立体内视测量技术研究
|
摘要
基于立体视觉的内视测量技术在国外已经得到了初步研究和应用,在国内也受到了极大的关注。随着CCD摄像机的小型化和计算机硬件集成度的不断提高,相关技术的不断发展,立体内视测量技术以其柔性、快速、非接触、精确、自动化程度高等特点为优势,得到了越来越广泛的研究与应用。
本文设计研发的立体内窥测量系统,可用来检测枪械、火炮的膛线及内部表面的损伤,对膛线损伤区域的深度、长度、面积等可进行定量测量和定性分析。
结合立体视觉的几何模型,以适合人眼观察的Panum区原理为基础,得出了符合人眼观察习惯的舒适视差范围,并根据在CRT显示器上舒适观察距离,计算出最大像素差,并以此推导出立体内窥镜的放大率、焦距、视场角等重要参数。对立体内窥镜光学系统包括物镜光学系统、转像光学系统和目镜光学系统等的结构与相关镜头参数进行了详细的设计与优化。
该立体内窥测量系统采用FPGA技术,对立体内窥镜所获图像实现了100Hz双路视频信号的交替逐行输出,使时分制显示图像频率达到50Hz,克服了传统时分制立体电视图像的闪烁现象和采用计算机图像时分制显示资源的不可分配性。系统利用Verilog HDL语言实现了双路视频处理及视频传输控制电路的设计,采用了外设SDRAM存储器作为帧存储器,以实现视频数据的存储。在FPGA中开辟FIFO用来缓存SDRAM输入和输出的像素数据,加快存储速度,使输出扫描帧频可达到100Hz逐行扫描,让观看者更舒适。
针对枪膛、炮膛测量的特点与需求和现场实际环境的分析,采用了一种灵活、简洁、精度高的标定方法—张氏平面标定法,对立体摄像机进行了标定,确定了空间点之间的对应关系,提高了测量的准确性。运用基于可调节阈值的Canny算法的最佳边缘检测算子,提高了图像边缘的清晰度与信噪比;采用基于动态规划的区域灰度匹配和特征点匹配的快速算法,根据特征点来限定非特征点的有效视差集,缩小了生成视差空间的计算复杂度和搜索范围,使遮掩区得到了一定程度的识别。实验表明,此算法简单实用,运算速度快,对边缘清晰的立体图像对达到了较高的匹配率。
该立体内窥测量系统,较好地解决了枪械、火炮膛线的立体图像采集困难、边缘不清晰,测量问题中的失配、误匹配和低速等问题,提高了枪械、火炮内膛线测量的准确性。
The measurement technology based on internal stereoscopic has been preliminary studied and applied abroad, and is getting more and more attention interiorly. With the CCD (Charge Coupled Device) camera miniaturization and integration of computer hardware, as well as continuous improvement of correlated technologies, internal stereoscopic measurement technology will soon be applied globally for its flexible, rapid, non-contact, precision, high degree of automatization and other characters.
This paper focuses on the study of using stereoscopic endoscope as a detect tool, using the images coordinates collected by CCD camera to determine and calculate the spatial relationship of the points, then get the distance information by image points. This technology is used to detect rifled damage of the guns and artillery, and it has qualitative analysis and quantitative measurement for the depth and length of the rifling damage region.
Stereoscopic endoscope design is one of the key issues to achieve stereoscopic measurement. The stereoscopic display technology is based on the human binocular stereo vision, and it can not be separated from the normal vision. The author combined stereoscopic visual geometric model, used the principle of Panum area which is fitted to the humans eyes to observe as a basic, calculated the comfort parallax range fitted to the human eyes observation habit, and in accordance with the comfortable observation distance showed in the CRT (Cathode Ray Tube)monitor, estimated the maximum pixel difference, according to this data, derived the magnification, focal length, field of view and other key parameters of the stereoscopic endoscope.
The difficult point of the entire detection system is the calibration of the stereoscopic camera, three-dimensional image reconstruction and match. According to the measurement characteristics of the bore and the barrel and the analysis of the on-site environment, it uses a flexible, simple, high-accuracy calibration - Zhang plane calibration, to meet the measurement requirements; at the same time, it brings forward the best edge detector operator of the Canny arithmetic, which is based on adjustable threshold value, so that the image on the edge is clearer, and improves the signal-to-noise ratio. It designs a quick algorithm for gray match of the area and the match of the feature points which are based on the dynamic programming, according to the characteristics points to limit the effective parallax set of the non-characteristics points, and narrows the computational complexity and the search range of the generated parallax space, so the covered area will be identified in a certain extent. This algorithm is simple and practical, the operating speed is fast, and it can achieve a higher match rate for the stereopictures which the edges are clear. It gets good results in experiments.
