基于计算机视觉的大尺度三维几何尺寸测量方法及应用
|
摘要
大尺度三维几何尺寸测量技术可以对外形不规则的大型物体进行几何量的精确测量。随着计算机技术与数字图像处理技术的发展,近年来集计算机视觉技术、数字图像处理技术以及光学技术于一身的基于计算机视觉的大尺度三维几何尺寸测量方法已经在质量检测、虚拟现实、逆向工程以及工业制造等领域得到了广泛的应用。但目前的技术在解决室外环境下大量程高速高精度便携式测量应用方面仍存在不足。本文在分析研究现有相关技术与文献的基础上,着重对测量系统的结构设计与软件算法进行研究,得到了一种具有快速、精确、便携以及大量程特点的新型测量方法。
论文首先对与成像质量最为相关的照明、镜头以及图像传感器进行了分析和研究,建立了视觉传感器的数学模型并分析了测量误差与传感器结构参数之间的关系,为测量装置的设计提供了依据。论文对计算机视觉测量中的特征提取算法进行了深入地研究,对现有的角点、光条提取算法进行了总结和对比,并分别基于参数自适应以及相位一致性分析给出了角点和光条的提取算法,实现了在现场环境下对图像特征的稳定提取。并设计了自动恢复算法,实现了对离焦模糊图像的恢复。实验结果显示该算法对离焦模糊图像的恢复质量良好,可以有效增强图像特征。
视觉传感器参数标定是三维视觉测量中的重要环节,其中对于线结构光传感器的参数标定,传统的标定方法需要昂贵的设备,效率低下,步骤复杂。本文提出了一种便捷的三维视觉系统结构光平面标定方法。该方法仅使用一个简单的二维平面靶标,在保证靶标与结构光相交的前提下,只需要将靶标在摄像机可视范围内自由移动数个位置即可完成标定过程。实验证明该方法效率高,步骤简单,具有很好的通用性。
为了解决双目立体匹配中二维搜索空间带来的不稳定性,并进一步提高双目匹配的精度,本文对于基于极线约束与结构光光条约束的亚像素级精度的匹配算法进行了探讨,并提出了一种基于局部曲线差值的高精度匹配算法,实现了对图像特征点的高精度立体匹配。
基于本文的理论研究结果研制了一种基于大尺度三维几何尺寸测量的货运列车超限自动检测系统并制作了样机。现场测试结果显示,该系统可以高速准确地完成对货运列车的自动超限检测。其测量的均方误差在10m的测量距离处为3mm,对于单节列车的测量时间不超过30分钟。上海铁路局的科技成果鉴定认为：该项技术达到了路内(国内)先进水平。
Large-scale 3D geometric measurement is an effective and flexible method for precise space coordinates measurement when the object's shape is irregular and the size is relatively large. In recent years, with the evolution in computer technologies and digital image processing, many computer vision based large-scale 3D geometric measurement methods incorporating computer vision technology, digital image processing technology and optics have been developed, which have found wide applications in quality control, virtual reality, reverse engineering and manufacturing. However, these existing methods have limitations when dealing with the portable measurement of large measuring range, high speed and high accuracy under external situation. In this paper, the previously research works and literatures of 3D geometric measurement are analyzed. A novel measurement method with portability, high speed, high accuracy and large measuring range is proposed aims the existing methods'shortages.
This dissertation firstly discussed the illumination, lens and image sensor, which are the most relevant factors to the image quality. The mathematical model of the vision sensor as well as the relationship between the sensor's structure and the measurement error were established. The design rules for measurement equipment were provided.
The algorithms for feature extraction have been studied in details. The existing corner and light stripe extraction methods have been summarized and compared. Self-adaptive corner detection method and phase congruency based stripe extraction method were proposed which can work steadily in outdoor situation. A Wiener filter based algorithm for defocus blurred image restoration was presented. The experiment results indicate that the new method effectively reduces the ringing effects and restores the defocus blurred image well.
The calibration of sensor is the key technique in 3D vision measurement. For structured light sensor, the traditional methods require expensive equipments and complex procedure. Beside, those methods'efficiency is quite low. In our method, a simple 2D planar target is used. The target can be freely moved to different positions in the visible space of the camera if only the target and the structured light plane can intersect to each other. The experiment results show that the proposed method is a universal method with high efficiency and simple procedure.
For stereo vision, the search space for matching one point in the left image to its correspondence in the right image is two dimensional which makes the matching quit fallible. In this paper, epipolar constraint and structured light stripe constraint which can make the corresponding points matching become a one to one situation is used. Although similar constraint has been reported by previous literates, the algorithms in these literates only offer pix level accuracy, which prevent a high overall accuracy of the final measurement. We have proposed a corresponding points matching algorithm based on local curve intersection, which provides sub-pixel level accuracy.
Finally, an automatic freight train gauge-exceeding detection method based on large scale 3D geometric measurement was presented. A prototype has been developed and the experimental results have shown that the proposed method can detect freight train gauge-exceeding with high accuracy and speed. The mean square error is about 3mm when the measuring range is 10m. The measurement time for one train is within half an hour. The prototype has passed the acceptance of Shanghai railway administration. The appraisal result proved that our technology has reached the leading domestic level.
论文首先对与成像质量最为相关的照明、镜头以及图像传感器进行了分析和研究,建立了视觉传感器的数学模型并分析了测量误差与传感器结构参数之间的关系,为测量装置的设计提供了依据。论文对计算机视觉测量中的特征提取算法进行了深入地研究,对现有的角点、光条提取算法进行了总结和对比,并分别基于参数自适应以及相位一致性分析给出了角点和光条的提取算法,实现了在现场环境下对图像特征的稳定提取。并设计了自动恢复算法,实现了对离焦模糊图像的恢复。实验结果显示该算法对离焦模糊图像的恢复质量良好,可以有效增强图像特征。
视觉传感器参数标定是三维视觉测量中的重要环节,其中对于线结构光传感器的参数标定,传统的标定方法需要昂贵的设备,效率低下,步骤复杂。本文提出了一种便捷的三维视觉系统结构光平面标定方法。该方法仅使用一个简单的二维平面靶标,在保证靶标与结构光相交的前提下,只需要将靶标在摄像机可视范围内自由移动数个位置即可完成标定过程。实验证明该方法效率高,步骤简单,具有很好的通用性。
为了解决双目立体匹配中二维搜索空间带来的不稳定性,并进一步提高双目匹配的精度,本文对于基于极线约束与结构光光条约束的亚像素级精度的匹配算法进行了探讨,并提出了一种基于局部曲线差值的高精度匹配算法,实现了对图像特征点的高精度立体匹配。
基于本文的理论研究结果研制了一种基于大尺度三维几何尺寸测量的货运列车超限自动检测系统并制作了样机。现场测试结果显示,该系统可以高速准确地完成对货运列车的自动超限检测。其测量的均方误差在10m的测量距离处为3mm,对于单节列车的测量时间不超过30分钟。上海铁路局的科技成果鉴定认为：该项技术达到了路内(国内)先进水平。
Large-scale 3D geometric measurement is an effective and flexible method for precise space coordinates measurement when the object's shape is irregular and the size is relatively large. In recent years, with the evolution in computer technologies and digital image processing, many computer vision based large-scale 3D geometric measurement methods incorporating computer vision technology, digital image processing technology and optics have been developed, which have found wide applications in quality control, virtual reality, reverse engineering and manufacturing. However, these existing methods have limitations when dealing with the portable measurement of large measuring range, high speed and high accuracy under external situation. In this paper, the previously research works and literatures of 3D geometric measurement are analyzed. A novel measurement method with portability, high speed, high accuracy and large measuring range is proposed aims the existing methods'shortages.
This dissertation firstly discussed the illumination, lens and image sensor, which are the most relevant factors to the image quality. The mathematical model of the vision sensor as well as the relationship between the sensor's structure and the measurement error were established. The design rules for measurement equipment were provided.
