基于视觉的并联机器人位姿检测方法研究
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摘要
位姿是反映并联机器人运动状态的重要参数,但由于并联机器人通常具有复杂的结构,其运动是三维空间的多自由度综合运动,且在无任何检测装置之前,并联机器人的精确空间位置是一个没有规律的未知数,所以如何同时获得多个自由度的高精度测量是并联机器人运动状态控制研究中亟待解决的重要难题。
针对以上问题,本论文以机器视觉检测技术为手段,设计了基于视觉的并联机器人运动位姿检测系统,建立了并联机器人的特征提取和运动跟踪算法,而后分别提出了基于单目视觉和双目视觉的位姿估计方法。
首先,论文系统地阐述了并联机器人的发展历史、国内外研究现状和主要的应用领域；分析了并联机器人位姿检测的方法及在并联机器人领域尚待解决的问题；描述了机器视觉系统的构成,理论的建立及机器视觉测量技术的研究与应用现状。
其次,设计了基于整体信息处理机制的网络控制系统结构并针对并联机器人的位姿检测给出了视觉信息反馈框架及位姿描述方法。
再次,建立并联机器人Haar-like特征集。而后利用该特征集训练简单分类器,并通过使用级联思想将各种简单的、独立的分类器结合起来构建了级联分类器,实现对并联机器人的运动跟踪。
然后,讨论了基于单目视觉的三种并联机器人位姿检测方法：基于矩形不变量的位姿检测方法、基于点相关的位姿计算方法和基于免疫进化算法的位姿估计方法。其中方法一是利用并联机器人末端执行器上的矩形结构在投影中的两个仿射不变量来计算并联机器人的位置和姿态。方法二是以方法一所估计的并联机构的运动位姿信息作为迭代初值；以摄像机坐标系原点到末端执行器平面的距离作为变量,建立该变量与4个特征点的投影深度的关系模型；以末端执行器的刚体结构特性及4个特征点的共面特性建立了7个约束方程,最后由高斯迭代实现对变量的估计,获得并联机构的运动位姿信息。方法三是以待估计的并联机器人的6个位姿参数作为抗原；借鉴生物免疫系统中克隆变异和免疫记忆机理,通过免疫进化获得位姿参数的可行解。
之后,构建了双目视觉位姿检测系统,提出了一种基于主动表观模型的并联机器人位姿检测方法。首先建立了分别适用于左右摄像机的两个AAM (Active Appearance Model)模型,并通过离线学习获得该模型参数；然后利用学习结果实现对输入图像中并联机器人的匹配和跟踪；最后根据投影空间的几何变换关系和匹配后的立体图像对,对并联机构末端执行器进行三维重建和位姿计算。
最后,对全文的工作进行总结,并展望视觉检测领域中有待深入探索的几个研究方向。
Pose is an important parameter reflecting the movement state of a parallel manipulator. However, the problem of measuring the full 6-DOF pose of a parallel manipulator with high accuracy is still urgent to be solved in the movement control of parallel manipulator due to the following three reasons:Firstly, the parallel manipulator usually has complex structure; Secondly, its movement is multi-degree-of-freedom comprehensive one in the three-dimensional space; Thirdly, when there is no any detection device, the precise space position of a parallel manipulator is an unknown quantity which does not obey any laws.
To resolve these problems, a pose detection system based on the stereoscopic vision technology has been designed in the thesis for a moving parallel manipulator. Then the algorithms for feature extraction and tracking the moving parallel manipulator are established. Afterward, the pose estimation methods based on monocular vision and binocular vision are proposed to get high accurate movement pose parameters of the parallel manipulator.
Firstly, the thesis illustrates the history, the current research and main application fields of the parallel manipulator systematically, analyzes the pose estimation methods and the problems to be solved in the parallel manipulator field, and describes the structure and theory of the machine vision system and the current situation of research and application of machine vision measurement technology.
Then, we design a system structure diagram based on the integrated information processing mechanism. For the pose detection of a parallel manipulator, a vision information feedback framework and pose description methods are offered.
Next, we extract the parallel manipulator's different types of Haar-like features and set up a completed feature set. Then, we use this completed feature set to train simple classifiers, and sets up a cascaded classifier by different kinds of single and separate classifiers to track the parallel manipulator.
Then, we discusses three methods of the parallel manipulator's pose estimation based on the monocular vision:Vision-based pose identification of the parallel manipulator using two affine invariants of a parallelogram; Iterative pose estimation for parallel manipulator using points correspondences; Immune evolutionary algorithm to determine the position and rotation of parallel manipulator. In the first method, two affine invariants of a parallelogram on the parallel manipulator's end-effector are utilized to determine the pose of the moving target relative to the camera. The second method is an iterative one. In the iterative process, results in the first method are used as initial values and the height of the vector from the camera's focus point to the parallel manipulator's end-effector is taken as the variable. Then, a model about this variable and the projection depths of each feature point is established, and an error matrix is also established through seven error functions which are produced by the depth estimation and the co-planarity of the four feature points. Finally, the Gaussian iteration is introduced to estimate the value of the variable, and to obtain the parallel mechanism motion pose information. In the third menthod, six pose parameters (three rotation parameters and three translation parameters) to be estimated are taken as antigens. Then several immune mechanisms (such as the clone and mutation mechanism, the immune remember mechanism) are borrowed to design an immune evolutionary algorithm. Through this evolution process, the best solution vector of six pose parameters can be obtained.
Moreover, a pose measurement system based on binocular is built, and a method of calculating the parallel manipulator's pose based on Active Appearance Model (AAM) is proposed. First, two independent AAM models are established:One for the right camera and the other for the left camera. Through an off-line training phase, the parameters of these two AAM models are obtained. Then the end-effector of parallel manipulator is tracked using the trained AAM models. At last, according to the geometry transform relations in projections space and the matched stereo image pairs, the 3D reconstruction and pose estimation of the parallel manipulator is realized.
Finally, the work in this thesis is summarized, and several research directions which need to be explored further are listed.
针对以上问题,本论文以机器视觉检测技术为手段,设计了基于视觉的并联机器人运动位姿检测系统,建立了并联机器人的特征提取和运动跟踪算法,而后分别提出了基于单目视觉和双目视觉的位姿估计方法。
首先,论文系统地阐述了并联机器人的发展历史、国内外研究现状和主要的应用领域；分析了并联机器人位姿检测的方法及在并联机器人领域尚待解决的问题；描述了机器视觉系统的构成,理论的建立及机器视觉测量技术的研究与应用现状。
其次,设计了基于整体信息处理机制的网络控制系统结构并针对并联机器人的位姿检测给出了视觉信息反馈框架及位姿描述方法。
再次,建立并联机器人Haar-like特征集。而后利用该特征集训练简单分类器,并通过使用级联思想将各种简单的、独立的分类器结合起来构建了级联分类器,实现对并联机器人的运动跟踪。
然后,讨论了基于单目视觉的三种并联机器人位姿检测方法：基于矩形不变量的位姿检测方法、基于点相关的位姿计算方法和基于免疫进化算法的位姿估计方法。其中方法一是利用并联机器人末端执行器上的矩形结构在投影中的两个仿射不变量来计算并联机器人的位置和姿态。方法二是以方法一所估计的并联机构的运动位姿信息作为迭代初值；以摄像机坐标系原点到末端执行器平面的距离作为变量,建立该变量与4个特征点的投影深度的关系模型；以末端执行器的刚体结构特性及4个特征点的共面特性建立了7个约束方程,最后由高斯迭代实现对变量的估计,获得并联机构的运动位姿信息。方法三是以待估计的并联机器人的6个位姿参数作为抗原；借鉴生物免疫系统中克隆变异和免疫记忆机理,通过免疫进化获得位姿参数的可行解。
之后,构建了双目视觉位姿检测系统,提出了一种基于主动表观模型的并联机器人位姿检测方法。首先建立了分别适用于左右摄像机的两个AAM (Active Appearance Model)模型,并通过离线学习获得该模型参数；然后利用学习结果实现对输入图像中并联机器人的匹配和跟踪；最后根据投影空间的几何变换关系和匹配后的立体图像对,对并联机构末端执行器进行三维重建和位姿计算。
最后,对全文的工作进行总结,并展望视觉检测领域中有待深入探索的几个研究方向。
Pose is an important parameter reflecting the movement state of a parallel manipulator. However, the problem of measuring the full 6-DOF pose of a parallel manipulator with high accuracy is still urgent to be solved in the movement control of parallel manipulator due to the following three reasons:Firstly, the parallel manipulator usually has complex structure; Secondly, its movement is multi-degree-of-freedom comprehensive one in the three-dimensional space; Thirdly, when there is no any detection device, the precise space position of a parallel manipulator is an unknown quantity which does not obey any laws.
To resolve these problems, a pose detection system based on the stereoscopic vision technology has been designed in the thesis for a moving parallel manipulator. Then the algorithms for feature extraction and tracking the moving parallel manipulator are established. Afterward, the pose estimation methods based on monocular vision and binocular vision are proposed to get high accurate movement pose parameters of the parallel manipulator.
Firstly, the thesis illustrates the history, the current research and main application fields of the parallel manipulator systematically, analyzes the pose estimation methods and the problems to be solved in the parallel manipulator field, and describes the structure and theory of the machine vision system and the current situation of research and application of machine vision measurement technology.
