJCV, 1998,30(2):79-116.
[201]C. Schmid and R. Mohr. Local grayvalue invariants for image and Machine Vision. Pacific Grove, CA: PWS, 1999.
[202]D. G. Lowe. Object recognition from local scale-invariant features. In Proc. of the International Conference on Computer Vision, 1999, 1150-1157.
[203]Ganhua Li, Dongxiang Zhou, Yunhui Liu. View synthesis based on texture mapping.IEEE 5th World Congress on Intelligent Control and Automation (WCICA 2004) No.A51,June 14-18,2004,3911-3917.
[204]R.Mehrotra,S.Nichani,and N.Ranganathan.Comer detection.Pattern Recognition,1990,23:1223-1233.
[205]E.Shilat,M.Werman,and Y.Gdalyahu.Ridge's comer detection and correspondence.In IEEE Conference on Computer Vision and Pattern Recognition,1997,976-981.
[206]H.Wang and M.Brady.Real-time coner detection algorithm for motion estimation.Image and Vision Computing,1995,13(9):695-703.
[207]C.Harris and M.Stephens.A combined comer and edge detector.In Alvey Vision Conference,1988,147-151.
[208]N.Georgis,M.Petrou,and J.Kittler.On the correspondence problem for wide angular separation of non-coplanar points.Image and Vision Computing,1998,16:35-41.
[209]S.M.Smith and J.M.Brady.SUSAN-A new approach to low level image processing.Int.Journal of Computer Vision,May 1997,23(1):45-78.
[210]S.Nassif,D.Capson and A.Vaz.Robust real-time comer location measurement.IEEE Instrumentation and Measurement Technology Conference,1997,1:106-111.
[211]X.H.ZHANG,et al.Techniques for modeling and rendering a video image-based virtual environment for driving simulation system.International Conference on Artificial Reality and Telexistence,Tokyo,Japan,1998,63-70.
[212]何友,王国宏,陆大多金,彭应宁.多传感器信息融合及应用.北京:电子工业出版社,2000.
[213]何友,唐劲松,王国宏.多雷达综合跟踪.电子科学学刊,1996(3):225-229.
[214]A.Farina,S.Pardini.Multiple platform sensor integration model:MULSIM Computer Program.ADA 079951,Naval Research Laboratory,31,Dec.1999:1-61.
[215]孙长库,叶声华.激光测量技术.天津:天津大学出版社,2001.
[216]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.
[217]Y.Shrai and M.Suwa.Recognition of polyhedrons with a range finder.In International Joint Conferrence on Artificial Intelligence,1971:80-87.
[218]G.J.Agin and T.O.Binford.Computer description of curved objects.In Internetional Joint Conferrence on Artificial Intelligence,1973:629-630.
[219]C.H.Chen and A.C.Kak.Modeling and calibration of a structure light scanner for 3-d robot vision.In Proceedings of the IEEE Conference on Robotics and Automation,1987:807-815.
[220]O. Faugeras. Three-dimentional computer vision: A Geometric Viewpoint. The MIT Press, 1993.
[221]R. Hartley and A. Zisserman. Multiple view geometry. Cambridge University Press, 2000.
[222]于金霞,蔡自兴,邹小兵,段琢华.基于动态自适应滤波的移动机器人障碍检测.自然科学进展, 2006. 16(5): 618-624.
[223]K. R. Beevers, W. H. Huang. SLAM With Sparse Sensing. In IEEE Intenetinal Confonrence on Robotics and Automation. 2006: 2285-2290.
[224]Y. Liu, R. Emery, D. Chakrabarti, W. Bulgard, and S. Thrun. Using EM to Learn 3D Models of Indoor Environments with Mobile Robots. Intenetinal Confonrence Machine Learning, June 28-July 1, 2001:329-336.
[225]V. Brajovic, K. Mori, and N. Jankovic.100 Frames/s cmos range image sensor.Digest of Technical Papers, 2001 IEEE International Solid-State Circuits Conference, February 2001, 256-257 and 453.
[226]L. D. Reid. Projective calibration of a laser-stripe range finder. Image and Vision Computing, October 1996, 14(9): 659-666.
[227]H. Baltzakis, A. Argyros, and P. Trahanias. Fusion of laser and visual data for robot motion planning and collision avoidance. International Journal of Machine Vision and Applications, 2003, 15(2): 92-100.
[228]J. Forest, J. Salvi. A review of laser scanning three-dimensional digitizers. In IEEE/RSJ International Conference on Intelligent Robots and System. EPFL Lausanne, Switzerland, 2002, 1: 73-78.
[229]J. Davis and X. Chen. A laser range scanner designed for minimum calibration complexity. In 2001 IEEE Third International Conference on 3D Digital Imaging and Modeling, Canada, 2001, 91-98.
[230]Q. L. Zhang, R. Pless. Extrinsic calibration of a camera and laser range finder (improves camera calibration). In 2004 IEEE/RSJ International Conference on Intelligent Robots and systems, Sendai, Japan, 2004, 2301-2306.
[231]T. Hong, R. Bostelman, and R. Madhavan. Obstacle detection using a TOF range camera for indoor AGV. Navigation, PerMIS 2004, Gaithersburg, MD,June 2004.
[232]I. Bauermann, E. Steinbach. Joint calibration of a range and visual sensor for the acquisition of RGBZ concentric mosaics. VMV2005, November 2005.
[233]S. Wasielewski, and O. Strauss. Calibration of a multi-sensor system laser rangefinder/camera. Proceedings of the Intelligent Vehicles '95 Symposium,Sep. 25-Sep. 26, 1995, Detroit, USA Sponsored by IEEE Industrial Electronics Society, 1995,472-477.
[234]G. Q. Wei, and S. D. Ma. Implicit and explicit camera calibration: theory and experiments.IEEE Transaction Pattern Analysis and Machine Intelligence,1994,16(5):169-180.
[235]J.Stoer,and R.Burlisch.Introduction to Numerical Analysis.Springer Verlag New York Inc,1980.
[236]C.Mei,and I.Sophia-Antipolis.Camera calibration toolbox for matlab.Available:http://www.vision.caltech.edu/bouguetj/ealib_doc/index.html.
[237]H.Wang.The calculation of the distance between beeline and dot.Journal of Shangluo Teachers College of China,June 2005,19(2):16-19,121.
[238]H.Wang.The calculation of the distance between beeline and dot.Journal of Shangluo Teachers College of China,June 2005,19(2):16-19,121.
[238]许智钦,孙长库,陶立,郑义忠.彩色三维激光扫描测量方法的研究.光学学报,2003,23(8):1008-1012.
[239]张宗华,彭翔,胡小唐.一种新型彩色三维光学成像系统.光学学报.2002,22(8):994-998.
[240]胡汉平,李德华,金刚,王祖喜.三维激光彩色扫描系统.高技术通讯,2001,3:25-30.
[241]范海英.Cyra三维激光扫描系统在精准林业中的应用研究.硕士论文,辽宁工程技术大学.