Design and debug the time division stereoscopic display system, it is the first use of FPGA(Field-Programmable Gate Array)technology, and breaches the original shortcomings of the need of the computer software to control, stereopictures flashing, and the poor real-time performance. This technology allows stereoscopic endoscopic images show completely out of the control of the computer, using Verilog HDL (Hardware Description Language) language to achieve a two-way video processing and video transmission control circuit design, in order to achieve video data storage, uses peripheral SDRAM (Synchronous Dynamic Random Access Memory) memory as a frame memory. Use FIFO(First Input First Output) in the FPGA as buffer to storage the input and output pixel data of SDRAM to speed up the pace, so that the output frame rate is up to 100Hz and has a progressive-scan, and also makes the watcher more comfortable.
To sum up, the paper completed the study on the internal stereoscopic measurement technology and the development of the system, as well as the measurement of the artillery rifling damage. The paper has a great significance for the development of non-contact optical detection techniques.
本文设计研发的立体内窥测量系统,可用来检测枪械、火炮的膛线及内部表面的损伤,对膛线损伤区域的深度、长度、面积等可进行定量测量和定性分析。
结合立体视觉的几何模型,以适合人眼观察的Panum区原理为基础,得出了符合人眼观察习惯的舒适视差范围,并根据在CRT显示器上舒适观察距离,计算出最大像素差,并以此推导出立体内窥镜的放大率、焦距、视场角等重要参数。对立体内窥镜光学系统包括物镜光学系统、转像光学系统和目镜光学系统等的结构与相关镜头参数进行了详细的设计与优化。
该立体内窥测量系统采用FPGA技术,对立体内窥镜所获图像实现了100Hz双路视频信号的交替逐行输出,使时分制显示图像频率达到50Hz,克服了传统时分制立体电视图像的闪烁现象和采用计算机图像时分制显示资源的不可分配性。系统利用Verilog HDL语言实现了双路视频处理及视频传输控制电路的设计,采用了外设SDRAM存储器作为帧存储器,以实现视频数据的存储。在FPGA中开辟FIFO用来缓存SDRAM输入和输出的像素数据,加快存储速度,使输出扫描帧频可达到100Hz逐行扫描,让观看者更舒适。
针对枪膛、炮膛测量的特点与需求和现场实际环境的分析,采用了一种灵活、简洁、精度高的标定方法—张氏平面标定法,对立体摄像机进行了标定,确定了空间点之间的对应关系,提高了测量的准确性。运用基于可调节阈值的Canny算法的最佳边缘检测算子,提高了图像边缘的清晰度与信噪比;采用基于动态规划的区域灰度匹配和特征点匹配的快速算法,根据特征点来限定非特征点的有效视差集,缩小了生成视差空间的计算复杂度和搜索范围,使遮掩区得到了一定程度的识别。实验表明,此算法简单实用,运算速度快,对边缘清晰的立体图像对达到了较高的匹配率。
该立体内窥测量系统,较好地解决了枪械、火炮膛线的立体图像采集困难、边缘不清晰,测量问题中的失配、误匹配和低速等问题,提高了枪械、火炮内膛线测量的准确性。
The measurement technology based on internal stereoscopic has been preliminary studied and applied abroad, and is getting more and more attention interiorly. With the CCD (Charge Coupled Device) camera miniaturization and integration of computer hardware, as well as continuous improvement of correlated technologies, internal stereoscopic measurement technology will soon be applied globally for its flexible, rapid, non-contact, precision, high degree of automatization and other characters.
This paper focuses on the study of using stereoscopic endoscope as a detect tool, using the images coordinates collected by CCD camera to determine and calculate the spatial relationship of the points, then get the distance information by image points. This technology is used to detect rifled damage of the guns and artillery, and it has qualitative analysis and quantitative measurement for the depth and length of the rifling damage region.
Stereoscopic endoscope design is one of the key issues to achieve stereoscopic measurement. The stereoscopic display technology is based on the human binocular stereo vision, and it can not be separated from the normal vision. The author combined stereoscopic visual geometric model, used the principle of Panum area which is fitted to the humans eyes to observe as a basic, calculated the comfort parallax range fitted to the human eyes observation habit, and in accordance with the comfortable observation distance showed in the CRT (Cathode Ray Tube)monitor, estimated the maximum pixel difference, according to this data, derived the magnification, focal length, field of view and other key parameters of the stereoscopic endoscope.