Then, we design a system structure diagram based on the integrated information processing mechanism. For the pose detection of a parallel manipulator, a vision information feedback framework and pose description methods are offered.
Next, we extract the parallel manipulator's different types of Haar-like features and set up a completed feature set. Then, we use this completed feature set to train simple classifiers, and sets up a cascaded classifier by different kinds of single and separate classifiers to track the parallel manipulator.
Then, we discusses three methods of the parallel manipulator's pose estimation based on the monocular vision:Vision-based pose identification of the parallel manipulator using two affine invariants of a parallelogram; Iterative pose estimation for parallel manipulator using points correspondences; Immune evolutionary algorithm to determine the position and rotation of parallel manipulator. In the first method, two affine invariants of a parallelogram on the parallel manipulator's end-effector are utilized to determine the pose of the moving target relative to the camera. The second method is an iterative one. In the iterative process, results in the first method are used as initial values and the height of the vector from the camera's focus point to the parallel manipulator's end-effector is taken as the variable. Then, a model about this variable and the projection depths of each feature point is established, and an error matrix is also established through seven error functions which are produced by the depth estimation and the co-planarity of the four feature points. Finally, the Gaussian iteration is introduced to estimate the value of the variable, and to obtain the parallel mechanism motion pose information. In the third menthod, six pose parameters (three rotation parameters and three translation parameters) to be estimated are taken as antigens. Then several immune mechanisms (such as the clone and mutation mechanism, the immune remember mechanism) are borrowed to design an immune evolutionary algorithm. Through this evolution process, the best solution vector of six pose parameters can be obtained.
Moreover, a pose measurement system based on binocular is built, and a method of calculating the parallel manipulator's pose based on Active Appearance Model (AAM) is proposed. First, two independent AAM models are established:One for the right camera and the other for the left camera. Through an off-line training phase, the parameters of these two AAM models are obtained. Then the end-effector of parallel manipulator is tracked using the trained AAM models. At last, according to the geometry transform relations in projections space and the matched stereo image pairs, the 3D reconstruction and pose estimation of the parallel manipulator is realized.
Finally, the work in this thesis is summarized, and several research directions which need to be explored further are listed.
引文
[1]冯志友,李永刚,张策,杨廷力,并联机器人机构运动与动力分析研究现状及展望,中国机械工程,2006,17(9)：979-984.
[2]陈学生,陈在礼,孔民秀,并联机器人研究的进展与现状,机器人,2002,24(5)：464-469.
[3]刘俊,非平整地面移动机器人平台位姿估计与控制研究,南京理工大学硕士学位论文,南京,2006.
[4]祝世平,强锡富,工件特征点三维坐标视觉测量方法综述,光学精密工程,2000,8(2)：192-197.
[5]U. Baker, S. Drue, N. Gotze et al, Vision-based control of an autonomous disassembly station, Robotics and Autonomous Systems,2001,35:179-189.
[6]赵新华,赵连玉,温殿英,解宁,并联机器人运动学传动性能研究,机械设计,2002,3：12-15.
[7]J.P. Merlet, Parallel robots, Netherlands:Springer,2001.
[8]J.E. Gwinnett, Amusement devices, US Patent No.1,789,680, January 20,1931.
[9]Pollard, W.L.G., Spray painting machine, US Patent No.2,213,108, August 26,1940.
[10]Pollard. W L.V., Position controlling apparatus, US Patent No.2,286,571, June 16,1942.
[11]V.E. Gough, Contribution to discussion of papers on research in automobile stability, control and tyre performance. Proc. Auto Div. Inst. Mech. Eng.,1956.
[12]V.E. Gough, S.G. Whitehall, Universal tyre test machine, Proceedings of the FISITA Ninth International Technical Congress,1962,117-137.
[13]Stewart, D., A platform with six degrees of freedom, Proceedings of the Institution of mechanical engineers,1965,180(Pt.1:15):371-385.
[14]K.H. Hunt, Kinematic Geometry of Mechanisms, Oxford:Clarendon Press,1978.
[15]H. McCallion, P.D. Truong, The analysis of a six-degree-of-freedom work station for mechanized assembly, Proceedings of the Fifth World Congress on Theory of Machines and Mechanisms,1979,611-616.
[16]Fichter, E.F., McDowell, E.D., A novel design for a robot arm, In Proc. Int. Computer Technical Conf, 1980,250-255.
[17]Lande, M.A., David, R.J.P., Articulation for manipulator arm, US Patent No.4,300,362, November 17,1981.
[18]R. Clavel. DELTA, A fast robot with parallel geometry,18th Int. Symp. On Industrial Robot, 1988,91-100.
[19]http://www05.abb.se/robotics/picker/index.html.
[20]F. Pierrot, P.Dauchez, A. Fournier, Towards a fully-parallel 6 d.o.f robot for high speed applications, IEEE International Conference on Robotics and Automation,1991,1288-1293.
[21]M. A. Uchiyama,6 DOF parallel robot HEXA, Advanced Robotics,1994,8(6):601.
[22]Hunt, K.H., Structural kinematics of in parallel actuated robot arms, Journal of Mechanisms, Transmissions and Automation in Design,1983,105(4):705-712.
[23]Hunt K. H., Kinematic geometry of mechanisms, Oxford:Clarendon Press,1990.
[24]Herve J. M., Structural synthesis of parallel robots generating spatial translation, Proc. IEEE Int. Conf. Robot & Auto, Pisa, Italy,1991,808.
[25]Orin, D.E., Oh, S.Y., Control of force distribution in robotic mechanisms containing closed kinematic chains, Journal of Dynamics System Measure and Control,1981,102:134-141.
[26]Earl, C.F., Rooney, J., Some kinematics structures for robot manipulator designs, Journal of Mechanisms, Transmissions and Automation in Design,1983,105(1):15-22.
[27]Rooney, J., Earl, C.F., Manipulator postures and kinematics assembly configurations, In 6th IFToMM World Congress on the Theory of Machines and Mechanisms,1983,1014-1020.
[28]Yang, D.C.H., Lee, T.W., Feasibility study of a platform type of robotic manipulator from a kinematic viewpoint, Journal of Mechanisms, Transmissions and Automation in Design, 1984,106(2):191-198.
[29]Mohamed, M.G., Sanger, J., Duffy, J., Instantaneous kinematics of fully-parallel devices, In 6th IFToMM World Congress on the Theory of Machines and Mechanisms,1983,77-80.
[30]Mohamed, M.G., Duffy, J., A direct determination of the instantaneous kinematics of fully parallel robot manipulators, Journal of Mechanisms, Transmissions and Automation in Design,1985,107(2):226-229.
[31]Ting, K.L., Tsai, G.H., Mobility and synthesis of five-bar programmable linkages, In 9th Applied Mechanisms Conf,1985,Ⅲ.1-Ⅲ.8.
[32]Fichter, E.F., A Stewart platform based manipulator:general theory and practical construction, International Journal of Robotics Research,1986,5(2):157-182.
[33]Sugimoto, K., Kinematic and dynamic analysis of parallel manipulators by means of motor algebra, Journal of Mechanisms, Transmissions and Automation in Design,1987,109(1): 3-7.
[34]Behi, F., Kinematic analysis for a six-degree-of-freedom 3-PRPS parallel mechanism, IEEE Journal of Robotics and Automation,1988,4(5):561-565.
[35]Do, W.Q.D., Yang, D.C.H., Inverse dynamic analysis and simulation of a platform type of robot, Journal of Robotic Systems,1988,5(3):209-227.
[36]Lee, K.M, Shah, D.K., Kinematic analysis of a three-degrees-of-freedom in-parallel actuated manipulator, IEEE Journal of Robotics and Automation,1988,4(3):354-360.
[37]Lee, K.M., Shah, D.K., Dynamic analysis of a three-degrees-of-freedom in-parallel actuated manipulator, IEEE Journal of Robotics and Automation,1988,4(3):361-368.
[38]Merlet, J.P., Force-feedback control of parallel manipulators, In IEEE International Conference on Robotics and Automation,1988,1484-1489.
[39]黄真,孔令富,方跃法,并联机器人机构学理论及控制,北京：北京机械工业出版社,1997.
[40]孙立宁,于晖,祝宇虹,张秀峰,蔡鹤皋,机构影响系数与并联机器人雅克比矩阵的研究,哈尔滨工业大学学报,2002,34(6)：810-814.
[41]方跃法,黄真,三自由度并联机器人操作平台的主运动螺旋研究,中国机械工程,2000,11(7)：805-808.
[42]吴生富,王洪波,黄真,并联机器人工作空间研究,机器人,1990,13(3)：33-39.
[43]张源,崔建昆,卢强,罗玉华,λ型并联机床的工作空间分析,机械工程与自动化,2010,158：32-34.
[44]李方,南仁东,一种分析并联机器人工作空间的解析几何法,机械制造,2003,41(463)：17-19.
[45]刘旭东,黄田,3-TPT型并联机器人工作空间解析与综合,中国机械工程,2001,12(Suppelement):151-153.
[46]金爱娟,徐松源,孙华,模糊神经网络在机器人控制中的应用,哈尔滨理工大学学报,1998,3(5)：50-53.