The algorithms for feature extraction have been studied in details. The existing corner and light stripe extraction methods have been summarized and compared. Self-adaptive corner detection method and phase congruency based stripe extraction method were proposed which can work steadily in outdoor situation. A Wiener filter based algorithm for defocus blurred image restoration was presented. The experiment results indicate that the new method effectively reduces the ringing effects and restores the defocus blurred image well.
The calibration of sensor is the key technique in 3D vision measurement. For structured light sensor, the traditional methods require expensive equipments and complex procedure. Beside, those methods'efficiency is quite low. In our method, a simple 2D planar target is used. The target can be freely moved to different positions in the visible space of the camera if only the target and the structured light plane can intersect to each other. The experiment results show that the proposed method is a universal method with high efficiency and simple procedure.
For stereo vision, the search space for matching one point in the left image to its correspondence in the right image is two dimensional which makes the matching quit fallible. In this paper, epipolar constraint and structured light stripe constraint which can make the corresponding points matching become a one to one situation is used. Although similar constraint has been reported by previous literates, the algorithms in these literates only offer pix level accuracy, which prevent a high overall accuracy of the final measurement. We have proposed a corresponding points matching algorithm based on local curve intersection, which provides sub-pixel level accuracy.
Finally, an automatic freight train gauge-exceeding detection method based on large scale 3D geometric measurement was presented. A prototype has been developed and the experimental results have shown that the proposed method can detect freight train gauge-exceeding with high accuracy and speed. The mean square error is about 3mm when the measuring range is 10m. The measurement time for one train is within half an hour. The prototype has passed the acceptance of Shanghai railway administration. The appraisal result proved that our technology has reached the leading domestic level.
引文
[1]王永强,基于约束算法的大尺寸三维视觉测量方法研究[D],北京邮电大学电子工程学院,2006.11
[2]韩庆龙,大尺寸截面配对式视觉测量方法的研究[D],天津大学精密仪器与光电子工程学院,2008.6
[3]吴斌,孙长钦,杨兵等,结构光传感器在柔性视觉测量系统中的应用研究[J],光电子·激光,2008,19(10)：1309-1311
[4]吕乃光,邓文怡,大型机械零部件孔同轴度测量技术研究[J],光电子·激光,2001,12(7)：694-696
[5]Wen Gewang, Zhang yihu, Zong Yushun,3-D shape measurement based computer vision[C], International Conference on Machine Learning and Cybernetics,2002:910-914.
[6]Quan Shi, Ning Xi, and Yifan Chen, Development of an automatic optical measurement system for automotive part surface Inspection[C], International Conference on Advanced Intelligent Mechatronics,2005:1557-1562.
[7]Ryo Kurazume, Yukihiro Tobata, Yumi Iwashita, and Tsutomu Hasegawa,3D laser measurement system for larger scale architectures using multiple mobile robot[C]s, International Conference on 3-D Digital Imaging and Modeling,2007:91-98.
[8]Hirofumi Nakai, Daisuke Iwai, Kosuke Sato,3D shape measurement using fixed camera and handheld laser scanner[C], SICE Annual Conference,2008:1536-1539.
[9]H.Yasser, Laser based system for reverse engineering[J], Computers and Engineering,1994, 26(2):387-394
[10]Mohammad Amini Tehrani, Ali Saghaeian, Omid Reza Mohajerani, A new approach to 3D modeling using structured light pattern[C], International Conference on Information and Communication Technologies,2008:1-5.
[11]刘晓,吴训威,破损古文物的计算机三维重构[J],电子与信息学报,2001,23(7)650-655
[12]Zhanli Li, Yuan Wang, Research on 3D reconstruction procedure of marked points for large workpiece measurement[C], International Conference on Information Assurance and Security,2009:273-276.
[13]刘建伟,梁晋,梁新合,等,大尺寸工业视觉测量系统[J],光学精密工程,2010,18(1)：126-134
[14]孙军华,张广军,魏振忠,等,大型自由曲面移动式三维视觉测量系统[J],仪器仪表学报,2006,27(12)：1689-1691
[15]张顺德,卢秉恒,丁玉成,光学三维型面分区域测量数据的拼接研究[J],中国激光,2001,28(6)：533-536
[16]Carbone. V, Carocci. M, Savio.E, etal, Combination of a vision system and a coordinate measuring machine for the reverse engineering of freeform surfaces[J], International Journal of Advanced Manufacturing Technology,2001,17(4):263-271
[17]Vaady. T, Martin. R. R, Cox. J, Reverse engineering of geometric models--an introduction[J], Computer Aided Design,1997,29(4):255-268
[18]龙玺,钟约先,李仁举,等,结构光三维扫描测量的三维拼接技术[J],清华大学学报,2002,42(4)：477-480
[19]Pancewicz. T, Kujawinska. M, CAD/CAM/CAE representation of 3D objects measured by fringe projection[J], SPIE laser Interferometry,1998,3479 (7):70-75
[20]Sansoni. G, Carocci.M, Rodella. R. Calibration and performance evaluation of a 3D imaging sensor based on the projection of structured light[J], IEEE Transactions on Instrumentation and Measurement,2000,49(3):628-636
[21]Reich. C, Ritter. R, Thesing. J,3D shape measurement of complex objects by combining photogrammetry and fringe projector[J], Optical Engineering,2000,39(1):224-231
[22]Hung. Y. Y, Lin. L, Park. B. G, Practical 3D computer vision techniques for full-field surface measurement[J]. Optical Engineering,2000,39(1):143-149
[23]任同群,大型3D形貌测量高精度拼接方法与技术研究[D],天津大学精密仪器与光电子工程学院：2008.1
[24]王志凌,基于双CCD的大锻件尺寸测量的实验研究[D],燕山大学电气工程学院,2005.10
[25]Hermann. G, Design considerations for a modular high precision 3D coordinate measuring machine[C], International Conference on Mechatronics,2006:161-165.
[26]周维虎,李百源,兰一兵,等,经纬仪坐标测量系统检测方法的研究[J],航空计测技术, 2000,20(1)：6-9
[27]Zhigang Liu, Yaozhong Xu, Zhongzheng, et al, A large scale 3D positioning method based on a network of rotating laser automatic theodolites[C], International Conference on Information and Automation,2010:513-518.
[28]Bouvet. D, Garcia. G, Gorham. B. J, et al, Precise 3-D localization by automatic laser theodolite and odometer for civil-engineering machines [C], International Conference on Robotics and Automation,2001:2045-2050.
[29]李广云,非正交系坐标测量系统原理及进展[J],测绘信息与工程,2003,28(1)：4-10
[30]Zongqian Zhan, Zuxun Zhang, Jianqing Zhang, An integrated photogrammetric system with metric digital camera and total station[C], International Multi-symposiums on Computer and Computational Sciences,2006:747-752.
[31]Guanbin Gao, Wen Wang, Keng Lin, et al, Kinematic calibration for articulated arm coordinate measuring machines base on particle swarm optimization[C], International Conference on Intelligent Computation Technology and Automation,2009:189-192
[32]Guanbin Gao, Wen Wang, Keng Lin, et al, Structual parameter identification for articulated arm coordinate measuring machines[C], International Conference on Intelligent Computation Technology and Automation,2009:128-131.
[33]Hori. T, Nishida. Y. J, Improvement of position estimation of the ultrasonic 3D tag system[C], International Symposium on Robot and Human Interactive Communication, 2008:436-441.
[34]秦世伟,谷川,Indoor GPS技术及其在工业领域中的应用[J],铁道勘察,2008,24(3)：31-34
[35]陈翔鹏何志英,数字化在线测量技术在飞机装配中应用[J],航空与航天,2008,12(4)：20-23
[36]周春艳,激光跟踪坐标测量系统的双线法基点自标定[J],科学技术与工程,2009,9(12)：3429-3431
[37]孙长库,叶声华,激光测量技术[M],天津：天津大学出版社,2001
[38]J. S. Massa, G. S. Buller, A. C. Walker, et al, Time of flight optical ranging system based on time correlated single photon counting[J], Appl. Opt.1998,37(31):7298-7304.