The difficult point of the entire detection system is the calibration of the stereoscopic camera, three-dimensional image reconstruction and match. According to the measurement characteristics of the bore and the barrel and the analysis of the on-site environment, it uses a flexible, simple, high-accuracy calibration - Zhang plane calibration, to meet the measurement requirements; at the same time, it brings forward the best edge detector operator of the Canny arithmetic, which is based on adjustable threshold value, so that the image on the edge is clearer, and improves the signal-to-noise ratio. It designs a quick algorithm for gray match of the area and the match of the feature points which are based on the dynamic programming, according to the characteristics points to limit the effective parallax set of the non-characteristics points, and narrows the computational complexity and the search range of the generated parallax space, so the covered area will be identified in a certain extent. This algorithm is simple and practical, the operating speed is fast, and it can achieve a higher match rate for the stereopictures which the edges are clear. It gets good results in experiments.
Design and debug the time division stereoscopic display system, it is the first use of FPGA(Field-Programmable Gate Array)technology, and breaches the original shortcomings of the need of the computer software to control, stereopictures flashing, and the poor real-time performance. This technology allows stereoscopic endoscopic images show completely out of the control of the computer, using Verilog HDL (Hardware Description Language) language to achieve a two-way video processing and video transmission control circuit design, in order to achieve video data storage, uses peripheral SDRAM (Synchronous Dynamic Random Access Memory) memory as a frame memory. Use FIFO(First Input First Output) in the FPGA as buffer to storage the input and output pixel data of SDRAM to speed up the pace, so that the output frame rate is up to 100Hz and has a progressive-scan, and also makes the watcher more comfortable.
To sum up, the paper completed the study on the internal stereoscopic measurement technology and the development of the system, as well as the measurement of the artillery rifling damage. The paper has a great significance for the development of non-contact optical detection techniques.
引文
[1]于辉,左洪福,陈果等.基于立体视觉的孔探分析系统及其应用.南京航空航天大学学报,2002(3):293-297
[2]Herbert Kaplan.A borescope that behaves like the human eye.PHOTONICS SPECTRA.SEPTEMBER 1994:4647.
[3]张阳德主编 内镜学 人民卫生出版社 2001年10月
[4]杨杰 中村仁彦 吴月华等 医用微型机械人 中国科学技术大学学报 第26卷第4期1996年12月
[5]袁启明 医用内窥镜的新进展 上海医疗器械研究所 中国医疗器械信息1997 3卷5期
[6]http://www.olympus.com.jp
[7]于辉,发动机故障图像的三维测量视觉重建与识别方法研究,[博士学位论文]南京:南京航空航天大学,2002。
[8]伍颂.赵建华,黄振佳.可测量视频内窥镜在无损检测中的应用.宇航材料工艺,2000(3):57
[9]王富治,高精度视觉测量系统研究,[硕士学位论文]合肥:电子科技大学,2005
[10]K.RADERMACHER,C.V.PICHLER,S.FISCHER,G.RAU,3D-Visualisation in Surgery Helmholtz-institute for Biomedical Engineering,Aachen University of Technology,Aachen(D)
[11]董太和,立体视觉原理,立体照相理论与实践讲座之四,浙江大学
[12]董太和,体视原理(上、下),立体照相理论与实距讲座之二、三,浙江大学
[13]K.Vijayan Asari,Sanjiv Kumar and D.Radhakrishnan,A new approach for nonlinear distortion correction in endoscopic image based on least squares estimation IEEE Transaction on Medical Imaging,1999,4(18):345-354
[14]Jie Wang,Yaorong Ge,An optimization problem in virtual endoscopy Theoretical Computer Science,207(1998)203-216
[15]孙慧,利用双目立体视觉技术重建电器触头立体图像,[硕士学位论文]天津,河北工业大学,2002
[16]吴夕,正常人双眼视觉的Panum融合区,眼科新进展,1999(9)375-376
[17]侯春萍,立体成像系统数学模型和视差控制方法,天津大学学报,2005,5,455-456