[47]万亚民,王孙安,杜海峰,液压并联机器人的动态神经网络控制研究,西安交通大学学报,2004,38(9)：955-958.
[48]张曙,U.Heisel,并联运动机床,北京：机械工业出版社,2003.
[49]B. Dasgupta, T. S. Mruthyunjaya, The Stewart Platform Manipulator:a review, Mechanism and Machine Theory,2000,35:15-40.
[50]刘浩,高速轮轨接触状态可视化检测装置,西南交通大学硕士学位论文,成都,2005.
[51]M.A., Computational Stereo. Computing Surveys,1994,4:553-572.
[52]马颂德,张正友,计算机视觉——计算理论和算法基础,北京：科学出版社,1998.
[53]刘曙光,刘明远,何城,机器视觉及其应用,机械制造,2000,38(7)：20-22.
[54]D. G. Lowe, Three Dimensional Object Recognition From Single Two Dimensional Images, Aritificial Intelligence,1987,37:355-395.
[55]Aloimonos Y, Rosenfeld A, A response to "Ignorance, Myopia, and Naviete in computer vision systems" by Jain R C and Binford T O, Computer Vision Graphics and Image Processing,1991,53(1):120-124.
[56]Tsatsoulis C., Fu,K.S., A computer vision system for assembly inspection, Intelligent Robots and Computer Vision,1984,521:352-357.
[57]Gregory,P.J, Taylor,C.J., Knowledge-based models for computer vision, Proceedings of the 4th International Conference on Robot Vision and Sensory Control,1988,325-330.
[58]Liu J, Paulsen M R., Corn whiteness measurement and classification using machine vision, Transaction of American Society of Agricultural Engineers,2000,43(3):757-763.
[59]Schmidberger,E.J., Ahlers,R.J., Quality control with a robot-guided electro-optical sensor, 4th International Conference on Robot Vision and Sensory Controls,1984,27-36.
[60]Duan Feng, Wang Yaonan, A machine vision inspector for beer bottle, Applications of Artificial Intelligence,2007,32(1):1-9.
[61]Milan Sonka, Vaclav Hlavac, Image processing, Analysis, and Machine Vision, United States:Thomson Learning and PT Press,2002.
[62]Aly A. Faraga, M. Sabry Hassounaa, Ayman El-Baza, et al, Real-time vision-based approach for probe tracking in operating room, International Congress Series 1268,2004, 467-472.
[63]Reinhard Rinn, Michael Becker, Ralph Foehr, et al, Steel mill defect detection and classification at 3000 ft/mm using mainstream technology, Proceedings of SPIE,1998,3303: 20-26.
[64]http://www.ni.com/vision/.
[65]Clark F., Olson, Habib Abi-Rached, et al, Wide-Baseline Stereo Vision For Mars Rovers, Proceedings Of The 2003 IEEE/RSJ Intl. Conference On Intelligent Robots And Systems, 2003,1302-1307.
[66]Yajun Fang, Ichiro Masaki, Berthold Horn, Depth-Based Target Segmentation for Intelligent Vehicles:Fusion of Radar and Binocular Stereo, IEEE Transactions on Intelligent Transportation Systems,2002,3(3):196-202.
[67]Minoru Asada, Takamaro Tanaka, Visual Tracking Of Unknown Moving Object By Adaptive Binocular Visual Servoing, Proceeding of the 1999 IEEE International Conference on Multisensor Fusion and Intelligent Systems,1999,249-254.
[68]Kei Okada, Masayuki Inaba, Integration Of Real-Time Binocular Stereo Vision And whole Body Information For Dynamic Walking Navigation Of Humanoid Robot, IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems,2003,131-136.
[69]Vallerand Steve, Kanbara Masayuki, Yokoya Naokazu, Binocular Vision-Based Augmented Reality System With An Increased Registration Depth Using Dynamic Correction Of Feature Positions, Proceedings of the 2003 IEEE Virtual Reality,2003, 22(26):271-272.
[70]张建荣,姜昱明,CCD成像在线测量玻璃管外径及壁厚方法的研究,应用光学,1995,25(3)：53-56.
[71]汪国有,熊佳,非接触式承载鞍磨损测量系统,红外与激光工程,2004,33(6)：669-674.
[72]杨敏,基于机器视觉的发动机气门杆直径及圆度检测研究,华南理工大学博士学位论文,广州,2004.
[73]郝颖明,董再励,刘百川,周静,非接触式机器人直线轨迹测量系统,机器人,2002,24(5)：394-398.
[74]应义斌,水果尺寸和面积的机器视觉检测方法研究,浙江大学学报(农业与生命学报),2000,26(3)：229-232.
[75]雷振山,刘兆妮,应用IMAQ Vision的齿轮测量技术,制造技术与机床,2005,2：53-56.
[76]罗振璧,郑力,汪劲松,张伯鹏,潘尚峰,唐晓红,机器视觉在尺寸在线检测中的应用,机械工程学报,1994,6：47-52.
[77]高媛媛,关胜晓,许栋,基于立体视觉运动速度检测系统的研究及实现,计算机工程与应用,2005,41(2)：45-53.
[78]马桂珍,湛海新,马丙辰,基于双目视觉的手术器械跟踪定位技术,微计算机应用,2005,26(2)：181-183.
[79]陈锡爱,徐方,基于手眼立体视觉的机器人定位系统,计算机应用,2005,25：302-304.
[80]杨耀权,施仁,高键年,基于光学三角原理的视觉检测系统及应用,华北电力大学学报,1998,28(3)：64-68.
[81]管业鹏,童林夙,陈娜,基于双目立体视觉的偏转线圈测量方法研究,电子学报,2003,31(9)：1382-1385.
[82]高庆吉,洪炳熔,阮玉峰,基于异构双目视觉的全自主足球机器人导航,哈尔滨工业大学学报,2003,35(9)：1029-1032,1088.
[83]董斌,杨韧,刘兴占,尤政,一种使用线阵型CCD实现高精度二维位置测量的方法,光学技术,1998,5：42-45.
[84]姜立军,甘东兵,王国荣,基于双目视觉传感的计算机辅助骨外科手术导航模型,华南理工大学学报(自然科学版),2006,34(2)：67-72.
[85]梁华为,直接从双峰直方图确定二值化阈值,模式识别与人工智能,2002,15(2)：253-256.
[86]刘安心,余跃庆,三维机器视觉测量系统的坐标计算模型研究,电气技术与自动化,2005,34(3)：71-73.
[87]陈建林,一种基于目标跟踪的并联机器人视觉检测研究,东华大学硕士学位论文,上海,2009.
[88]Chaumette F., Hu Tchinson S., Visual servo control part I:basic approaches, IEEE Robotics & Automation Magazine,2006(12):82-90.
[89]Chaumette F., Hu Tchinson S.,Visual servo control part Ⅱ:advanced approaches, IEEE Robotics & Automation Magazine,2007(3):109-118.
[90]王麟琨,徐德,谭民,机器人视觉伺服研究进展,机器人,2004,26(3)：277-282.
[91]赵清杰,连广宇,孙增圻,机器人视觉伺服综述,控制与决策,2001,16(6)：849-853.
[92]AiQiu Zuo, Q M Jonathan, et al, Stereo Vision Guided Control of a Stewart Platform, Proceedings of 2002 IEEE International Symposlum on Intelligent Control Vancouver, Canada,2002,125-130.
[93]吴笛飞,一种并联机器人位姿的视觉检测系统研究,东华大学硕士学位论文,上海,2008.
[94]雷成,吴福朝,胡占义,Kruppa方程与摄像机自标定,自动化学报,2001,27(5)：621-630.
[95]Zhengyou Zhang, A Flexible New Technique for Camera Calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[96]S. Birchfield, Elliptical Head Tracking Using Intensity Gradients and Color Histograms, Proc. IEEE conf. Computer Vision and Pattern Recognition,1998,232-237.
[97]H. Chen and T. Liu, Trust-Region Methods for Real-Time Tracking, Proc. Eigth Int'l Conf. Computer Vision,2001,2:717-722.
[98]徐瑞鑫,复杂背景下实时目标跟踪与识别研究,中国科学院长春光学精密机械与物理研究所硕士学位论文,长春,2002.
[99]Yu Zhong, Anil K. Jain, et al, Object Tracking using Deformable Templates, IEEE transactions on pattern analysis and machine intelligence,2000,22(5):544-549.
[100]A.K.Jain, YZhong, S.Lakshmanan, Object Matching Using Defonnable Templates, IEEE Trans. Pattern Analysis and Machine Intelligence,1996,18(3):267-278.
[101]Shoichi Araki, Takashi Matsuoaka, Naokazu Yokoya, et al. Real-time tracking of multiple moving object contours in a moving camera image sequence. IEICE Trans Inf & Syst, 2000,E83-D(7):1583-1591.
[102]Yun-Ting Lin, Yuh-Lin Chang, Tracking Deformable Objects with the Active Contour Model, Proceedings of IEEE International Conference on Multimedia Computing and Systems (ICMCS'97),1997,608-609.
[103]M.Bertalmio, GSapiro, and GRandall, MorphingActive Contours. Proc. Int'l Conf Scale-Space Theories in Computer Vision,1999,46-57.