[39]B. Nilsson, T. E. Carlsson, Direct three dimensional shape measurement by digital light-in-flight holography [J], Appl. Opt.1998,37(34):7954-7959.
[40]王润田,双频超声波测距[J],声学技术,1996,15(3)：116-118
[41]张维,漆新民,李坚等,一种新型测距仪的研制[J],航空计测技术,1998,18(6)：27-29
[42]张广军,视觉测量[M],北京：科学出版社,2008
[43]Y. Hu, J. Xi, E. Li, J. Chicharo, et al, Three-dimensional profilometry based on shift estimation of projected fringe patterns[J], Appl. Opt.2006,45(4):678-687.
[44]T. Baumbach, W. Osten, C. Kopylow, et al, Remote metrology by comparative digital holography [J], Appl. Opt.2006,45(5):925-934.
[45]D. Purcell, A. Davies, F. Farahi, Effective wavelength calibration for moire fringe projection [J], Appl. Opt.2006,45(34):8629-8635.
[46]Da. Feipeng, Gai. Shaoyan, Flexible three-dimensional measurement technique based on a digital light processing projector [J], Appl. Opt.2008,47(3):377-385.
[47]Yongchang Wang, Daniel L. Lau, Laurence G. Hassebrook, Fit-sphere unwrapping and performance analysis of 3D fingerprints [J], Appl. Opt.2010,49(4):592-600.
[48]Wang. Zhangyang, Du. Hua, Park. Seungbae, et al, Three-dimensional shape measurement with a fast and accurate approach [J], Appl. Opt.2009,48(6):1052-1061.
[49]Roberts. L. G, Machine perception of three-dimensional solids, In Optical and Electro-optical Information Processing [M]. Cambridge:MIT Press,1965:7-24
[50]Guzman. A, Decomposition of a visual scene into three-dimensional bodies[D], In automatic Interpretation and Classification of Images. New York:Academic Press,1969: 15-58
[51]贾云得,机器视觉[M],北京：科学出版社,2000：1-13
[52]伍雪东,计算机视觉中摄像机定标及位姿和运动估计方法的研究[D],湖南大学电气与信息工程学院,2004：4.
[53]Zhanli Li, Yuan Wang, Research on 3D reconstruction procedure of marked points for large workpiece measurement[C], International Conference on Information Assurance and Security,2009:273-276.
[54]Wen Gewang, Zhang yihu, Zong Yushun,3-D shape measurement based computer vision[C], International Conference on Machine Learning and Cybernetics,2002:910-914.
[55]Ryo Kurazume, Yukihiro Tobata, Yumi Iwashita, and Tsutomu Hasegawa,3D laser measurement system for larger scale architectures using multiple mobile robots[C], International Conference on 3-D Digital Imaging and Modeling,2007:91-98.
[56]Richard. K, Adaptive optical three-dimensional measurement with structured light[J]. Optical Engineering,39(1),2000:150-158.
[57]Kurtis. K, Jeremy. A, Real-time structured light depth extraction[J], Three-dimensional Image Capture and Applications, Proc.SPIE,3958,2000:11-18.
[58]刘维一,王肇忻,母国光,等,彩色编码投影光栅轮廓术中分色问题的探究[J],光学学报,2001,21(4)：454-458.
[59]周富强,李章,张广军,基于三维测量扫描线点云的表面重建[J],仪器仪表学报,2006,27(6)：619-623.
[60]谢凯,刘宛予,浦昭邦,结构光三维视觉检测系统的标定方法研究[J],光电子·激光,2007,18(3)：369-371
[61]孙长钦,基于多光条结构光视觉传感器的大尺寸园钢测量技术研究[D],天津大学精密仪器与光电子工程学院,2008,8
[62]黄桂平,钦桂勤,卢成静,数字近景摄影大尺寸三坐标测量系统V-STARS的测试与应用[J],宇航计测技术,2009,29(2)：5-22
[63]武坚,郝春生,马永刚,等,数字图像融合技术在摄影测量中的应用[J],海洋测绘,2008,28(4)：73-75
[64]Yan.K.Q, Cheng.T, Close shot photogrammetry for measuring winddrifted snow distribution on stepped flat roofs[C], IEEE International Colloquium on Computing, Communication, Control and Management,2008,1:332-335.
[65]Rudin. L, Monasse. P, Ping. Y, Epipolar photogrammetry:a novel method for forensic image comparison and measurement[C], IEEE International Conference on Image Processing,2005,3:385-388
[66]Radu. H, Gabriella. C, David. D, Stereo calibration from rigid motions. IEEE Transactions on Pattern Analysis and Machine Intelligence.2000,22(12):1446-1452
[67]Horn.B.K.P, Brooks.M.J, Shape from Shading[M],Cambridge MA, MIT Press,1989
[68]Zhang. R, Tsai. P. S, Cryer. E, Shape form shading:a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence [J],1999,21(8):690-706.
[69]杨少荣,吴迪靖,段德山,机器视觉算法与应用[M],北京：清华大学出版社,2008
[70]叶海加,陈罡,邢渊,双目CCD结构光三维测量系统中的立体匹配[J],光学精密工程,2004,12(1)：72-75
[71]陶立,孙长库,何丽,等,基于结构光扫描的彩色三维信息测量技术[J],光电子·激光,2006,17(1)：111-114
[72]胡勇,王从军,韩明,等,基于计算机视觉的三维激光扫描测量系统[J],华中科技大学学报：自然科学版,2004,32(1)：16-18
[73]冈萨雷斯,数字图像处理[M].北京：电子工业出版社,2006.
[74]Born. M, Wolf. W, Principles of optics[M], Cambridge:Cambridge University Press,7th, 1999
[75]Weng. J. W, Cohen. P, Herniou.M, Camera calibration with distortion models and accuracy evaluation[J], Transactions on Pattern Analysis and Machine Intelligence,1992,14(10): 965-980
[76]Holst. G. C, CCD arrays, cameras, and displays [M]. SPIE Press, Bellingham, WA,2nd, 1998
[77]Gamal. A. Er, Eltoukhy. H, CMOS image sensor. IEEE Circuits and Devices Magazine[J], 2005,21(3):6-20
[78]Miida. T, A 1.5Mpixel imager with localized hole-modulation method[C], ISSCC 2002: 42-43
[79]陈榕庭,彭美桂,CCD/CMOS图像传感器基础与应用[M],北京：科学出版社,2006
[80]Alleysson. D, Susstrunk. S, Herault.J, Linear demosaicing inspired by the human visual system [J]. IEEE Transactions on Image Processing,2005,14(4):439-449.
[81]Hirakawa.K, Parks. T. W, Adaptive homogeneity-directed demosaicing algorithm. IEEE Transactions on Image Processing,2005,14(3):360-369
[82]Zhang.Y.X, Wang. S, Zhang. X. P, etl, Motion blurred image restoration algorithm for IC wire bonder[C], Proceedings of SPIE-The International Society for Optical Engineering, 2009,7513
[83]董寅,张益听,王顺,张旭苹,布里渊光时域反射传感系统数据采集模块的研制[J].光电子技术.2007,27(2)：88-91
[84]Hartley. R, Zisserman. A, Multiple view geometry in computer vision[M], Cambridge: Cambridge University Press,2nd,2003
[85]ZHANG. Z. Y, Determining the epipolar geometry and its uncertainty:a review[J], International Journal of Computer Vision,1998,27(2):161-198.
[86]ZHANG. Z. Y, A flexible new technique for camera calibration[C], Seventh International Conference on Computer Vision (ICCV99),1999:666-673.
[87]TSAI. R. Y, A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV camera and lenses[J], IEEE Journal of Robotics and Automation,1987,3(4):323-344.
[88]卞晓东,基于机器视觉的车辆几何尺寸测量系统研究[D],东南大学,2005.
[89]Ren. Zhiguo, Cai. Lilong, Three-dimensional structure measurement of diamond crowns based on stereo vision [J], Appl. Opt.2009,48(31):5917-5932.