[18]颜树华等,立体视差法中距离分辨率的理论分析 光电工程1998 vol.25.No2
[19]Mayhew John E W,Frisby John P.3D Model StereocopicGues/M」.M assachusetts,London,England The M IT Press Cambridge,1991
[20]Victor S Grinberg,Gregg Podnar,Siegel M W.Geom etry of binocular imaging[A].In Proceedings of SPIE,Stereocopic Displays and VirucalSystem[C].San Jose,CA,USA:1994,2177:56-65
[21]白闻喜 医用内窥镜视放大率的测量原理及测试方法 长春光学精密机械学院学报 1994 vol.24 No.3
[22]张以漠,应用光学。机械工业出版社,1982
[23]王之江,光学设计手册。机械工业出版社,1987
[24]袁旭沧,应用光学。国防工业出版社,1988
[25]袁旭沧,现代光学设计方法。北京理工大学出版社
[26]Zobel;Jurgen Endoscope optical system United States Patent 4,723,843 1988
[27]向阳 电视腹腔镜系统 长春光学精密机械学院 硕士论文1996
[28]Tsai R Y.A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses.IEEE Journal of Robotics and Automation.1987,3(4):323-344
[29]Javier Ignzcio.Real-time 3D head position tracker system with stereo cameras using a face recognition neural network.[Doctor paper].UMI numbe.2004
[30]Jean hsu.Stereoscopic medical imaging.UMI.[Doctor paper].1995
[31]LatticeXP系列培训教程.上海莱迪斯半导体公司市场部,2005
[32]朱东魏 色度空间转换的设计及FPGA实现 电视技术2005.10
[33]王月兰,曾迎生.信息融合技术在彩色图像分割方法中的应用.计算机学报,2000年7月.23(7):763-767
[34]Tremeau A,Borel N A.Region growing and merging algorithm to color segmentation.Pattern Recognition,1997,30(7):1191-1203
[35]Henry R.Kang.Color Technology For Electronic Imaging Devices.SPIE-The International Society for Optical Engineering,1997
[36]Oliver D.Faugeras.Digital Color Image Processing Within the Framework of a Human Visual Model.IEEE Transactions on acoustics,speech and signal processing,ASSP-27(4)
[37]Tsai R Y.An efficient and accurate camera Calibration technique for recognition[C].Miami.FL.1986.364-374
[38]A.Basu,Activc Calibration of Cameras:Theory and lmplementation,IEEE Transactions onsystems,Man,and Cybernetics,vol,25,no.2,1995,pp 256-265.
[39]A.Basu and K.Ravi,Activc Camcra Calibration Using Pan,Roll-IEEE Transactions on systems,Man, and Cybernetics -PartB Cybernetics,vol.27,no.3,.1997,pp 559-566
[40]F.Li and M.Brady and C.Wiles,Fast Computation of the Fun-damental Matrix for an Active Stereo Vision System,4th European conference on computer Vision,Cambridge,UK,Apr.1996,pp 157-166
[41]S.Ganapaphy.Decomposition of transformation matrices for robot vision.Proc.Int.Conf.Robotics and Automation,1984:130-139
[42]W.Faig.Calibration of close-range photogrammetry systems:Mathematical formulation.Photogrammetric Eng.Remote sensing,1975,41(12):]1479-1486
[43]I.Sobel.On calibrating computer controlled cameras forperceiving 3-D scenes.Artificial Intell,1974,5:185-188
[44]Daini A.,Juberts M.Accurate remote measurement of robot trajectory motion.Proc.Int.Conference on Robotics and Automation,1985:92-99
[45]Martins H A,Berk j.R.,Kelly R.B.Camera models based on data from two calibration planes.Computer Graphics and imaging Processing,1981,17:173-180
[46]Faugeras O D,Maybank S.Motion from point matches:multiplicity of solutions.Intl.J.Computer Vision,1990,4:225-246
[47]Maybank S J,Faugeras O D.A theory of self-calibration of a moving camera.Intl.J.Computer Vision,1992,8(2):123-151
[48]马颂德,张正友.计算机视觉.北京:科学出版社,1998,1:52-76
[49]贾云得.机器视觉.北京:科学出版社,2000,4:65-75
[50]Z.Y.Zbang,R.Deriche and O.Faugeras et al.A robust technique for matching two uncalibrated images through the recovery of the unknown epipolor.Artificial intelligence Journal,1995.78(10):87-119
[51]Z.Y Zhang.Determining the epipolar geometry and its uncertainty:a review.The International Journal of Computer Vision,1998,27(2):161-195
[52]Z.Y Zhang.A New.Multistage Approach to Motion and Structure Estimation:From Essential Parameters to Euclidean Motion via Fundamental Matrix.Technical Report 2910,INRIA Sophia-Antipolis,France,June 1996