[104]Vieren C, Cabestaing F, Postaire J., Catching Moving Objects with Snakes for Motion Tracking, Pattern Recognition Letters,1995,16:679-685.
[105]W J Rucklidge, Efficiently Locating Objects Using Hausdorff Distance, International Journal of Computer Vision,1997,24(3):251-270.
[106]J.Shi, C.Tomasi, Good Feature to track, Proceedings of IEEE Conference on Computer Vision and pattern Recognition,1994,593-600.
[107]Rainer Lienhart, Jochen Maydt, An Extended Set of Haar-like Features for Rapid Object Detection, IEEE2002international Conference on Image Processing, Rochester, 2002,900-903.
[108]Z.F.He, T.N.Tan, ZN.Sun, Topology modeling for Adaboost-cascade based object detection, Pattern Recognition Letters,2010,31(9):912-919.
[109]S.Z.Su, S.Y Chen, S.Z.Li, D.J. Duh, Structured local binary Haar pattern for pixel-based graphics retrieval, Electronics Letters,2010,46(14):996-997.
[110]T.S.Huang, A.N.Netravali, Motion and structure from feature correspondences:A review, Proc.IEEE,1994,82:252-268.
[111]郑南宁,计算机视觉与模式识别,北京：国防工业出版,1998.
[112]Mahmoud I Khalil, Mohamed M Bayoumi, A Dyadic Wavelet Affine Invariant Function for 2D Shape Recognition, IEEE Trans Pattern Analysis and Machine Intelligence, 2001,23(10):1152-1164.
[113]Dongjoe Shin, TardiTjahjadi, Clique descriptor of affine invariant regions for robust wide baseline image matching, Pattern Recognition,2010,43:3261-3272.
[114]Terry Y.F. Yuen, Peter W.M. Tsang, Affine invariant matching of broken boundaries based on particle swarm optimization, Image and Vision Computing,2008,26:1230-1239.
[115]A. Bandera, R. Marfil, E. Antunez, Affine-invariant contours recognition using an incremental hybrid learning approach, Pattern Recognition Letters,2009,30:1310-1320.
[116]S. Ponnusamy, J. Qiao, X. Wang, Schwarzian derivative, integral means, and the affine and linear invariant families of biharmonic mappings, Applied Mathematics and Computation,2010,216:3468-3479.
[117]罗荣华,闵华清,马鹏飞,柳雄,一种仿射不变特征及其在SLAM中的应用,华中科技大学学报(自然科学版),2008,36：159-162.
[118]毛玉星,韩倍涛,冯莲,王豪,徐少志,郝远洋,粒子群优化算法实现仿射不变性形状识别,重庆大学学报,2010,33(3)：65-70.
[119]程亮,龚健雅,韩鹏,宋小刚,遥感影像仿射不变特征匹配的自动优化,武汉大学学报(·信息科学版),2009,34(4)：418-422.
[120]汪文英,张冬明,张勇东,李锦涛,利用仿射变换的快速空间关系验证,计算机辅助设计与图形学学报,2010,22(4)：625-631.
[121]R. Horaud, B. Conio, O. Leboulleux, B. Lacolle, An analytic solution for the perspective 4-point problem, Computer Vision Graphics Image Process,1989,47(1):33-44.
[122]Z.-Y. Hu, F.-C.Wu, A note on the number of solutions of the noncoplanar p4p problem, IEEE Trans. Pattern Anal. Machine Intell.,2002,24(4):550-555.
[123]D. Nister, H. Stewenius, A minimal solution to the generalized 3-point pose problem, Math. Imaging Vision,2007,27(1):67-79.
[124]X.-S. Gao, J. Tang, On the probability of the number of solutions for the P4P problem, Math. Imaging Vision,2006,25(1):79-86.
[125]Xu, D., Li, Y.F., Tan, M., A general recursive linear method and unique solution pattern design for the perspective-n-point problem, Image and Vision Computing,2008,26:740-750.
[126]Hatem Hmam, Jijoong Kim, Optimal non-iterative pose estimation via convex relaxation, Image and Vision Computing,2010,28:1515-1523.
[127]D.G. Lowe, Fitting parameterized three-dimensional models to images, IEEE Trans. Pattern Analysis and Machine Intelligence,1991,13:441-450.
[128]L. J. QIN, F. Zhu, A novel algorithm for real-time pose determination and its iterative method, Proceedings of the 2006 IEEE International Conference on Information Acquisition,2006,26-30.
[129]E. Olson, J. Leonard, S. Teller, Fast Iterative Alignment of Pose Graphs with Poor Initial Estimates, Proceedings of the International Conference on Robotics and Automation,2006, 2262-2269.
[130]S.H. Or, W.S. Luk, K.H. Wong, et al, An Efficient Iterative Pose Estimation Algorithm, Image and Vision Computing,1998,16(5):353-362.
[131]裴巧娜,基于光流法的运动目标检测与跟踪技术,北方工业大学硕士学位论文,北京,2009.
[132]李喜来,李艾华,白向峰,智能交通系统运动车辆的光流法检测,光电技术应用,2010,25(2)：75-78.
[133]靳薇,张建奇,张翔,基于局部梯度算子的嘴部检测与定位,光电工程,2009,36(10)：135-140.
[134]曹阳,曲仕茹,张飞,马文佳,基于梯度统计和区域生长融合的运动车辆阴影检测方法,传感技术学报,2010,23(3)：413-417.
[135]鲜海滢,基于图像时空梯度的运动目标检测技术研究,电子科技大学博士学位论文,成都,2009.
[136]Qin Lijuan, Zhu Feng, A New Method for Pose Estimation from Line Correspondences, Acta Automatica Sinica,2008,34(2):130-134.
[137]Guanghui Wang, Jonathan Wua, Zhengqiao Ji, Single view based pose estimation from circle or parallel lines, Pattern Recognition Letters,2008,29:977-985.
[138]Pradit Mittrapiyanuruk, Guilherme N. DeSouza, Avinash C. Kak, Calculating the 3D-Pose of Rigid-Objects Using Active Appearance Models, IEEE International Conference in Robotics and Automation,2004,5147-5152.
[139]Jie Huang, Chunlei Zhao, Yoshitaka Ohtake, Huakang L, Qunfei Zhao, Robot Position Identification Using Specially Designed Landmarks, Instrumentation and Measurement Technology Conference Sorrento,2006,2091-2094.
[140]B. Stenger, A. Thayananthan, P.H.S. Torr, R. Cipolla, Estimating 3D hand pose using hierarchical multi-label classification, Image and Vision Computing,2007,25:1885-1894.
[141]Yasushi Sumi,Yutaka Ishiyama, Fumiaki Tomita, Robot-vision architecture for real-time 6-DOF object localization, Computer Vision and Image Understanding,2007,105:218-230.
[142]Khotanzad,A., Lion J., Recognition and pose estimation of unoccluded three-dimensional objects from a two-dimensional perspective view by banks of neural networks, IEEE Trans. Neural Networks,1996,7(4):897-906.
[143]S. Hati, S. Sengupta, Robust camera parameter estimation using genetic algorithm, Pattern Recognition Letters,2001,22:289-298.
[144]Hu, ZH, Ding, YS, An immune inspired co-evolutionary affinity network for prefetching of distributed object, Journal of Parallel and Distributed Computing,2010,70(2):92-100.
[145]Hu, ZH, Ding, YS, Yu, XK, et al., A hybrid neural network and immune algorithm approach for fit garment design, Textile Research Journal,2009,79(14):1319-1330.
[146]Hu, ZH, Ding, YS, Shao, Q, Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system, Expert Systems with Aoolications,2009,36(3): 5248-5255.
[147]Liu, FC, Ren, LH, Ding, YS, A new mutual-coupled immune network based peer to peer network model with balanced tree structure, Intelligent Decision Making Systems,2010,2: 173-178.
[148]Liang, ZX, Ding, YS, Gu, Q, et al.,Optimizing Multicast Tree in NGN Based on Self-Adaptive Immune Algorithm, Asia-Pacific Conference on Information Processing, 2009,47-50.
[149]Xu, HQ, Ding, YS, Optimizing method for analog circuit design using adaptive immune genetic algorithm, Proceedings of the 4th international conference on frontier of computer science and technology,2009,359-363.
[150]Hu, ZH, Ding, YS, Lu, XJ, Cognitive immune system-based co-evolutionary information retrieval model, International Symposium on Intelligent Information Technology Application,2008,212-215.
[151]Ding, YS, Hu, ZH, Sun, HB, An antibody network inspired evolutionary framework for distributed object computing, Information Sciences,2008,178(24):4619-4631.
[152]K.C. Tan, C.K. Goh, A.A. Mamun, E.Z. Ei, An evolutionary artificial immune system for multi-objective optimization, European Journal of Operational Research,2008,187: 371-392.
[153]Maoguo Gong, Licheng Jiao, Xiangrong Zhang, A population-based artificial immune system for numerical optimization, Neurocomputing,2008,72:149-161.
[154]王磊,潘进,焦李成,免疫规划,计算机学报,2000,23(8)：806-812.
[155]D. Castro, Immune,Swarm and Evolutionary Algorithms Part I:Basic Models, In Proc. The 9th International Conference on Neural Information Processing,2002,1464-1468.