[90]W. S. Kim, A. I. Ansar, R. D. Steele, et al, Performance analysis and validation of a stereo vision system [C], IEEE International Conference on Systems, Man and Cybernetics,2005,2: 1409-1416.
[91]C. C. Chang, S. Chatterjee, P. R. Kube, A quantization error analysis for convergent stereo [C], IEEE International Conference on Image Processing,1994,2:735-739.
[92]苏宇,基于特征点的图像拼接技术研究[D],西安电子科技大学,2008
[93]Wang. H, Brady. M. Real time corner detection algorithm for motion estimation[J], Image and Vision Computing,1995,13(9):695-703
[94]Trajkovicm, Hedley.M, Fast corner detection[J], Image and Vision Computing,1998,16(1): 75-87
[95]Bretschi. J, Automated inspection systems for industry[J]. Electronics Letters,1996,8(12): 1695-1697
[96]Kithenl, Roseenfeld.A.K. Gray-level corner detection[J]. Pattern Recognition Letters,1982, 3(1):95-102.
[97]Mokhtarian. F, Suomela. R, Robust image corner detection through curvature scale space [J]. IEEE Trans. On Pattern Analysis and Machine Intelligence,1998,20(12):1376-1381.
[98]Smith, S.M., Brady, J.M, SUSAN-a new approach to low level image processing [J], International Journal of Computer Vision 23,1997:45-78
[99]Harris, C., Stephens, M, A combined corner and edge detector [C]. In:Proceedings,4th Alvey Vision Conference[C],1988:147-151.
[100]Schmid. C, Mohr. R, Bauckhage. C, Evaluation of interest point detectors [J], International Journal of Computer Vision,2000,37(2):151-172.
[101]张春森,基于点特征匹配的SUSAN, Harris算子比较[J],西安科技大学学报,2007,27(4)：608-616.
[102]张春美,龚志辉,黄艳,几种特征点提取算法的性能评估及改进[J],测绘科学技术学报,2008,5(3)：231-234
[103]Zhang. J. Y, Chen. Q, Bai. X. J, etl, An advanced Harris-Laplace feature detector with high repeatability[C], International Congress on Image and Signal Processing,2009:1-5
[104]Guo. C. G, Li. X. L, Zhong. L. F, A fast and accurate corner detector based on Harris algorithm[C], International Symposium on Intelligent Information Technology Application, 2009,2:49
[105]王玉珠,杨丹,张小洪,基于B样条的改进型Harris角点检测算法[J],计算机应用研究,2007,2：192-194
[106]陈白帆,蔡自兴,基于尺度空间理论的Harris角点检测[J],中南大学学报(自然科学版),2005,36(5)：751-754
[107]王德超,涂亚庆,张铁楠,一种多尺度Harris角点检测方法[J],计算机应用与软件,2008,25(12)：242-262
[108]李薇薇,段智敏,一种改进型的Harris角点检测算法[J],机械工程与自动化,2009,2(1)：19-23
[109]毛雁明,兰美辉,王运琼,冯乔生,一种改进的基于Harris的角点检测方法[J]，2009,19(5)：130-133
[110]张小洪,李博,杨丹,一种新的Harris多尺度角点检测[J],电子与信息学报,2007,29(7)：1735-1738
[111]赵万金,龚声蓉,刘纯平,沈项军,一种自适应的Harris角点检测算法[J],计算机工程,2008,34(10)：212-217
[112]王耀贵,图像高斯平滑滤波分析[J],计算机与信息技术,2008,8：79-90
[113]Son. S, Park. H, Lee. K. H, Automated laser scanning system for reverse engineering and inspection [J], International Journal of Machine Tools & Manufacture,2002,42(8),889-897.
[114]Trinh. N. H, Laster. J, Fleming. B. C, etl, Accurate measurement of cartilage morphology using a 3D laser scanner[J], Computer Vision Approaches to Medical Image.2006,4241: 37-48.
[115]Wang. C. C, Shyue. S.W, Cheng. S. H, Underwater_structure_inspection_with_laser_light stripes[C], Proceedings of the 2000 International Symposium,2000:201-205
[116]Xie. X. Z, Wang. J.G, Jin. M, Study on a full field of view laser scanning system[J], International Journal of Machine Tools&Manufacture,2007,47:33-43.
[117]张舜德,卢秉恒,丁玉成,高斯光束柱面反射展成法生成光刀的研究[J],中国激光,2001,28(2)：133-136
[118]崔希君,杨川,刘保华,等,线性结构光心的自适应迭代提取法[J],西安交通大学学报,2007,41(1)：73-76.
[119]熊会元,宗志坚,高群,等,精确提取线结构光条纹中心方法[J],计算机工程与应用,2009,45(1)：235-237.
[120]胡斌,李德华,金刚,等,基于方向模板的结构光条文中心检测方法[J],计算机工程与应用,2002,11：59-109
[121]胡坤,周富强,张广军,一种快速结构光条纹中心亚像素精度提取方法[J],仪器仪表学报,2006,27(10)：1326-1329
[122]贺俊吉,张广军,结构光三维视觉检测中光条图像处理方法研究[J],北京航空航天大学学报,2003,29(7)：593-597
[123]周富强,陈强,张广军,结构光光条提取的混合图像处理方法[J],2008,19(11)：1534-1537
[124]周富强,王飞,张广军,结构光直线光条图像特征的三步法提取[J],2008,44(11)：215-219
[125]Steger C. An unbiased detector of curvilinear structures[J], IEEE Transactions on Pattern Analysis and machine Intelligence 20,1998:113-125.
[126]Kovesi, P, Symmetry and asymmetry from local phase[C], Tenth Australian Joint Conference on Artificial Intelligence,1997:185-190.
[127]Kovesi, P, Phase congruency detects corners and edges, The Australian Pattern Recognition Society Conference,2003:309-318
[128]Field. D. J, Relations between the statistics of natural images and the response properties of cortical cells [J], Journal of The Optical Society of America,1987,4(12):2379-2394.
[129]Wu. C. Z, Wang. Q, A novel approach for interest point detection based on phase congruency[C], TENCON,2005:1-6.
[130]惠颖,罗海波.基于相位一致性检测的目标分割和跟踪方法[J],红外与激光工程,2007,36(6)：984-987
[131]卢振泰,冯衍秋,冯前进,等.基于主相位一致性的医学图像配准[J],电子学报,2008,36(10)：1974-1978
[132]Huiwen. L, Chunguang. Xu, Zhongwei. F etl. A method for extracting the center of ting-structured-light stripe[C]. International Symposium on Intelligent Information Technology Application,2008,2:906-910
[133]王莉,戴特力.获得去离焦模糊的显微镜焦平面图像的一种方法[J],激光杂志,2005,26(6)：45-46.
[134]郑楚君,李榕,常鸿森.离焦模糊数字图像的Wiener滤波频域复原[J],激光杂志,2004,25(5),57-58.
[135]胡小平,陈国良,毛征宇,等.离焦模糊图像的微纳滤波复原研究[J],仪器仪表学报,2007,28(3)：479-482.
[136]孙辉,张葆,刘晶红,等.离焦模糊图像的微纳滤波恢复[J],光学技术,2009,35(2)：295-298.
[137]胡小平,尹喜云,陈国良,等.离焦模糊图像的自适应滤波及逆滤波器复原[J],光电工程,2007,34(4)：105-108.
[138]Su. L. Y, Li. F. L, Xu. F, etl, Defocused image restoration using RBF network and Iterative wiener filter in wavelet domain [C], Image and Signal Processing CISP 08,2008(3): 311-315.
[139]Cu. L, Ding. W. R, Man. Y. Y, etl, Restoration of Defocus Blur Image Based on Global Phase Coherence[C], IEEE, Image and Signal Processing CISP 09,2009:1-5.
[140]Bi. X. J, Wang. T, Adaptive_blind_image_restoration_algorithm_of_degraded_image[C], Image and Signal Processing CISP 08,2008(3):536-540
[141]Moghaddam. M.E. A robust noise independent method to estimate out of focus blur[C], IEEE, Acoustics, Speech and Signal Processing,2008,4:1273-1276.