[53]王建琦,邓雁萍,李介谷.一种改进的角点提取方法.上海交通大学学报,34(7):913-916,2000.7
[54]C.Hchou and Y.C.Chen,Moment preserving pattern matching,Pattern Recognition.1990,23(5):461-474
[55]A.Goshtasby,S.H.Gage and J.F.Bartholic.A two-stage cross correlation approach to template matching.PAMI,May 1984,6(3):374-378
[56]C.Harris and T.Stephens.A combined corner and edge detector,the Fourth Alvey Vision Conference.1989:147-151
[57]Milan Sonka,Vaclav Hlavac,Roger Boyle.图像处理、分析与机器视觉(M).艾海舟,武勃译.北京:人民邮电出版社,2003:128-130
[58]杨枝灵,王开等.Visual C++数字图像获取、处理及实践应用(第一版).人民邮电出版社,2003
[59]陈延标,数字图像处理,北京:人民邮电出版社,1990
[60]贾永红,计算机图像处理与分析,武汉:武汉大学出版社,2001.9
[61]唐良瑞,马全明,景晓军等,图像处理实用技术,北京:化学工业出版社,2002.1
[62]Deok J.Park,Kwon M.Nam,Rae Hong Park,Edge Dection In Noisy Images Based On The Co-occurrence Matrix.Pattern Recognition,1994,27(6):765-775
[63]Wen Tsuen Chen,Chia Bsien Wen,Chin Wen Yang,A Fast Two—Dimensional Entropic Thresholding Algorithm.Pattern Recogngition,1994,27(7):885-893
[64]K.R.Castleman著,朱志刚,林学,石定机译,数字图象处理,电子工业出版社,1999
[65]张玲华,朱幼莲,用零交叉方法对图象进行边界检测的研究与实现,南京邮电学院学报,1997.17(3):46-50
[66]苑玮琦,王建军,张宏勋,一种基于梯度极值的边缘检测算法,信息与控制,1997,26(2):117-120
[67]李华,朱光喜,朱耀庭,一种基于方向曲线的目标检测方法,中国图象图形学报,1999,4(7):590-594
[68]王志凌,基于双CCD的大锻件尺寸测量的实验研究,[硕士学位论文],秦皇岛,燕山大学,2005
[69]John Canny.A Computational Approach to Edge Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698
[70]John Canny.Finding Edges and Lines in Images.MIT Artif Intell.Lab.,Cambridge,MA,Tech.Rep.AI-TR-720,1983
[71]S.O.Rice,Mathematical Analysis of Random Noise,Bell Syst Tech.J.,vol.24,pp.46-156,1945
[72]Canny J F,A Computational Approach to Edge Detection,IEEE Trans on PAMI 1986(6):679-698
[73]Hirschmuller H(2001) Improvements in real-time correlation-based stereo vision.In:Proceedings of IEEE workshop on stereo and multi-baseline vision,pp 141-148.Kauai.Hl
[74]Hong,L,Chen,G.:Segment-based stereo matching using graph cuts.In:CVPRO4.(2004) I:74-81
[75]Y.Nakamura et al,"Occlusion detectable stereo—Occlusion patterns in camera matrix."proe.IEEE CVPR'96,pp.371-378,San Francisco,June 1996
[76]高文,陈熙霖,计算机视觉—算法与系统原理,清华大学出版社,1999
[77]赵荣椿,数字图象处理导论,西北工业大学,1995
[78]张艳珍,微机视觉系统相关理论及技术研究,[博士学位论文],大连:大连理工大学,2001.4
[79]Levine M D.Handley O.Computer Determination of Depth Maps.Computer Graphics and Image Processing,1972,2,131-150
[80]Woebbecke D M,Meyer G E,Yon K Bargen,Mortensen D A.Shape Features for Identifying Young Weeds Using Image Analysis.Transactions of the ASAE,1995,38(1):271-281
[81]Tao Y,Heinemann P H,Varghese Z,Morrow C T,Sommer H J Ⅲ.Machine Vision far Color Inspection of Potatoes and Apples.Transactions of the ASAE,1995,38(5):1555-1561
[82]Guyer D E,Miles G E,Schreiber M M,Mitchell O R,Vanderbilt C.Machine Vision and Image Processing for Plant Identification.Transactions of the ASAE,1986,29(6):1500-1507
[83]石继雨,机器人双目立体视觉技术研究,[硕士学位论文]哈尔滨,哈尔滨工程大学,2003
[84]黄士海等.图像格式编程指南.北京:清华大学出版社1993
[85]ISO 8600-1:2005.Optics and photonics—Medical endoscope and endotherapy devices-Part1:Genernal requirements[s]
[86]ISO 8600-6:2005.Optics and photonics—edical endoscopes and endotherapy devices—Part 6:Vocabulary[S]
[87]ISO 9039:1994.Optics and Optical instruments-Quality evaluation of optical systems-Determination of distortion:1-3[S]
[88]何春燕等.双目立体视觉系统定标误差分析.中国体视学与图像分析.2003年12月(8):230-231
[89]Shin S W.When should we consider lens distortion in camera cali-bration[J].Pattern Recognition,1995.28(3)
[90]周永新,罗述谦.基于形状特征点最大互信息的医学图像配准.计算机辅助设计与图形学学报,2002,14(7):654-658
[2]Herbert Kaplan.A borescope that behaves like the human eye.PHOTONICS SPECTRA.SEPTEMBER 1994:4647.