[156]D. Dasgupta, Artificial Immune System and Their Application, Berlin:Springer-Verlag, 1998.
[157]D. Dasgupta, S. Forrest, Novelty Detection in Time Series Data Using Ideas From Immuneology, In Proc. the 5th International Conference on Intelligent Systems,1996, 19-21.
[158]S. Forrest, A. S. Perelson, L. Allen, et al. Self-Nonself Discrimination in a Computer, In Proc.1994 IEEE Symposium on Research in Security and Privacy,1994,202-212.
[159]J. Kim, P. J. Bentley, Towards an Artificial Immune System for Network Intrusion Detection:an Investigation of Clonal Selection with a Negative Selection Operator, In Proc. the 2001 Congress on Evolutionary Computation,2001,2:1244-1252.
[160]D. Dasgupta, S. Yu, N. Majumdar., MILA--Multi-Level Immune Learning Algorithm and its Application to Anomaly Detection, The Soft Computing Journal,2005,9(3):172-184.
[161]G. Du, T. Huang, B. Zhao, and L. Song, Dynamic Self-Defined Immunity Model Base on Data Mining for Network Intrusion Detection, In Proc.4th International Conference on Machine Learning and Cybernetics,2005,6:3866-3870.
[162]A. Ishiguro, T. Kondo, Y. Shirai,et al, Immunoid:An Architecture for Behavior Arbitration Based on the Immune Networks, In Proc. The 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems,1996,1730-1738.
[163]J. E. Hunt, D. E. Cooke, Learning Using an Artificial Immune System, Journal of Network and Computer Applications,1996,19(2):189-212.
[164]D. F. McCoy,V Devarajan, Artificial Immune Systems and Aerial Image Segmentation, In Proc. IEEE International Conference on Systems, Man, and Cybernetics, 1997,1:867-872.
[165]J. Timmis, Artificial Immune Systems:a Novel Data Analysis Technique Inspired by the Immune Network Theory, PhD thesis, Department of Computer Science, University of Wales, Aberystwyth. Ceredigion. Wales,2000.
[166]D. Castro, F. J. V. Zuben, An Evolutionary Immune Network for Data Clustering, In Proc. the Sixth Brazilian Symposium on Neural Networks,2000,84-89.
[167]Y Ishida, Active Diagnosis by an Immunity-Based Agent Approach, In Proc. the 7th International Workshop on Principles of Diagnosis,1996,106-114.
[168]Y Ishida, Fully Distributed Diagnosis by PDP Learning Algorithm:Towards Immune Network PDP Model, In Proc. the International Joint Conference on Neural Networks, 1990,777-782.
[169]Jiaquan Gao, Jun Wang, WBMOAIS:A novel artificial immune system for multi-objective optimization, Computers & Operations Research,2010,37(1):50-61.
[170]Y.-C. Hsieh, Y.-C. Lee, P.-S. You, T.-C. Chen, An immune based two-phase approach for the multiple-type surveillance camera location problem, Expert Systems with Applications, 2009,36(7):10634-10639.
[171]V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian, Artificial immune system for parameter estimation of induction motor, Expert Systems with Applications,2010,37(8):6109-6115.
[172]Ilhan Aydin, Mehmet Karakose. Erhan Akin, Artificial immune classifier with swarm learning, Engineering Applications of Artificial Intelligence, (In Press).
[173]Shuaiqiang Wang, Jun Ma, Qiang He, An immune programming-based ranking function discovery approach for effective information retrieval, Expert Systems with Applications, 2010,37(8):5863-5871.
[174]Jianyong Chen, Qiuzhen Lin, Zhen Ji, A hybrid immune multi-objective optimization algorithm, European Journal of Operational Research,2010,204:294-302.
[175]莫宏伟,人工免疫系统原理与应用,哈尔滨：哈尔滨工业大学出版社,2002.
[176]肖人彬,王磊,人工免疫系统：原理、模型、分析及展望,计算机学报,2002,25(12)：1281-1293.
[177]Cutello V, Nicosia C, Pavone M, et al. An Immune Algorithm for Protein Structure Prediction on Lattice Models, IEEE Transactions on Evolutionary Computation,2007,11(1): 101-117.
[178]葛动元,姚锡凡,李凯南,双目立体视觉系统的标定,机械设计与制造,2010,6：188-189.
[179]魏振忠,樊妍睿,张广军,光栅式双目立体视觉传感器的立体匹配方法,红外与激光工程,2010,39(2)：330-334.
[180]杜志军,王阳生,利用主动外观模型合成动态人脸表情,计算机辅助设计与图形学学报,2009,21(11)：1590-1594.
[181]呼月宁,张艳宁,朱宇,崔瑞,AAM在多姿态人脸特征点检测中的应用,计算机工程与应用,2010,46(12)：161-165.
[182]Jason Saragih, RolandGocke, Learning AAM fitting through simulation, Pattern Recognition,2009,42:2628-2636.
[183]范小九,彭强,Jim X Chen,利用主动外观模型进行3D人脸的快速自动建模,计算机辅助设计与图形学学报,2009,21(5)：668-673.
[184]张宇辉,吕国强,胡跃辉,刘文文,立体显示的双目模型算法及实现,计算机工程与应用,2006,42(35)：65-67.
[185]任翠花,多视图匹配算法研究,哈尔滨工业大学硕士学位论文,哈尔滨,2008.
[186]P. Tresadern, H. Bhaskar, S. Adeshina, C. Taylor, T.F. Cootes, Combining Local and Global Shape Models for Deformable Object Matching, Proc. BMVC09,2009,1-12.
[187]张淑平,丁永生,郝矿荣,吴笛飞,基于立体视觉的六自由度大载荷试验平台的位姿检测,计算机应用研究,2008,25(6)：1744-1746.
[2]陈学生,陈在礼,孔民秀,并联机器人研究的进展与现状,机器人,2002,24(5)：464-469.
[3]刘俊,非平整地面移动机器人平台位姿估计与控制研究,南京理工大学硕士学位论文,南京,2006.
[4]祝世平,强锡富,工件特征点三维坐标视觉测量方法综述,光学精密工程,2000,8(2)：192-197.
[5]U. Baker, S. Drue, N. Gotze et al, Vision-based control of an autonomous disassembly station, Robotics and Autonomous Systems,2001,35:179-189.
[6]赵新华,赵连玉,温殿英,解宁,并联机器人运动学传动性能研究,机械设计,2002,3：12-15.
[7]J.P. Merlet, Parallel robots, Netherlands:Springer,2001.
[8]J.E. Gwinnett, Amusement devices, US Patent No.1,789,680, January 20,1931.
[9]Pollard, W.L.G., Spray painting machine, US Patent No.2,213,108, August 26,1940.
[10]Pollard. W L.V., Position controlling apparatus, US Patent No.2,286,571, June 16,1942.
[11]V.E. Gough, Contribution to discussion of papers on research in automobile stability, control and tyre performance. Proc. Auto Div. Inst. Mech. Eng.,1956.
[12]V.E. Gough, S.G. Whitehall, Universal tyre test machine, Proceedings of the FISITA Ninth International Technical Congress,1962,117-137.
[13]Stewart, D., A platform with six degrees of freedom, Proceedings of the Institution of mechanical engineers,1965,180(Pt.1:15):371-385.
[14]K.H. Hunt, Kinematic Geometry of Mechanisms, Oxford:Clarendon Press,1978.
[15]H. McCallion, P.D. Truong, The analysis of a six-degree-of-freedom work station for mechanized assembly, Proceedings of the Fifth World Congress on Theory of Machines and Mechanisms,1979,611-616.
[16]Fichter, E.F., McDowell, E.D., A novel design for a robot arm, In Proc. Int. Computer Technical Conf, 1980,250-255.
[17]Lande, M.A., David, R.J.P., Articulation for manipulator arm, US Patent No.4,300,362, November 17,1981.
[18]R. Clavel. DELTA, A fast robot with parallel geometry,18th Int. Symp. On Industrial Robot, 1988,91-100.
[19]http://www05.abb.se/robotics/picker/index.html.
[20]F. Pierrot, P.Dauchez, A. Fournier, Towards a fully-parallel 6 d.o.f robot for high speed applications, IEEE International Conference on Robotics and Automation,1991,1288-1293.
[21]M. A. Uchiyama,6 DOF parallel robot HEXA, Advanced Robotics,1994,8(6):601.
[22]Hunt, K.H., Structural kinematics of in parallel actuated robot arms, Journal of Mechanisms, Transmissions and Automation in Design,1983,105(4):705-712.
[23]Hunt K. H., Kinematic geometry of mechanisms, Oxford:Clarendon Press,1990.
[24]Herve J. M., Structural synthesis of parallel robots generating spatial translation, Proc. IEEE Int. Conf. Robot & Auto, Pisa, Italy,1991,808.
[25]Orin, D.E., Oh, S.Y., Control of force distribution in robotic mechanisms containing closed kinematic chains, Journal of Dynamics System Measure and Control,1981,102:134-141.
[26]Earl, C.F., Rooney, J., Some kinematics structures for robot manipulator designs, Journal of Mechanisms, Transmissions and Automation in Design,1983,105(1):15-22.