[142]Sezan. M. I, Pavlovic. G, Tekalp. A. M, etl. On modeling the focus blur in image restoration[C], IEEE, Acoustics, Speech and Signal Processing,1991,4:2485-2488.
[143]Andreas. E, Savakis. H, Joel. T. On the accuracy of PSF representation in image restoration [J], IEEE Transactions on Image Processing,1993,2:252-259.
[144]张益昕,王顺,张旭苹,大尺度三维视觉测量中的离焦模糊图像恢复[J],仪器仪表学报,2010,3(12)：1-6
[145]Abdel-Aziz. Y. I, Karara. H. M, Direct linear transformation into object space coordinates in close-range photogrammetry[C], Symposium on Close-Range Photogrammetry,1971: 1-18
[146]Faugeras. O. D, Toscani. G, Camera calibration for 3D computer vision[C], International Workshop on Industrial Applications of Machine Vision and Machine Intelligence,1987: 240-247
[147]Ganapathy. S, Decomposition of transformation matrices for robot vision[C], IEEE International Conference on Robotics and Automation,1984:130-139
[148]Weng. J, Cohen. P, Herniou. M, Calibration of stereo cameras using a non-linear distortion model[C], International Conference on Pattern Recognition,1990,1:246-253
[149]DEWAR R. Self-generated targets for spatial calibration of structured light optical sectioning sensors with respect to an external coordinate systems[C], Proceedings of Robots and Vision, Detroit,1988:5-13.
[150]刘凤梅,段发阶,叶声华,一种新的高精度的线结构光传感器标定方法[J],天津大学学报,1999，2(5)：547-550.
[151]周富强,张广军,表面视觉传感器模型参数的简易标定方法[J],机械工程学报,2005,41(3)：175-176.
[152]许丽,张之江,基于共面靶标的结构光标定方法[J],光电子激光,2009,20(8)：1063-1069.
[153]Zhang. Z. Y, Deriche. R, Faugeras. O, and etl, A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry[J], Artificial Intelligence,1995,78:87-119.
[154]Zhang. Z. Y, Determining the epopolar geometry and its uncertainty:a review[J], International Journal of Computer Vision,1998,27(2):161-198
[155]Zhang. Z. Y, On the epipolar geometry between two images with lens distortion. International Conference of Pattern Recognition,1996,1:407-411
[156]Davies. E. R, Machine vision theory, algorithms, practicalities[M], Elsevier,2009
[157]李雪芹,铁路超限货物装载技术相关问题研究[D],西南交通大学交通运输规划与管理专业,2008
[158]郑光前,谭仲平,铁路货运组织[M],湖南：湖南大学出版社,1998
[159]朱为鹏,基于数字图像处理的货车超限检测算法研究[D],西南交通大学机械电子工程专业,2003
[160]吴昌东,货车超限检测系统中图像处理方法研究[D],西南交通大学信号与信息处理专业,2008
[161]王欣秋,铁路货车超限及装载状态监测系统应用[J],铁道技术监督,2006,34(4)：24-25
[162]梁伟明,铁路超限超重货物运输项目评价研究[D],华北电力大学项目管理专业,2008
[163]吴育俭,郭维鸿,刘东岭,铁路货运技术M],北京：中国铁道出版社,2003
[164]杜庆光,货车垂下品自动检测仪[J],铁道车辆,2007,45(4)：25-26
[165]雷波,贺赛先,毛庆洲,铁路货车底部超限测量系统[J],仪表技术与传感器,2005,5：52-54
[166]钟生贵,吴顺生,黄小平,货物列车超限远程检测系统的研究[J],中国铁路,2004,10：39-41
[167]熊军,仲思东,毛庆洲,利用测量光幕检测货物列车端部超限[J],铁道车辆,2005,43(11)-35-37
[168]杜津玲,勾长虹,基于激光雷达的铁路超限货物测量方法[J],军事交通学院学报,2009,1](1)：89-91
[169]孙丽丽,肖世德,基于结构光原理的铁路货车超限检测算法研究[J],计算机应用,2005,25(1)：213-217
[170]Kumar. A, Ahuja. N, Hart.J.M, etl, A Vision System for Monitoring Intermodal Freight Trains[C], Applications of Computer Vision, IEEE,2007:24-24
[171]Kong.Q.J, Kumar.A, Ahuja, etl, Robust segmentation of freight containers in train monitoring videos[C], Applications of Computer Vision, IEEE,2009:1-6
[172]王颋,肖世德,朱为鹏,货车超限差分算法图像分割[J],计算机工程与应用,2004,11(4)：212-215
[173]于东,顾培亮,陈钟,高旋,铁路货车装载状态监视和超限检测系统的研究[J],中国铁道科学,2004,25(5)：141-144
[174]王欣秋,铁路货车超限及装载状态监测系统应用[J],铁道技术监督,2006,34(4)：24-25
[2]韩庆龙,大尺寸截面配对式视觉测量方法的研究[D],天津大学精密仪器与光电子工程学院,2008.6
[3]吴斌,孙长钦,杨兵等,结构光传感器在柔性视觉测量系统中的应用研究[J],光电子·激光,2008,19(10)：1309-1311
[4]吕乃光,邓文怡,大型机械零部件孔同轴度测量技术研究[J],光电子·激光,2001,12(7)：694-696
[5]Wen Gewang, Zhang yihu, Zong Yushun,3-D shape measurement based computer vision[C], International Conference on Machine Learning and Cybernetics,2002:910-914.
[6]Quan Shi, Ning Xi, and Yifan Chen, Development of an automatic optical measurement system for automotive part surface Inspection[C], International Conference on Advanced Intelligent Mechatronics,2005:1557-1562.
[7]Ryo Kurazume, Yukihiro Tobata, Yumi Iwashita, and Tsutomu Hasegawa,3D laser measurement system for larger scale architectures using multiple mobile robot[C]s, International Conference on 3-D Digital Imaging and Modeling,2007:91-98.
[8]Hirofumi Nakai, Daisuke Iwai, Kosuke Sato,3D shape measurement using fixed camera and handheld laser scanner[C], SICE Annual Conference,2008:1536-1539.
[9]H.Yasser, Laser based system for reverse engineering[J], Computers and Engineering,1994, 26(2):387-394
[10]Mohammad Amini Tehrani, Ali Saghaeian, Omid Reza Mohajerani, A new approach to 3D modeling using structured light pattern[C], International Conference on Information and Communication Technologies,2008:1-5.
[11]刘晓,吴训威,破损古文物的计算机三维重构[J],电子与信息学报,2001,23(7)650-655
[12]Zhanli Li, Yuan Wang, Research on 3D reconstruction procedure of marked points for large workpiece measurement[C], International Conference on Information Assurance and Security,2009:273-276.
[13]刘建伟,梁晋,梁新合,等,大尺寸工业视觉测量系统[J],光学精密工程,2010,18(1)：126-134
[14]孙军华,张广军,魏振忠,等,大型自由曲面移动式三维视觉测量系统[J],仪器仪表学报,2006,27(12)：1689-1691
[15]张顺德,卢秉恒,丁玉成,光学三维型面分区域测量数据的拼接研究[J],中国激光,2001,28(6)：533-536
[16]Carbone. V, Carocci. M, Savio.E, etal, Combination of a vision system and a coordinate measuring machine for the reverse engineering of freeform surfaces[J], International Journal of Advanced Manufacturing Technology,2001,17(4):263-271
[17]Vaady. T, Martin. R. R, Cox. J, Reverse engineering of geometric models--an introduction[J], Computer Aided Design,1997,29(4):255-268
[18]龙玺,钟约先,李仁举,等,结构光三维扫描测量的三维拼接技术[J],清华大学学报,2002,42(4)：477-480
[19]Pancewicz. T, Kujawinska. M, CAD/CAM/CAE representation of 3D objects measured by fringe projection[J], SPIE laser Interferometry,1998,3479 (7):70-75
[20]Sansoni. G, Carocci.M, Rodella. R. Calibration and performance evaluation of a 3D imaging sensor based on the projection of structured light[J], IEEE Transactions on Instrumentation and Measurement,2000,49(3):628-636
[21]Reich. C, Ritter. R, Thesing. J,3D shape measurement of complex objects by combining photogrammetry and fringe projector[J], Optical Engineering,2000,39(1):224-231
[22]Hung. Y. Y, Lin. L, Park. B. G, Practical 3D computer vision techniques for full-field surface measurement[J]. Optical Engineering,2000,39(1):143-149
[23]任同群,大型3D形貌测量高精度拼接方法与技术研究[D],天津大学精密仪器与光电子工程学院：2008.1
[24]王志凌,基于双CCD的大锻件尺寸测量的实验研究[D],燕山大学电气工程学院,2005.10
[25]Hermann. G, Design considerations for a modular high precision 3D coordinate measuring machine[C], International Conference on Mechatronics,2006:161-165.