[3]张阳德主编 内镜学 人民卫生出版社 2001年10月
[4]杨杰 中村仁彦 吴月华等 医用微型机械人 中国科学技术大学学报 第26卷第4期1996年12月
[5]袁启明 医用内窥镜的新进展 上海医疗器械研究所 中国医疗器械信息1997 3卷5期
[6]http://www.olympus.com.jp
[7]于辉,发动机故障图像的三维测量视觉重建与识别方法研究,[博士学位论文]南京:南京航空航天大学,2002。
[8]伍颂.赵建华,黄振佳.可测量视频内窥镜在无损检测中的应用.宇航材料工艺,2000(3):57
[9]王富治,高精度视觉测量系统研究,[硕士学位论文]合肥:电子科技大学,2005
[10]K.RADERMACHER,C.V.PICHLER,S.FISCHER,G.RAU,3D-Visualisation in Surgery Helmholtz-institute for Biomedical Engineering,Aachen University of Technology,Aachen(D)
[11]董太和,立体视觉原理,立体照相理论与实践讲座之四,浙江大学
[12]董太和,体视原理(上、下),立体照相理论与实距讲座之二、三,浙江大学
[13]K.Vijayan Asari,Sanjiv Kumar and D.Radhakrishnan,A new approach for nonlinear distortion correction in endoscopic image based on least squares estimation IEEE Transaction on Medical Imaging,1999,4(18):345-354
[14]Jie Wang,Yaorong Ge,An optimization problem in virtual endoscopy Theoretical Computer Science,207(1998)203-216
[15]孙慧,利用双目立体视觉技术重建电器触头立体图像,[硕士学位论文]天津,河北工业大学,2002
[16]吴夕,正常人双眼视觉的Panum融合区,眼科新进展,1999(9)375-376
[17]侯春萍,立体成像系统数学模型和视差控制方法,天津大学学报,2005,5,455-456
[18]颜树华等,立体视差法中距离分辨率的理论分析 光电工程1998 vol.25.No2
[19]Mayhew John E W,Frisby John P.3D Model StereocopicGues/M」.M assachusetts,London,England The M IT Press Cambridge,1991
[20]Victor S Grinberg,Gregg Podnar,Siegel M W.Geom etry of binocular imaging[A].In Proceedings of SPIE,Stereocopic Displays and VirucalSystem[C].San Jose,CA,USA:1994,2177:56-65
[21]白闻喜 医用内窥镜视放大率的测量原理及测试方法 长春光学精密机械学院学报 1994 vol.24 No.3
[22]张以漠,应用光学。机械工业出版社,1982
[23]王之江,光学设计手册。机械工业出版社,1987
[24]袁旭沧,应用光学。国防工业出版社,1988
[25]袁旭沧,现代光学设计方法。北京理工大学出版社
[26]Zobel;Jurgen Endoscope optical system United States Patent 4,723,843 1988
[27]向阳 电视腹腔镜系统 长春光学精密机械学院 硕士论文1996
[28]Tsai R Y.A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses.IEEE Journal of Robotics and Automation.1987,3(4):323-344
[29]Javier Ignzcio.Real-time 3D head position tracker system with stereo cameras using a face recognition neural network.[Doctor paper].UMI numbe.2004
[30]Jean hsu.Stereoscopic medical imaging.UMI.[Doctor paper].1995
[31]LatticeXP系列培训教程.上海莱迪斯半导体公司市场部,2005
[32]朱东魏 色度空间转换的设计及FPGA实现 电视技术2005.10
[33]王月兰,曾迎生.信息融合技术在彩色图像分割方法中的应用.计算机学报,2000年7月.23(7):763-767
[34]Tremeau A,Borel N A.Region growing and merging algorithm to color segmentation.Pattern Recognition,1997,30(7):1191-1203
[35]Henry R.Kang.Color Technology For Electronic Imaging Devices.SPIE-The International Society for Optical Engineering,1997
[36]Oliver D.Faugeras.Digital Color Image Processing Within the Framework of a Human Visual Model.IEEE Transactions on acoustics,speech and signal processing,ASSP-27(4)
[37]Tsai R Y.An efficient and accurate camera Calibration technique for recognition[C].Miami.FL.1986.364-374
[38]A.Basu,Activc Calibration of Cameras:Theory and lmplementation,IEEE Transactions onsystems,Man,and Cybernetics,vol,25,no.2,1995,pp 256-265.
[39]A.Basu and K.Ravi,Activc Camcra Calibration Using Pan,Roll-IEEE Transactions on systems,Man, and Cybernetics -PartB Cybernetics,vol.27,no.3,.1997,pp 559-566
[40]F.Li and M.Brady and C.Wiles,Fast Computation of the Fun-damental Matrix for an Active Stereo Vision System,4th European conference on computer Vision,Cambridge,UK,Apr.1996,pp 157-166
[41]S.Ganapaphy.Decomposition of transformation matrices for robot vision.Proc.Int.Conf.Robotics and Automation,1984:130-139
[42]W.Faig.Calibration of close-range photogrammetry systems:Mathematical formulation.Photogrammetric Eng.Remote sensing,1975,41(12):]1479-1486
[43]I.Sobel.On calibrating computer controlled cameras forperceiving 3-D scenes.Artificial Intell,1974,5:185-188
[44]Daini A.,Juberts M.Accurate remote measurement of robot trajectory motion.Proc.Int.Conference on Robotics and Automation,1985:92-99
[45]Martins H A,Berk j.R.,Kelly R.B.Camera models based on data from two calibration planes.Computer Graphics and imaging Processing,1981,17:173-180
[46]Faugeras O D,Maybank S.Motion from point matches:multiplicity of solutions.Intl.J.Computer Vision,1990,4:225-246
[47]Maybank S J,Faugeras O D.A theory of self-calibration of a moving camera.Intl.J.Computer Vision,1992,8(2):123-151
[48]马颂德,张正友.计算机视觉.北京:科学出版社,1998,1:52-76
[49]贾云得.机器视觉.北京:科学出版社,2000,4:65-75
[50]Z.Y.Zbang,R.Deriche and O.Faugeras et al.A robust technique for matching two uncalibrated images through the recovery of the unknown epipolor.Artificial intelligence Journal,1995.78(10):87-119
[51]Z.Y Zhang.Determining the epipolar geometry and its uncertainty:a review.The International Journal of Computer Vision,1998,27(2):161-195
[52]Z.Y Zhang.A New.Multistage Approach to Motion and Structure Estimation:From Essential Parameters to Euclidean Motion via Fundamental Matrix.Technical Report 2910,INRIA Sophia-Antipolis,France,June 1996
[53]王建琦,邓雁萍,李介谷.一种改进的角点提取方法.上海交通大学学报,34(7):913-916,2000.7
[54]C.Hchou and Y.C.Chen,Moment preserving pattern matching,Pattern Recognition.1990,23(5):461-474
[55]A.Goshtasby,S.H.Gage and J.F.Bartholic.A two-stage cross correlation approach to template matching.PAMI,May 1984,6(3):374-378
[56]C.Harris and T.Stephens.A combined corner and edge detector,the Fourth Alvey Vision Conference.1989:147-151
[57]Milan Sonka,Vaclav Hlavac,Roger Boyle.图像处理、分析与机器视觉(M).艾海舟,武勃译.北京:人民邮电出版社,2003:128-130
[58]杨枝灵,王开等.Visual C++数字图像获取、处理及实践应用(第一版).人民邮电出版社,2003
[59]陈延标,数字图像处理,北京:人民邮电出版社,1990
[60]贾永红,计算机图像处理与分析,武汉:武汉大学出版社,2001.9
[61]唐良瑞,马全明,景晓军等,图像处理实用技术,北京:化学工业出版社,2002.1
[62]Deok J.Park,Kwon M.Nam,Rae Hong Park,Edge Dection In Noisy Images Based On The Co-occurrence Matrix.Pattern Recognition,1994,27(6):765-775
[63]Wen Tsuen Chen,Chia Bsien Wen,Chin Wen Yang,A Fast Two—Dimensional Entropic Thresholding Algorithm.Pattern Recogngition,1994,27(7):885-893
[64]K.R.Castleman著,朱志刚,林学,石定机译,数字图象处理,电子工业出版社,1999
[65]张玲华,朱幼莲,用零交叉方法对图象进行边界检测的研究与实现,南京邮电学院学报,1997.17(3):46-50
[66]苑玮琦,王建军,张宏勋,一种基于梯度极值的边缘检测算法,信息与控制,1997,26(2):117-120
[67]李华,朱光喜,朱耀庭,一种基于方向曲线的目标检测方法,中国图象图形学报,1999,4(7):590-594
[68]王志凌,基于双CCD的大锻件尺寸测量的实验研究,[硕士学位论文],秦皇岛,燕山大学,2005
[69]John Canny.A Computational Approach to Edge Detection.IEEE Transactions on Pattern Analysis and Machine Intelligence,1986,8(6):679-698
[70]John Canny.Finding Edges and Lines in Images.MIT Artif Intell.Lab.,Cambridge,MA,Tech.Rep.AI-TR-720,1983
[71]S.O.Rice,Mathematical Analysis of Random Noise,Bell Syst Tech.J.,vol.24,pp.46-156,1945
[72]Canny J F,A Computational Approach to Edge Detection,IEEE Trans on PAMI 1986(6):679-698
[73]Hirschmuller H(2001) Improvements in real-time correlation-based stereo vision.In:Proceedings of IEEE workshop on stereo and multi-baseline vision,pp 141-148.Kauai.Hl
[74]Hong,L,Chen,G.:Segment-based stereo matching using graph cuts.In:CVPRO4.(2004) I:74-81
[75]Y.Nakamura et al,"Occlusion detectable stereo—Occlusion patterns in camera matrix."proe.IEEE CVPR'96,pp.371-378,San Francisco,June 1996
[76]高文,陈熙霖,计算机视觉—算法与系统原理,清华大学出版社,1999
[77]赵荣椿,数字图象处理导论,西北工业大学,1995
[78]张艳珍,微机视觉系统相关理论及技术研究,[博士学位论文],大连:大连理工大学,2001.4
[79]Levine M D.Handley O.Computer Determination of Depth Maps.Computer Graphics and Image Processing,1972,2,131-150
[80]Woebbecke D M,Meyer G E,Yon K Bargen,Mortensen D A.Shape Features for Identifying Young Weeds Using Image Analysis.Transactions of the ASAE,1995,38(1):271-281
[81]Tao Y,Heinemann P H,Varghese Z,Morrow C T,Sommer H J Ⅲ.Machine Vision far Color Inspection of Potatoes and Apples.Transactions of the ASAE,1995,38(5):1555-1561
[82]Guyer D E,Miles G E,Schreiber M M,Mitchell O R,Vanderbilt C.Machine Vision and Image Processing for Plant Identification.Transactions of the ASAE,1986,29(6):1500-1507
[83]石继雨,机器人双目立体视觉技术研究,[硕士学位论文]哈尔滨,哈尔滨工程大学,2003
[84]黄士海等.图像格式编程指南.北京:清华大学出版社1993
[85]ISO 8600-1:2005.Optics and photonics—Medical endoscope and endotherapy devices-Part1:Genernal requirements[s]
[86]ISO 8600-6:2005.Optics and photonics—edical endoscopes and endotherapy devices—Part 6:Vocabulary[S]
[87]ISO 9039:1994.Optics and Optical instruments-Quality evaluation of optical systems-Determination of distortion:1-3[S]
[88]何春燕等.双目立体视觉系统定标误差分析.中国体视学与图像分析.2003年12月(8):230-231
[89]Shin S W.When should we consider lens distortion in camera cali-bration[J].Pattern Recognition,1995.28(3)
[90]周永新,罗述谦.基于形状特征点最大互信息的医学图像配准.计算机辅助设计与图形学学报,2002,14(7):654-658