[27]Rooney, J., Earl, C.F., Manipulator postures and kinematics assembly configurations, In 6th IFToMM World Congress on the Theory of Machines and Mechanisms,1983,1014-1020.
[28]Yang, D.C.H., Lee, T.W., Feasibility study of a platform type of robotic manipulator from a kinematic viewpoint, Journal of Mechanisms, Transmissions and Automation in Design, 1984,106(2):191-198.
[29]Mohamed, M.G., Sanger, J., Duffy, J., Instantaneous kinematics of fully-parallel devices, In 6th IFToMM World Congress on the Theory of Machines and Mechanisms,1983,77-80.
[30]Mohamed, M.G., Duffy, J., A direct determination of the instantaneous kinematics of fully parallel robot manipulators, Journal of Mechanisms, Transmissions and Automation in Design,1985,107(2):226-229.
[31]Ting, K.L., Tsai, G.H., Mobility and synthesis of five-bar programmable linkages, In 9th Applied Mechanisms Conf,1985,Ⅲ.1-Ⅲ.8.
[32]Fichter, E.F., A Stewart platform based manipulator:general theory and practical construction, International Journal of Robotics Research,1986,5(2):157-182.
[33]Sugimoto, K., Kinematic and dynamic analysis of parallel manipulators by means of motor algebra, Journal of Mechanisms, Transmissions and Automation in Design,1987,109(1): 3-7.
[34]Behi, F., Kinematic analysis for a six-degree-of-freedom 3-PRPS parallel mechanism, IEEE Journal of Robotics and Automation,1988,4(5):561-565.
[35]Do, W.Q.D., Yang, D.C.H., Inverse dynamic analysis and simulation of a platform type of robot, Journal of Robotic Systems,1988,5(3):209-227.
[36]Lee, K.M, Shah, D.K., Kinematic analysis of a three-degrees-of-freedom in-parallel actuated manipulator, IEEE Journal of Robotics and Automation,1988,4(3):354-360.
[37]Lee, K.M., Shah, D.K., Dynamic analysis of a three-degrees-of-freedom in-parallel actuated manipulator, IEEE Journal of Robotics and Automation,1988,4(3):361-368.
[38]Merlet, J.P., Force-feedback control of parallel manipulators, In IEEE International Conference on Robotics and Automation,1988,1484-1489.
[39]黄真,孔令富,方跃法,并联机器人机构学理论及控制,北京：北京机械工业出版社,1997.
[40]孙立宁,于晖,祝宇虹,张秀峰,蔡鹤皋,机构影响系数与并联机器人雅克比矩阵的研究,哈尔滨工业大学学报,2002,34(6)：810-814.
[41]方跃法,黄真,三自由度并联机器人操作平台的主运动螺旋研究,中国机械工程,2000,11(7)：805-808.
[42]吴生富,王洪波,黄真,并联机器人工作空间研究,机器人,1990,13(3)：33-39.
[43]张源,崔建昆,卢强,罗玉华,λ型并联机床的工作空间分析,机械工程与自动化,2010,158：32-34.
[44]李方,南仁东,一种分析并联机器人工作空间的解析几何法,机械制造,2003,41(463)：17-19.
[45]刘旭东,黄田,3-TPT型并联机器人工作空间解析与综合,中国机械工程,2001,12(Suppelement):151-153.
[46]金爱娟,徐松源,孙华,模糊神经网络在机器人控制中的应用,哈尔滨理工大学学报,1998,3(5)：50-53.
[47]万亚民,王孙安,杜海峰,液压并联机器人的动态神经网络控制研究,西安交通大学学报,2004,38(9)：955-958.
[48]张曙,U.Heisel,并联运动机床,北京：机械工业出版社,2003.
[49]B. Dasgupta, T. S. Mruthyunjaya, The Stewart Platform Manipulator:a review, Mechanism and Machine Theory,2000,35:15-40.
[50]刘浩,高速轮轨接触状态可视化检测装置,西南交通大学硕士学位论文,成都,2005.
[51]M.A., Computational Stereo. Computing Surveys,1994,4:553-572.
[52]马颂德,张正友,计算机视觉——计算理论和算法基础,北京：科学出版社,1998.
[53]刘曙光,刘明远,何城,机器视觉及其应用,机械制造,2000,38(7)：20-22.
[54]D. G. Lowe, Three Dimensional Object Recognition From Single Two Dimensional Images, Aritificial Intelligence,1987,37:355-395.
[55]Aloimonos Y, Rosenfeld A, A response to "Ignorance, Myopia, and Naviete in computer vision systems" by Jain R C and Binford T O, Computer Vision Graphics and Image Processing,1991,53(1):120-124.
[56]Tsatsoulis C., Fu,K.S., A computer vision system for assembly inspection, Intelligent Robots and Computer Vision,1984,521:352-357.
[57]Gregory,P.J, Taylor,C.J., Knowledge-based models for computer vision, Proceedings of the 4th International Conference on Robot Vision and Sensory Control,1988,325-330.
[58]Liu J, Paulsen M R., Corn whiteness measurement and classification using machine vision, Transaction of American Society of Agricultural Engineers,2000,43(3):757-763.
[59]Schmidberger,E.J., Ahlers,R.J., Quality control with a robot-guided electro-optical sensor, 4th International Conference on Robot Vision and Sensory Controls,1984,27-36.
[60]Duan Feng, Wang Yaonan, A machine vision inspector for beer bottle, Applications of Artificial Intelligence,2007,32(1):1-9.
[61]Milan Sonka, Vaclav Hlavac, Image processing, Analysis, and Machine Vision, United States:Thomson Learning and PT Press,2002.
[62]Aly A. Faraga, M. Sabry Hassounaa, Ayman El-Baza, et al, Real-time vision-based approach for probe tracking in operating room, International Congress Series 1268,2004, 467-472.
[63]Reinhard Rinn, Michael Becker, Ralph Foehr, et al, Steel mill defect detection and classification at 3000 ft/mm using mainstream technology, Proceedings of SPIE,1998,3303: 20-26.
[64]http://www.ni.com/vision/.
[65]Clark F., Olson, Habib Abi-Rached, et al, Wide-Baseline Stereo Vision For Mars Rovers, Proceedings Of The 2003 IEEE/RSJ Intl. Conference On Intelligent Robots And Systems, 2003,1302-1307.
[66]Yajun Fang, Ichiro Masaki, Berthold Horn, Depth-Based Target Segmentation for Intelligent Vehicles:Fusion of Radar and Binocular Stereo, IEEE Transactions on Intelligent Transportation Systems,2002,3(3):196-202.
[67]Minoru Asada, Takamaro Tanaka, Visual Tracking Of Unknown Moving Object By Adaptive Binocular Visual Servoing, Proceeding of the 1999 IEEE International Conference on Multisensor Fusion and Intelligent Systems,1999,249-254.
[68]Kei Okada, Masayuki Inaba, Integration Of Real-Time Binocular Stereo Vision And whole Body Information For Dynamic Walking Navigation Of Humanoid Robot, IEEE Conference on Multisensor Fusion and Integration for Intelligent Systems,2003,131-136.
[69]Vallerand Steve, Kanbara Masayuki, Yokoya Naokazu, Binocular Vision-Based Augmented Reality System With An Increased Registration Depth Using Dynamic Correction Of Feature Positions, Proceedings of the 2003 IEEE Virtual Reality,2003, 22(26):271-272.
[70]张建荣,姜昱明,CCD成像在线测量玻璃管外径及壁厚方法的研究,应用光学,1995,25(3)：53-56.
[71]汪国有,熊佳,非接触式承载鞍磨损测量系统,红外与激光工程,2004,33(6)：669-674.
[72]杨敏,基于机器视觉的发动机气门杆直径及圆度检测研究,华南理工大学博士学位论文,广州,2004.
[73]郝颖明,董再励,刘百川,周静,非接触式机器人直线轨迹测量系统,机器人,2002,24(5)：394-398.
[74]应义斌,水果尺寸和面积的机器视觉检测方法研究,浙江大学学报(农业与生命学报),2000,26(3)：229-232.
[75]雷振山,刘兆妮,应用IMAQ Vision的齿轮测量技术,制造技术与机床,2005,2：53-56.
[76]罗振璧,郑力,汪劲松,张伯鹏,潘尚峰,唐晓红,机器视觉在尺寸在线检测中的应用,机械工程学报,1994,6：47-52.
[77]高媛媛,关胜晓,许栋,基于立体视觉运动速度检测系统的研究及实现,计算机工程与应用,2005,41(2)：45-53.
[78]马桂珍,湛海新,马丙辰,基于双目视觉的手术器械跟踪定位技术,微计算机应用,2005,26(2)：181-183.
[79]陈锡爱,徐方,基于手眼立体视觉的机器人定位系统,计算机应用,2005,25：302-304.
[80]杨耀权,施仁,高键年,基于光学三角原理的视觉检测系统及应用,华北电力大学学报,1998,28(3)：64-68.
[81]管业鹏,童林夙,陈娜,基于双目立体视觉的偏转线圈测量方法研究,电子学报,2003,31(9)：1382-1385.
[82]高庆吉,洪炳熔,阮玉峰,基于异构双目视觉的全自主足球机器人导航,哈尔滨工业大学学报,2003,35(9)：1029-1032,1088.
[83]董斌,杨韧,刘兴占,尤政,一种使用线阵型CCD实现高精度二维位置测量的方法,光学技术,1998,5：42-45.
[84]姜立军,甘东兵,王国荣,基于双目视觉传感的计算机辅助骨外科手术导航模型,华南理工大学学报(自然科学版),2006,34(2)：67-72.
[85]梁华为,直接从双峰直方图确定二值化阈值,模式识别与人工智能,2002,15(2)：253-256.
[86]刘安心,余跃庆,三维机器视觉测量系统的坐标计算模型研究,电气技术与自动化,2005,34(3)：71-73.
[87]陈建林,一种基于目标跟踪的并联机器人视觉检测研究,东华大学硕士学位论文,上海,2009.
[88]Chaumette F., Hu Tchinson S., Visual servo control part I:basic approaches, IEEE Robotics & Automation Magazine,2006(12):82-90.
[89]Chaumette F., Hu Tchinson S.,Visual servo control part Ⅱ:advanced approaches, IEEE Robotics & Automation Magazine,2007(3):109-118.
[90]王麟琨,徐德,谭民,机器人视觉伺服研究进展,机器人,2004,26(3)：277-282.
[91]赵清杰,连广宇,孙增圻,机器人视觉伺服综述,控制与决策,2001,16(6)：849-853.
[92]AiQiu Zuo, Q M Jonathan, et al, Stereo Vision Guided Control of a Stewart Platform, Proceedings of 2002 IEEE International Symposlum on Intelligent Control Vancouver, Canada,2002,125-130.
[93]吴笛飞,一种并联机器人位姿的视觉检测系统研究,东华大学硕士学位论文,上海,2008.
[94]雷成,吴福朝,胡占义,Kruppa方程与摄像机自标定,自动化学报,2001,27(5)：621-630.
[95]Zhengyou Zhang, A Flexible New Technique for Camera Calibration, IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(11):1330-1334.
[96]S. Birchfield, Elliptical Head Tracking Using Intensity Gradients and Color Histograms, Proc. IEEE conf. Computer Vision and Pattern Recognition,1998,232-237.
[97]H. Chen and T. Liu, Trust-Region Methods for Real-Time Tracking, Proc. Eigth Int'l Conf. Computer Vision,2001,2:717-722.
[98]徐瑞鑫,复杂背景下实时目标跟踪与识别研究,中国科学院长春光学精密机械与物理研究所硕士学位论文,长春,2002.
[99]Yu Zhong, Anil K. Jain, et al, Object Tracking using Deformable Templates, IEEE transactions on pattern analysis and machine intelligence,2000,22(5):544-549.
[100]A.K.Jain, YZhong, S.Lakshmanan, Object Matching Using Defonnable Templates, IEEE Trans. Pattern Analysis and Machine Intelligence,1996,18(3):267-278.
[101]Shoichi Araki, Takashi Matsuoaka, Naokazu Yokoya, et al. Real-time tracking of multiple moving object contours in a moving camera image sequence. IEICE Trans Inf & Syst, 2000,E83-D(7):1583-1591.
[102]Yun-Ting Lin, Yuh-Lin Chang, Tracking Deformable Objects with the Active Contour Model, Proceedings of IEEE International Conference on Multimedia Computing and Systems (ICMCS'97),1997,608-609.
[103]M.Bertalmio, GSapiro, and GRandall, MorphingActive Contours. Proc. Int'l Conf Scale-Space Theories in Computer Vision,1999,46-57.
[104]Vieren C, Cabestaing F, Postaire J., Catching Moving Objects with Snakes for Motion Tracking, Pattern Recognition Letters,1995,16:679-685.
[105]W J Rucklidge, Efficiently Locating Objects Using Hausdorff Distance, International Journal of Computer Vision,1997,24(3):251-270.
[106]J.Shi, C.Tomasi, Good Feature to track, Proceedings of IEEE Conference on Computer Vision and pattern Recognition,1994,593-600.
[107]Rainer Lienhart, Jochen Maydt, An Extended Set of Haar-like Features for Rapid Object Detection, IEEE2002international Conference on Image Processing, Rochester, 2002,900-903.
[108]Z.F.He, T.N.Tan, ZN.Sun, Topology modeling for Adaboost-cascade based object detection, Pattern Recognition Letters,2010,31(9):912-919.
[109]S.Z.Su, S.Y Chen, S.Z.Li, D.J. Duh, Structured local binary Haar pattern for pixel-based graphics retrieval, Electronics Letters,2010,46(14):996-997.
[110]T.S.Huang, A.N.Netravali, Motion and structure from feature correspondences:A review, Proc.IEEE,1994,82:252-268.
[111]郑南宁,计算机视觉与模式识别,北京：国防工业出版,1998.
[112]Mahmoud I Khalil, Mohamed M Bayoumi, A Dyadic Wavelet Affine Invariant Function for 2D Shape Recognition, IEEE Trans Pattern Analysis and Machine Intelligence, 2001,23(10):1152-1164.
[113]Dongjoe Shin, TardiTjahjadi, Clique descriptor of affine invariant regions for robust wide baseline image matching, Pattern Recognition,2010,43:3261-3272.
[114]Terry Y.F. Yuen, Peter W.M. Tsang, Affine invariant matching of broken boundaries based on particle swarm optimization, Image and Vision Computing,2008,26:1230-1239.
[115]A. Bandera, R. Marfil, E. Antunez, Affine-invariant contours recognition using an incremental hybrid learning approach, Pattern Recognition Letters,2009,30:1310-1320.
[116]S. Ponnusamy, J. Qiao, X. Wang, Schwarzian derivative, integral means, and the affine and linear invariant families of biharmonic mappings, Applied Mathematics and Computation,2010,216:3468-3479.
[117]罗荣华,闵华清,马鹏飞,柳雄,一种仿射不变特征及其在SLAM中的应用,华中科技大学学报(自然科学版),2008,36：159-162.
[118]毛玉星,韩倍涛,冯莲,王豪,徐少志,郝远洋,粒子群优化算法实现仿射不变性形状识别,重庆大学学报,2010,33(3)：65-70.
[119]程亮,龚健雅,韩鹏,宋小刚,遥感影像仿射不变特征匹配的自动优化,武汉大学学报(·信息科学版),2009,34(4)：418-422.
[120]汪文英,张冬明,张勇东,李锦涛,利用仿射变换的快速空间关系验证,计算机辅助设计与图形学学报,2010,22(4)：625-631.
[121]R. Horaud, B. Conio, O. Leboulleux, B. Lacolle, An analytic solution for the perspective 4-point problem, Computer Vision Graphics Image Process,1989,47(1):33-44.
[122]Z.-Y. Hu, F.-C.Wu, A note on the number of solutions of the noncoplanar p4p problem, IEEE Trans. Pattern Anal. Machine Intell.,2002,24(4):550-555.
[123]D. Nister, H. Stewenius, A minimal solution to the generalized 3-point pose problem, Math. Imaging Vision,2007,27(1):67-79.
[124]X.-S. Gao, J. Tang, On the probability of the number of solutions for the P4P problem, Math. Imaging Vision,2006,25(1):79-86.
[125]Xu, D., Li, Y.F., Tan, M., A general recursive linear method and unique solution pattern design for the perspective-n-point problem, Image and Vision Computing,2008,26:740-750.
[126]Hatem Hmam, Jijoong Kim, Optimal non-iterative pose estimation via convex relaxation, Image and Vision Computing,2010,28:1515-1523.
[127]D.G. Lowe, Fitting parameterized three-dimensional models to images, IEEE Trans. Pattern Analysis and Machine Intelligence,1991,13:441-450.
[128]L. J. QIN, F. Zhu, A novel algorithm for real-time pose determination and its iterative method, Proceedings of the 2006 IEEE International Conference on Information Acquisition,2006,26-30.
[129]E. Olson, J. Leonard, S. Teller, Fast Iterative Alignment of Pose Graphs with Poor Initial Estimates, Proceedings of the International Conference on Robotics and Automation,2006, 2262-2269.
[130]S.H. Or, W.S. Luk, K.H. Wong, et al, An Efficient Iterative Pose Estimation Algorithm, Image and Vision Computing,1998,16(5):353-362.
[131]裴巧娜,基于光流法的运动目标检测与跟踪技术,北方工业大学硕士学位论文,北京,2009.
[132]李喜来,李艾华,白向峰,智能交通系统运动车辆的光流法检测,光电技术应用,2010,25(2)：75-78.
[133]靳薇,张建奇,张翔,基于局部梯度算子的嘴部检测与定位,光电工程,2009,36(10)：135-140.
[134]曹阳,曲仕茹,张飞,马文佳,基于梯度统计和区域生长融合的运动车辆阴影检测方法,传感技术学报,2010,23(3)：413-417.
[135]鲜海滢,基于图像时空梯度的运动目标检测技术研究,电子科技大学博士学位论文,成都,2009.
[136]Qin Lijuan, Zhu Feng, A New Method for Pose Estimation from Line Correspondences, Acta Automatica Sinica,2008,34(2):130-134.
[137]Guanghui Wang, Jonathan Wua, Zhengqiao Ji, Single view based pose estimation from circle or parallel lines, Pattern Recognition Letters,2008,29:977-985.
[138]Pradit Mittrapiyanuruk, Guilherme N. DeSouza, Avinash C. Kak, Calculating the 3D-Pose of Rigid-Objects Using Active Appearance Models, IEEE International Conference in Robotics and Automation,2004,5147-5152.
[139]Jie Huang, Chunlei Zhao, Yoshitaka Ohtake, Huakang L, Qunfei Zhao, Robot Position Identification Using Specially Designed Landmarks, Instrumentation and Measurement Technology Conference Sorrento,2006,2091-2094.
[140]B. Stenger, A. Thayananthan, P.H.S. Torr, R. Cipolla, Estimating 3D hand pose using hierarchical multi-label classification, Image and Vision Computing,2007,25:1885-1894.
[141]Yasushi Sumi,Yutaka Ishiyama, Fumiaki Tomita, Robot-vision architecture for real-time 6-DOF object localization, Computer Vision and Image Understanding,2007,105:218-230.
[142]Khotanzad,A., Lion J., Recognition and pose estimation of unoccluded three-dimensional objects from a two-dimensional perspective view by banks of neural networks, IEEE Trans. Neural Networks,1996,7(4):897-906.
[143]S. Hati, S. Sengupta, Robust camera parameter estimation using genetic algorithm, Pattern Recognition Letters,2001,22:289-298.
[144]Hu, ZH, Ding, YS, An immune inspired co-evolutionary affinity network for prefetching of distributed object, Journal of Parallel and Distributed Computing,2010,70(2):92-100.
[145]Hu, ZH, Ding, YS, Yu, XK, et al., A hybrid neural network and immune algorithm approach for fit garment design, Textile Research Journal,2009,79(14):1319-1330.
[146]Hu, ZH, Ding, YS, Shao, Q, Immune co-evolutionary algorithm based partition balancing optimization for tobacco distribution system, Expert Systems with Aoolications,2009,36(3): 5248-5255.
[147]Liu, FC, Ren, LH, Ding, YS, A new mutual-coupled immune network based peer to peer network model with balanced tree structure, Intelligent Decision Making Systems,2010,2: 173-178.
[148]Liang, ZX, Ding, YS, Gu, Q, et al.,Optimizing Multicast Tree in NGN Based on Self-Adaptive Immune Algorithm, Asia-Pacific Conference on Information Processing, 2009,47-50.
[149]Xu, HQ, Ding, YS, Optimizing method for analog circuit design using adaptive immune genetic algorithm, Proceedings of the 4th international conference on frontier of computer science and technology,2009,359-363.
[150]Hu, ZH, Ding, YS, Lu, XJ, Cognitive immune system-based co-evolutionary information retrieval model, International Symposium on Intelligent Information Technology Application,2008,212-215.
[151]Ding, YS, Hu, ZH, Sun, HB, An antibody network inspired evolutionary framework for distributed object computing, Information Sciences,2008,178(24):4619-4631.
[152]K.C. Tan, C.K. Goh, A.A. Mamun, E.Z. Ei, An evolutionary artificial immune system for multi-objective optimization, European Journal of Operational Research,2008,187: 371-392.
[153]Maoguo Gong, Licheng Jiao, Xiangrong Zhang, A population-based artificial immune system for numerical optimization, Neurocomputing,2008,72:149-161.
[154]王磊,潘进,焦李成,免疫规划,计算机学报,2000,23(8)：806-812.
[155]D. Castro, Immune,Swarm and Evolutionary Algorithms Part I:Basic Models, In Proc. The 9th International Conference on Neural Information Processing,2002,1464-1468.
[156]D. Dasgupta, Artificial Immune System and Their Application, Berlin:Springer-Verlag, 1998.
[157]D. Dasgupta, S. Forrest, Novelty Detection in Time Series Data Using Ideas From Immuneology, In Proc. the 5th International Conference on Intelligent Systems,1996, 19-21.
[158]S. Forrest, A. S. Perelson, L. Allen, et al. Self-Nonself Discrimination in a Computer, In Proc.1994 IEEE Symposium on Research in Security and Privacy,1994,202-212.
[159]J. Kim, P. J. Bentley, Towards an Artificial Immune System for Network Intrusion Detection:an Investigation of Clonal Selection with a Negative Selection Operator, In Proc. the 2001 Congress on Evolutionary Computation,2001,2:1244-1252.
[160]D. Dasgupta, S. Yu, N. Majumdar., MILA--Multi-Level Immune Learning Algorithm and its Application to Anomaly Detection, The Soft Computing Journal,2005,9(3):172-184.
[161]G. Du, T. Huang, B. Zhao, and L. Song, Dynamic Self-Defined Immunity Model Base on Data Mining for Network Intrusion Detection, In Proc.4th International Conference on Machine Learning and Cybernetics,2005,6:3866-3870.
[162]A. Ishiguro, T. Kondo, Y. Shirai,et al, Immunoid:An Architecture for Behavior Arbitration Based on the Immune Networks, In Proc. The 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems,1996,1730-1738.
[163]J. E. Hunt, D. E. Cooke, Learning Using an Artificial Immune System, Journal of Network and Computer Applications,1996,19(2):189-212.
[164]D. F. McCoy,V Devarajan, Artificial Immune Systems and Aerial Image Segmentation, In Proc. IEEE International Conference on Systems, Man, and Cybernetics, 1997,1:867-872.
[165]J. Timmis, Artificial Immune Systems:a Novel Data Analysis Technique Inspired by the Immune Network Theory, PhD thesis, Department of Computer Science, University of Wales, Aberystwyth. Ceredigion. Wales,2000.
[166]D. Castro, F. J. V. Zuben, An Evolutionary Immune Network for Data Clustering, In Proc. the Sixth Brazilian Symposium on Neural Networks,2000,84-89.
[167]Y Ishida, Active Diagnosis by an Immunity-Based Agent Approach, In Proc. the 7th International Workshop on Principles of Diagnosis,1996,106-114.
[168]Y Ishida, Fully Distributed Diagnosis by PDP Learning Algorithm:Towards Immune Network PDP Model, In Proc. the International Joint Conference on Neural Networks, 1990,777-782.
[169]Jiaquan Gao, Jun Wang, WBMOAIS:A novel artificial immune system for multi-objective optimization, Computers & Operations Research,2010,37(1):50-61.
[170]Y.-C. Hsieh, Y.-C. Lee, P.-S. You, T.-C. Chen, An immune based two-phase approach for the multiple-type surveillance camera location problem, Expert Systems with Applications, 2009,36(7):10634-10639.
[171]V.P. Sakthivel, R. Bhuvaneswari, S. Subramanian, Artificial immune system for parameter estimation of induction motor, Expert Systems with Applications,2010,37(8):6109-6115.
[172]Ilhan Aydin, Mehmet Karakose. Erhan Akin, Artificial immune classifier with swarm learning, Engineering Applications of Artificial Intelligence, (In Press).
[173]Shuaiqiang Wang, Jun Ma, Qiang He, An immune programming-based ranking function discovery approach for effective information retrieval, Expert Systems with Applications, 2010,37(8):5863-5871.
[174]Jianyong Chen, Qiuzhen Lin, Zhen Ji, A hybrid immune multi-objective optimization algorithm, European Journal of Operational Research,2010,204:294-302.
[175]莫宏伟,人工免疫系统原理与应用,哈尔滨：哈尔滨工业大学出版社,2002.
[176]肖人彬,王磊,人工免疫系统：原理、模型、分析及展望,计算机学报,2002,25(12)：1281-1293.
[177]Cutello V, Nicosia C, Pavone M, et al. An Immune Algorithm for Protein Structure Prediction on Lattice Models, IEEE Transactions on Evolutionary Computation,2007,11(1): 101-117.
[178]葛动元,姚锡凡,李凯南,双目立体视觉系统的标定,机械设计与制造,2010,6：188-189.
[179]魏振忠,樊妍睿,张广军,光栅式双目立体视觉传感器的立体匹配方法,红外与激光工程,2010,39(2)：330-334.
[180]杜志军,王阳生,利用主动外观模型合成动态人脸表情,计算机辅助设计与图形学学报,2009,21(11)：1590-1594.
[181]呼月宁,张艳宁,朱宇,崔瑞,AAM在多姿态人脸特征点检测中的应用,计算机工程与应用,2010,46(12)：161-165.
[182]Jason Saragih, RolandGocke, Learning AAM fitting through simulation, Pattern Recognition,2009,42:2628-2636.
[183]范小九,彭强,Jim X Chen,利用主动外观模型进行3D人脸的快速自动建模,计算机辅助设计与图形学学报,2009,21(5)：668-673.
[184]张宇辉,吕国强,胡跃辉,刘文文,立体显示的双目模型算法及实现,计算机工程与应用,2006,42(35)：65-67.
[185]任翠花,多视图匹配算法研究,哈尔滨工业大学硕士学位论文,哈尔滨,2008.
[186]P. Tresadern, H. Bhaskar, S. Adeshina, C. Taylor, T.F. Cootes, Combining Local and Global Shape Models for Deformable Object Matching, Proc. BMVC09,2009,1-12.
[187]张淑平,丁永生,郝矿荣,吴笛飞,基于立体视觉的六自由度大载荷试验平台的位姿检测,计算机应用研究,2008,25(6)：1744-1746.