[26]周维虎,李百源,兰一兵,等,经纬仪坐标测量系统检测方法的研究[J],航空计测技术, 2000,20(1)：6-9
[27]Zhigang Liu, Yaozhong Xu, Zhongzheng, et al, A large scale 3D positioning method based on a network of rotating laser automatic theodolites[C], International Conference on Information and Automation,2010:513-518.
[28]Bouvet. D, Garcia. G, Gorham. B. J, et al, Precise 3-D localization by automatic laser theodolite and odometer for civil-engineering machines [C], International Conference on Robotics and Automation,2001:2045-2050.
[29]李广云,非正交系坐标测量系统原理及进展[J],测绘信息与工程,2003,28(1)：4-10
[30]Zongqian Zhan, Zuxun Zhang, Jianqing Zhang, An integrated photogrammetric system with metric digital camera and total station[C], International Multi-symposiums on Computer and Computational Sciences,2006:747-752.
[31]Guanbin Gao, Wen Wang, Keng Lin, et al, Kinematic calibration for articulated arm coordinate measuring machines base on particle swarm optimization[C], International Conference on Intelligent Computation Technology and Automation,2009:189-192
[32]Guanbin Gao, Wen Wang, Keng Lin, et al, Structual parameter identification for articulated arm coordinate measuring machines[C], International Conference on Intelligent Computation Technology and Automation,2009:128-131.
[33]Hori. T, Nishida. Y. J, Improvement of position estimation of the ultrasonic 3D tag system[C], International Symposium on Robot and Human Interactive Communication, 2008:436-441.
[34]秦世伟,谷川,Indoor GPS技术及其在工业领域中的应用[J],铁道勘察,2008,24(3)：31-34
[35]陈翔鹏何志英,数字化在线测量技术在飞机装配中应用[J],航空与航天,2008,12(4)：20-23
[36]周春艳,激光跟踪坐标测量系统的双线法基点自标定[J],科学技术与工程,2009,9(12)：3429-3431
[37]孙长库,叶声华,激光测量技术[M],天津：天津大学出版社,2001
[38]J. S. Massa, G. S. Buller, A. C. Walker, et al, Time of flight optical ranging system based on time correlated single photon counting[J], Appl. Opt.1998,37(31):7298-7304.
[39]B. Nilsson, T. E. Carlsson, Direct three dimensional shape measurement by digital light-in-flight holography [J], Appl. Opt.1998,37(34):7954-7959.
[40]王润田,双频超声波测距[J],声学技术,1996,15(3)：116-118
[41]张维,漆新民,李坚等,一种新型测距仪的研制[J],航空计测技术,1998,18(6)：27-29
[42]张广军,视觉测量[M],北京：科学出版社,2008
[43]Y. Hu, J. Xi, E. Li, J. Chicharo, et al, Three-dimensional profilometry based on shift estimation of projected fringe patterns[J], Appl. Opt.2006,45(4):678-687.
[44]T. Baumbach, W. Osten, C. Kopylow, et al, Remote metrology by comparative digital holography [J], Appl. Opt.2006,45(5):925-934.
[45]D. Purcell, A. Davies, F. Farahi, Effective wavelength calibration for moire fringe projection [J], Appl. Opt.2006,45(34):8629-8635.
[46]Da. Feipeng, Gai. Shaoyan, Flexible three-dimensional measurement technique based on a digital light processing projector [J], Appl. Opt.2008,47(3):377-385.
[47]Yongchang Wang, Daniel L. Lau, Laurence G. Hassebrook, Fit-sphere unwrapping and performance analysis of 3D fingerprints [J], Appl. Opt.2010,49(4):592-600.
[48]Wang. Zhangyang, Du. Hua, Park. Seungbae, et al, Three-dimensional shape measurement with a fast and accurate approach [J], Appl. Opt.2009,48(6):1052-1061.
[49]Roberts. L. G, Machine perception of three-dimensional solids, In Optical and Electro-optical Information Processing [M]. Cambridge:MIT Press,1965:7-24
[50]Guzman. A, Decomposition of a visual scene into three-dimensional bodies[D], In automatic Interpretation and Classification of Images. New York:Academic Press,1969: 15-58
[51]贾云得,机器视觉[M],北京：科学出版社,2000：1-13
[52]伍雪东,计算机视觉中摄像机定标及位姿和运动估计方法的研究[D],湖南大学电气与信息工程学院,2004：4.
[53]Zhanli Li, Yuan Wang, Research on 3D reconstruction procedure of marked points for large workpiece measurement[C], International Conference on Information Assurance and Security,2009:273-276.
[54]Wen Gewang, Zhang yihu, Zong Yushun,3-D shape measurement based computer vision[C], International Conference on Machine Learning and Cybernetics,2002:910-914.
[55]Ryo Kurazume, Yukihiro Tobata, Yumi Iwashita, and Tsutomu Hasegawa,3D laser measurement system for larger scale architectures using multiple mobile robots[C], International Conference on 3-D Digital Imaging and Modeling,2007:91-98.
[56]Richard. K, Adaptive optical three-dimensional measurement with structured light[J]. Optical Engineering,39(1),2000:150-158.
[57]Kurtis. K, Jeremy. A, Real-time structured light depth extraction[J], Three-dimensional Image Capture and Applications, Proc.SPIE,3958,2000:11-18.
[58]刘维一,王肇忻,母国光,等,彩色编码投影光栅轮廓术中分色问题的探究[J],光学学报,2001,21(4)：454-458.
[59]周富强,李章,张广军,基于三维测量扫描线点云的表面重建[J],仪器仪表学报,2006,27(6)：619-623.
[60]谢凯,刘宛予,浦昭邦,结构光三维视觉检测系统的标定方法研究[J],光电子·激光,2007,18(3)：369-371
[61]孙长钦,基于多光条结构光视觉传感器的大尺寸园钢测量技术研究[D],天津大学精密仪器与光电子工程学院,2008,8
[62]黄桂平,钦桂勤,卢成静,数字近景摄影大尺寸三坐标测量系统V-STARS的测试与应用[J],宇航计测技术,2009,29(2)：5-22
[63]武坚,郝春生,马永刚,等,数字图像融合技术在摄影测量中的应用[J],海洋测绘,2008,28(4)：73-75
[64]Yan.K.Q, Cheng.T, Close shot photogrammetry for measuring winddrifted snow distribution on stepped flat roofs[C], IEEE International Colloquium on Computing, Communication, Control and Management,2008,1:332-335.
[65]Rudin. L, Monasse. P, Ping. Y, Epipolar photogrammetry:a novel method for forensic image comparison and measurement[C], IEEE International Conference on Image Processing,2005,3:385-388
[66]Radu. H, Gabriella. C, David. D, Stereo calibration from rigid motions. IEEE Transactions on Pattern Analysis and Machine Intelligence.2000,22(12):1446-1452
[67]Horn.B.K.P, Brooks.M.J, Shape from Shading[M],Cambridge MA, MIT Press,1989
[68]Zhang. R, Tsai. P. S, Cryer. E, Shape form shading:a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence [J],1999,21(8):690-706.
[69]杨少荣,吴迪靖,段德山,机器视觉算法与应用[M],北京：清华大学出版社,2008
[70]叶海加,陈罡,邢渊,双目CCD结构光三维测量系统中的立体匹配[J],光学精密工程,2004,12(1)：72-75
[71]陶立,孙长库,何丽,等,基于结构光扫描的彩色三维信息测量技术[J],光电子·激光,2006,17(1)：111-114
[72]胡勇,王从军,韩明,等,基于计算机视觉的三维激光扫描测量系统[J],华中科技大学学报：自然科学版,2004,32(1)：16-18
[73]冈萨雷斯,数字图像处理[M].北京：电子工业出版社,2006.
[74]Born. M, Wolf. W, Principles of optics[M], Cambridge:Cambridge University Press,7th, 1999
[75]Weng. J. W, Cohen. P, Herniou.M, Camera calibration with distortion models and accuracy evaluation[J], Transactions on Pattern Analysis and Machine Intelligence,1992,14(10): 965-980
[76]Holst. G. C, CCD arrays, cameras, and displays [M]. SPIE Press, Bellingham, WA,2nd, 1998
[77]Gamal. A. Er, Eltoukhy. H, CMOS image sensor. IEEE Circuits and Devices Magazine[J], 2005,21(3):6-20
[78]Miida. T, A 1.5Mpixel imager with localized hole-modulation method[C], ISSCC 2002: 42-43
[79]陈榕庭,彭美桂,CCD/CMOS图像传感器基础与应用[M],北京：科学出版社,2006
[80]Alleysson. D, Susstrunk. S, Herault.J, Linear demosaicing inspired by the human visual system [J]. IEEE Transactions on Image Processing,2005,14(4):439-449.
[81]Hirakawa.K, Parks. T. W, Adaptive homogeneity-directed demosaicing algorithm. IEEE Transactions on Image Processing,2005,14(3):360-369
[82]Zhang.Y.X, Wang. S, Zhang. X. P, etl, Motion blurred image restoration algorithm for IC wire bonder[C], Proceedings of SPIE-The International Society for Optical Engineering, 2009,7513
[83]董寅,张益听,王顺,张旭苹,布里渊光时域反射传感系统数据采集模块的研制[J].光电子技术.2007,27(2)：88-91
[84]Hartley. R, Zisserman. A, Multiple view geometry in computer vision[M], Cambridge: Cambridge University Press,2nd,2003
[85]ZHANG. Z. Y, Determining the epipolar geometry and its uncertainty:a review[J], International Journal of Computer Vision,1998,27(2):161-198.
[86]ZHANG. Z. Y, A flexible new technique for camera calibration[C], Seventh International Conference on Computer Vision (ICCV99),1999:666-673.
[87]TSAI. R. Y, A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV camera and lenses[J], IEEE Journal of Robotics and Automation,1987,3(4):323-344.
[88]卞晓东,基于机器视觉的车辆几何尺寸测量系统研究[D],东南大学,2005.
[89]Ren. Zhiguo, Cai. Lilong, Three-dimensional structure measurement of diamond crowns based on stereo vision [J], Appl. Opt.2009,48(31):5917-5932.
[90]W. S. Kim, A. I. Ansar, R. D. Steele, et al, Performance analysis and validation of a stereo vision system [C], IEEE International Conference on Systems, Man and Cybernetics,2005,2: 1409-1416.
[91]C. C. Chang, S. Chatterjee, P. R. Kube, A quantization error analysis for convergent stereo [C], IEEE International Conference on Image Processing,1994,2:735-739.
[92]苏宇,基于特征点的图像拼接技术研究[D],西安电子科技大学,2008
[93]Wang. H, Brady. M. Real time corner detection algorithm for motion estimation[J], Image and Vision Computing,1995,13(9):695-703
[94]Trajkovicm, Hedley.M, Fast corner detection[J], Image and Vision Computing,1998,16(1): 75-87
[95]Bretschi. J, Automated inspection systems for industry[J]. Electronics Letters,1996,8(12): 1695-1697
[96]Kithenl, Roseenfeld.A.K. Gray-level corner detection[J]. Pattern Recognition Letters,1982, 3(1):95-102.
[97]Mokhtarian. F, Suomela. R, Robust image corner detection through curvature scale space [J]. IEEE Trans. On Pattern Analysis and Machine Intelligence,1998,20(12):1376-1381.
[98]Smith, S.M., Brady, J.M, SUSAN-a new approach to low level image processing [J], International Journal of Computer Vision 23,1997:45-78
[99]Harris, C., Stephens, M, A combined corner and edge detector [C]. In:Proceedings,4th Alvey Vision Conference[C],1988:147-151.
[100]Schmid. C, Mohr. R, Bauckhage. C, Evaluation of interest point detectors [J], International Journal of Computer Vision,2000,37(2):151-172.
[101]张春森,基于点特征匹配的SUSAN, Harris算子比较[J],西安科技大学学报,2007,27(4)：608-616.
[102]张春美,龚志辉,黄艳,几种特征点提取算法的性能评估及改进[J],测绘科学技术学报,2008,5(3)：231-234
[103]Zhang. J. Y, Chen. Q, Bai. X. J, etl, An advanced Harris-Laplace feature detector with high repeatability[C], International Congress on Image and Signal Processing,2009:1-5
[104]Guo. C. G, Li. X. L, Zhong. L. F, A fast and accurate corner detector based on Harris algorithm[C], International Symposium on Intelligent Information Technology Application, 2009,2:49
[105]王玉珠,杨丹,张小洪,基于B样条的改进型Harris角点检测算法[J],计算机应用研究,2007,2：192-194
[106]陈白帆,蔡自兴,基于尺度空间理论的Harris角点检测[J],中南大学学报(自然科学版),2005,36(5)：751-754
[107]王德超,涂亚庆,张铁楠,一种多尺度Harris角点检测方法[J],计算机应用与软件,2008,25(12)：242-262
[108]李薇薇,段智敏,一种改进型的Harris角点检测算法[J],机械工程与自动化,2009,2(1)：19-23
[109]毛雁明,兰美辉,王运琼,冯乔生,一种改进的基于Harris的角点检测方法[J]，2009,19(5)：130-133
[110]张小洪,李博,杨丹,一种新的Harris多尺度角点检测[J],电子与信息学报,2007,29(7)：1735-1738
[111]赵万金,龚声蓉,刘纯平,沈项军,一种自适应的Harris角点检测算法[J],计算机工程,2008,34(10)：212-217
[112]王耀贵,图像高斯平滑滤波分析[J],计算机与信息技术,2008,8：79-90
[113]Son. S, Park. H, Lee. K. H, Automated laser scanning system for reverse engineering and inspection [J], International Journal of Machine Tools & Manufacture,2002,42(8),889-897.
[114]Trinh. N. H, Laster. J, Fleming. B. C, etl, Accurate measurement of cartilage morphology using a 3D laser scanner[J], Computer Vision Approaches to Medical Image.2006,4241: 37-48.
[115]Wang. C. C, Shyue. S.W, Cheng. S. H, Underwater_structure_inspection_with_laser_light stripes[C], Proceedings of the 2000 International Symposium,2000:201-205
[116]Xie. X. Z, Wang. J.G, Jin. M, Study on a full field of view laser scanning system[J], International Journal of Machine Tools&Manufacture,2007,47:33-43.
[117]张舜德,卢秉恒,丁玉成,高斯光束柱面反射展成法生成光刀的研究[J],中国激光,2001,28(2)：133-136
[118]崔希君,杨川,刘保华,等,线性结构光心的自适应迭代提取法[J],西安交通大学学报,2007,41(1)：73-76.
[119]熊会元,宗志坚,高群,等,精确提取线结构光条纹中心方法[J],计算机工程与应用,2009,45(1)：235-237.
[120]胡斌,李德华,金刚,等,基于方向模板的结构光条文中心检测方法[J],计算机工程与应用,2002,11：59-109
[121]胡坤,周富强,张广军,一种快速结构光条纹中心亚像素精度提取方法[J],仪器仪表学报,2006,27(10)：1326-1329
[122]贺俊吉,张广军,结构光三维视觉检测中光条图像处理方法研究[J],北京航空航天大学学报,2003,29(7)：593-597
[123]周富强,陈强,张广军,结构光光条提取的混合图像处理方法[J],2008,19(11)：1534-1537
[124]周富强,王飞,张广军,结构光直线光条图像特征的三步法提取[J],2008,44(11)：215-219
[125]Steger C. An unbiased detector of curvilinear structures[J], IEEE Transactions on Pattern Analysis and machine Intelligence 20,1998:113-125.
[126]Kovesi, P, Symmetry and asymmetry from local phase[C], Tenth Australian Joint Conference on Artificial Intelligence,1997:185-190.
[127]Kovesi, P, Phase congruency detects corners and edges, The Australian Pattern Recognition Society Conference,2003:309-318
[128]Field. D. J, Relations between the statistics of natural images and the response properties of cortical cells [J], Journal of The Optical Society of America,1987,4(12):2379-2394.
[129]Wu. C. Z, Wang. Q, A novel approach for interest point detection based on phase congruency[C], TENCON,2005:1-6.
[130]惠颖,罗海波.基于相位一致性检测的目标分割和跟踪方法[J],红外与激光工程,2007,36(6)：984-987
[131]卢振泰,冯衍秋,冯前进,等.基于主相位一致性的医学图像配准[J],电子学报,2008,36(10)：1974-1978
[132]Huiwen. L, Chunguang. Xu, Zhongwei. F etl. A method for extracting the center of ting-structured-light stripe[C]. International Symposium on Intelligent Information Technology Application,2008,2:906-910
[133]王莉,戴特力.获得去离焦模糊的显微镜焦平面图像的一种方法[J],激光杂志,2005,26(6)：45-46.
[134]郑楚君,李榕,常鸿森.离焦模糊数字图像的Wiener滤波频域复原[J],激光杂志,2004,25(5),57-58.
[135]胡小平,陈国良,毛征宇,等.离焦模糊图像的微纳滤波复原研究[J],仪器仪表学报,2007,28(3)：479-482.
[136]孙辉,张葆,刘晶红,等.离焦模糊图像的微纳滤波恢复[J],光学技术,2009,35(2)：295-298.
[137]胡小平,尹喜云,陈国良,等.离焦模糊图像的自适应滤波及逆滤波器复原[J],光电工程,2007,34(4)：105-108.
[138]Su. L. Y, Li. F. L, Xu. F, etl, Defocused image restoration using RBF network and Iterative wiener filter in wavelet domain [C], Image and Signal Processing CISP 08,2008(3): 311-315.
[139]Cu. L, Ding. W. R, Man. Y. Y, etl, Restoration of Defocus Blur Image Based on Global Phase Coherence[C], IEEE, Image and Signal Processing CISP 09,2009:1-5.
[140]Bi. X. J, Wang. T, Adaptive_blind_image_restoration_algorithm_of_degraded_image[C], Image and Signal Processing CISP 08,2008(3):536-540
[141]Moghaddam. M.E. A robust noise independent method to estimate out of focus blur[C], IEEE, Acoustics, Speech and Signal Processing,2008,4:1273-1276.
[142]Sezan. M. I, Pavlovic. G, Tekalp. A. M, etl. On modeling the focus blur in image restoration[C], IEEE, Acoustics, Speech and Signal Processing,1991,4:2485-2488.
[143]Andreas. E, Savakis. H, Joel. T. On the accuracy of PSF representation in image restoration [J], IEEE Transactions on Image Processing,1993,2:252-259.
[144]张益昕,王顺,张旭苹,大尺度三维视觉测量中的离焦模糊图像恢复[J],仪器仪表学报,2010,3(12)：1-6
[145]Abdel-Aziz. Y. I, Karara. H. M, Direct linear transformation into object space coordinates in close-range photogrammetry[C], Symposium on Close-Range Photogrammetry,1971: 1-18
[146]Faugeras. O. D, Toscani. G, Camera calibration for 3D computer vision[C], International Workshop on Industrial Applications of Machine Vision and Machine Intelligence,1987: 240-247
[147]Ganapathy. S, Decomposition of transformation matrices for robot vision[C], IEEE International Conference on Robotics and Automation,1984:130-139
[148]Weng. J, Cohen. P, Herniou. M, Calibration of stereo cameras using a non-linear distortion model[C], International Conference on Pattern Recognition,1990,1:246-253
[149]DEWAR R. Self-generated targets for spatial calibration of structured light optical sectioning sensors with respect to an external coordinate systems[C], Proceedings of Robots and Vision, Detroit,1988:5-13.
[150]刘凤梅,段发阶,叶声华,一种新的高精度的线结构光传感器标定方法[J],天津大学学报,1999，2(5)：547-550.
[151]周富强,张广军,表面视觉传感器模型参数的简易标定方法[J],机械工程学报,2005,41(3)：175-176.
[152]许丽,张之江,基于共面靶标的结构光标定方法[J],光电子激光,2009,20(8)：1063-1069.
[153]Zhang. Z. Y, Deriche. R, Faugeras. O, and etl, A robust technique for matching two uncalibrated images through the recovery of the unknown epipolar geometry[J], Artificial Intelligence,1995,78:87-119.
[154]Zhang. Z. Y, Determining the epopolar geometry and its uncertainty:a review[J], International Journal of Computer Vision,1998,27(2):161-198
[155]Zhang. Z. Y, On the epipolar geometry between two images with lens distortion. International Conference of Pattern Recognition,1996,1:407-411
[156]Davies. E. R, Machine vision theory, algorithms, practicalities[M], Elsevier,2009
[157]李雪芹,铁路超限货物装载技术相关问题研究[D],西南交通大学交通运输规划与管理专业,2008
[158]郑光前,谭仲平,铁路货运组织[M],湖南：湖南大学出版社,1998
[159]朱为鹏,基于数字图像处理的货车超限检测算法研究[D],西南交通大学机械电子工程专业,2003
[160]吴昌东,货车超限检测系统中图像处理方法研究[D],西南交通大学信号与信息处理专业,2008
[161]王欣秋,铁路货车超限及装载状态监测系统应用[J],铁道技术监督,2006,34(4)：24-25
[162]梁伟明,铁路超限超重货物运输项目评价研究[D],华北电力大学项目管理专业,2008
[163]吴育俭,郭维鸿,刘东岭,铁路货运技术M],北京：中国铁道出版社,2003
[164]杜庆光,货车垂下品自动检测仪[J],铁道车辆,2007,45(4)：25-26
[165]雷波,贺赛先,毛庆洲,铁路货车底部超限测量系统[J],仪表技术与传感器,2005,5：52-54
[166]钟生贵,吴顺生,黄小平,货物列车超限远程检测系统的研究[J],中国铁路,2004,10：39-41
[167]熊军,仲思东,毛庆洲,利用测量光幕检测货物列车端部超限[J],铁道车辆,2005,43(11)-35-37
[168]杜津玲,勾长虹,基于激光雷达的铁路超限货物测量方法[J],军事交通学院学报,2009,1](1)：89-91
[169]孙丽丽,肖世德,基于结构光原理的铁路货车超限检测算法研究[J],计算机应用,2005,25(1)：213-217
[170]Kumar. A, Ahuja. N, Hart.J.M, etl, A Vision System for Monitoring Intermodal Freight Trains[C], Applications of Computer Vision, IEEE,2007:24-24
[171]Kong.Q.J, Kumar.A, Ahuja, etl, Robust segmentation of freight containers in train monitoring videos[C], Applications of Computer Vision, IEEE,2009:1-6
[172]王颋,肖世德,朱为鹏,货车超限差分算法图像分割[J],计算机工程与应用,2004,11(4)：212-215
[173]于东,顾培亮,陈钟,高旋,铁路货车装载状态监视和超限检测系统的研究[J],中国铁道科学,2004,25(5)：141-144
[174]王欣秋,铁路货车超限及装载状态监测系统应用[J],铁道技术监督,2006,34(4)：24-25
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |