基于光电成像的特殊管道静态参数高精度测量技术
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摘要
高精度的特殊管道在兵器、航天、核能等诸多领域内有着广泛应用。其静态参数的检测是特殊管道设计、研究和使用中一个必需且重要的技术环节。随着特殊管道研制、生产水平和使用要求的不断提高,原来的基于光学望远系统的人工测量、人工处理数据的方法已经不能满足需要,因此研究开发高精度、高效率和自动化的特殊管道静态参数测量系统迫在眉睫。
作者在对特殊管道静态参数的测量原理、方法和检测装备的性能及其局限性进行深入分析的基础上,研究并设计了一种高精度的光机电一体化系统。论文以研究过程中遇到的特殊管道复杂环境的高精度光电成像测量为切入点,对测量过程中的图像获取、图像处理、图像测量以及测量数据的处理等多个关键技术进行了深入研究,提出了相应的技术方案,实践结果验证了其合理性和有效性。
首先,鉴于成像环境特殊又复杂,而获取图像像质要求较高,故必须借助人工照明系统,因此提出了相应的场景照明设计理念,并且设计了适合特殊环境图像获取的分布式LED光电成像照明光源,实践证明该光源能够保证在特殊复杂环境下获取较高质量测量需求的图像。
其次,LED照明光源在一定程度上使成像质量得到了提高,但是仍存在较多的噪声,还不能满足高精度测量要求,为此作者设计了基于小波和中值滤波相结合的自适应图像混合去噪方法和基于人眼视觉特性和模糊算子的图像增强算法,提出了基于MRF规整化非均匀照明背景的抑制算法,并进行了试验仿真,结果表明按上述方法预处理后图像的信噪比显著提高,适合于对靶标图像的预处理。同时由于传统的图像分割方法很难满足特殊管道高精度图像测量的要求,本文又分别提出了基于蚁群算法和最大熵相结合的多阈值图像分割算法、基于局部直径Hough变换的测量靶面同心圆自适应检测方法。完成了相应的算法仿真试验,结果表明图像经过上述方法处理后能满足测量要求。
再次,为了满足特殊管道静态参数高精度测量的需求,本文又分别提出了基于高斯曲线拟合的光斑图像亚像素检测方法及基于小波和Zernike矩的标尺靶亚像素检测算法；同时针对像机畸变对高精度测量的影响,提出了基于移动特征靶标的径向畸变标定方法、基于递推最小二乘法像机径向畸变校正方法及基于特征平行直线的几何畸变校正方法；此外在分析了系统误差源的基础上,提出了基于序列图像特征配准的像机旋转误差补偿算法。仿真试验结果显示这些方法能够提高图像测量精度并且减小和消除误差。
最后,建立了测量数据处理的部分数学模型,引入了基于三次NURBS拟合的身管空间轴线三维重建算法及基于三次样条插值的药室容积和内表重建算法；此外针对便携式设计提出了基于DSP实时实现的硬件方案。
High precision special type tube is widely used in many fields such as weapon, spaceflight and nuclear energy etc. Measurement of the static parameters is a prerequisite and important technique process in designing, studying and use of the special type of tube. With the development of study, manufacture and requirement of application, the classic measurement method based on optics glass and data processing artificially cannot satisfy new requirements of the special type of tube. A kind of high precision, high efficiency, and automatism measurement system need be constructed urgently.
Based on thorough study of the measuring principle, measuring method and performance and limitation of the special type of tube, a project to the tube static parameters measurement system with high precision and automatism are designed which integrate the technical of optical, fine mechanism and electron. The critical technical in image obtain, image process, image measurement and data process is studied deeply to the point of accuracy photoelectric imaging measurement in special type tube complex environment during the design. As a result, a series of suitable technologies have been put forward. The feasibility and effectiveness have been verified by practice.
Firstly, because an excellent image must be obtain in the special and complex imaging environment, artificial illumination method should be used. In order to get a relatively "good" image, a scene illumination system design idea has been used and a kind of distributed-LED artificial illumination light source which fits the image obtain in special and complex image environment has been designed and practice shows that the designing can ensure obtain high quality image for measure in the special and complex image environment.
Secondly, benefit from LED illumination light source, the image quality is improved in some degree but far from the requirement of high precision measurement and include some noise still. So a series algorithms which suit image obtained in the special and complex environment, such as the image denoise means mixed wavelet and self-adaptive median filter, the image enhancement based on human visual property and generalized fuzzy operator have been adopt here,and a novel nonuniform illumination background suppress algorithm based on markov random field model designed. Simulation and test shows that these image preprocess algorithms can improve the SNR greatly and can meet the requirement for the image quality of the later image process. At the same, as the classic image segmentation methods cannot satisfy the need of high accuracy image measurement of special tube, some new methods are put forward to suit the segmentation special image, which include the multi-threshold image segmentation algorithms based on ant optimization and maximum fuzzy entropy, the concentric circles adaptive high accuracy detection algorithms based on local diameter Hough transform. Simulation and test indicate the image process methods can satisfy the need of the subsequent image measure.
Thirdly, in order to realize the special type tube static parameters high accuracy measurement, we invent some new algorithms which include the sub-pixel edge detection algorithm for laser spot based on gauss plotfit and sub-pixel detect algorithms for rule target based on wavelet transform and Zernike moments; Simultaneity, in consequence of the lens distortion serious influence the measurement precision, a few lens calibrating and correction methods are construct such as the lens radial distortion calibrating method based on moving feature target, the lens radial distortion correction algorithm based on recursive least squares, the geometrical distortion correction method based on property parallel lines. Moreover, after analyzing the system-error in detail, a kind of camera rotation compensation algorithms based on sequence image frame feature matching. Result shows that the methods can improve the image measurement accuracy and can reduce even eliminate the error.
Lastly, the measurement mathematical model are construct partly, the tube space three-dimension axial reconstruct algorithm based on Cubic NURBS plot-fitting and the power chamber volume reconstruction based on three order spline interpolation algorithm are put forward, and these algorithm can get super effect. Meanwhile, aim at the portable design a real-time hard-ware project based on DSP is designed.
作者在对特殊管道静态参数的测量原理、方法和检测装备的性能及其局限性进行深入分析的基础上,研究并设计了一种高精度的光机电一体化系统。论文以研究过程中遇到的特殊管道复杂环境的高精度光电成像测量为切入点,对测量过程中的图像获取、图像处理、图像测量以及测量数据的处理等多个关键技术进行了深入研究,提出了相应的技术方案,实践结果验证了其合理性和有效性。
首先,鉴于成像环境特殊又复杂,而获取图像像质要求较高,故必须借助人工照明系统,因此提出了相应的场景照明设计理念,并且设计了适合特殊环境图像获取的分布式LED光电成像照明光源,实践证明该光源能够保证在特殊复杂环境下获取较高质量测量需求的图像。
其次,LED照明光源在一定程度上使成像质量得到了提高,但是仍存在较多的噪声,还不能满足高精度测量要求,为此作者设计了基于小波和中值滤波相结合的自适应图像混合去噪方法和基于人眼视觉特性和模糊算子的图像增强算法,提出了基于MRF规整化非均匀照明背景的抑制算法,并进行了试验仿真,结果表明按上述方法预处理后图像的信噪比显著提高,适合于对靶标图像的预处理。同时由于传统的图像分割方法很难满足特殊管道高精度图像测量的要求,本文又分别提出了基于蚁群算法和最大熵相结合的多阈值图像分割算法、基于局部直径Hough变换的测量靶面同心圆自适应检测方法。完成了相应的算法仿真试验,结果表明图像经过上述方法处理后能满足测量要求。
再次,为了满足特殊管道静态参数高精度测量的需求,本文又分别提出了基于高斯曲线拟合的光斑图像亚像素检测方法及基于小波和Zernike矩的标尺靶亚像素检测算法；同时针对像机畸变对高精度测量的影响,提出了基于移动特征靶标的径向畸变标定方法、基于递推最小二乘法像机径向畸变校正方法及基于特征平行直线的几何畸变校正方法；此外在分析了系统误差源的基础上,提出了基于序列图像特征配准的像机旋转误差补偿算法。仿真试验结果显示这些方法能够提高图像测量精度并且减小和消除误差。
最后,建立了测量数据处理的部分数学模型,引入了基于三次NURBS拟合的身管空间轴线三维重建算法及基于三次样条插值的药室容积和内表重建算法；此外针对便携式设计提出了基于DSP实时实现的硬件方案。
High precision special type tube is widely used in many fields such as weapon, spaceflight and nuclear energy etc. Measurement of the static parameters is a prerequisite and important technique process in designing, studying and use of the special type of tube. With the development of study, manufacture and requirement of application, the classic measurement method based on optics glass and data processing artificially cannot satisfy new requirements of the special type of tube. A kind of high precision, high efficiency, and automatism measurement system need be constructed urgently.
Based on thorough study of the measuring principle, measuring method and performance and limitation of the special type of tube, a project to the tube static parameters measurement system with high precision and automatism are designed which integrate the technical of optical, fine mechanism and electron. The critical technical in image obtain, image process, image measurement and data process is studied deeply to the point of accuracy photoelectric imaging measurement in special type tube complex environment during the design. As a result, a series of suitable technologies have been put forward. The feasibility and effectiveness have been verified by practice.
Firstly, because an excellent image must be obtain in the special and complex imaging environment, artificial illumination method should be used. In order to get a relatively "good" image, a scene illumination system design idea has been used and a kind of distributed-LED artificial illumination light source which fits the image obtain in special and complex image environment has been designed and practice shows that the designing can ensure obtain high quality image for measure in the special and complex image environment.
Secondly, benefit from LED illumination light source, the image quality is improved in some degree but far from the requirement of high precision measurement and include some noise still. So a series algorithms which suit image obtained in the special and complex environment, such as the image denoise means mixed wavelet and self-adaptive median filter, the image enhancement based on human visual property and generalized fuzzy operator have been adopt here,and a novel nonuniform illumination background suppress algorithm based on markov random field model designed. Simulation and test shows that these image preprocess algorithms can improve the SNR greatly and can meet the requirement for the image quality of the later image process. At the same, as the classic image segmentation methods cannot satisfy the need of high accuracy image measurement of special tube, some new methods are put forward to suit the segmentation special image, which include the multi-threshold image segmentation algorithms based on ant optimization and maximum fuzzy entropy, the concentric circles adaptive high accuracy detection algorithms based on local diameter Hough transform. Simulation and test indicate the image process methods can satisfy the need of the subsequent image measure.
Thirdly, in order to realize the special type tube static parameters high accuracy measurement, we invent some new algorithms which include the sub-pixel edge detection algorithm for laser spot based on gauss plotfit and sub-pixel detect algorithms for rule target based on wavelet transform and Zernike moments; Simultaneity, in consequence of the lens distortion serious influence the measurement precision, a few lens calibrating and correction methods are construct such as the lens radial distortion calibrating method based on moving feature target, the lens radial distortion correction algorithm based on recursive least squares, the geometrical distortion correction method based on property parallel lines. Moreover, after analyzing the system-error in detail, a kind of camera rotation compensation algorithms based on sequence image frame feature matching. Result shows that the methods can improve the image measurement accuracy and can reduce even eliminate the error.
Lastly, the measurement mathematical model are construct partly, the tube space three-dimension axial reconstruct algorithm based on Cubic NURBS plot-fitting and the power chamber volume reconstruction based on three order spline interpolation algorithm are put forward, and these algorithm can get super effect. Meanwhile, aim at the portable design a real-time hard-ware project based on DSP is designed.
引文
[1]樊成军.身管弯曲对火炮射击精度影响分析[D].南京：南京理工大学,2002.1-10.
[2]浦发.炮口角的形成、估算及其对跳角和射击的影响[J].兵工学报,1983,4(1)：1-8.
[3]谷向阳.PHL96-300型武器系统定向管参数测量原理与方法研究[D].南京：南京理工大学,2001,3.
[4]赵敏,袁志鹏.液压缸缸筒直线度测量仪的设计[J].液压气动与密封,2001,21(6)：24-27.
[5]季立江,廖俊必,史伟.基于图像处理的炮管轴线直线度检测方法[J].兵工自动化,2002,21(6)：29-32.
[6]刘战锋,宁延平,刘雁蜀.精密深孔直线度测量技术的现状与发展[J].新技术新工艺,2004,31(11)：36-39.
[7]沙吉乐,邾继贵,杨学友,等.大直径钢管直线度在线测量研究[J].仪器仪表学报,2001,22(S3)：55-56,77.
[8]R S Lu, Y F Li, Q Yu. On-line measurement of the straightness of seamless steel pipes using machine vision technique[J]. Sensors and Actuators, A:Physical,2001, 94(1-2):95-101.
[9]庞厚金.大缸体内圆直线度的测量与分析[J].工业计量,2001,11(6)：41-42.
[10]贺安之,吕爱民,闫大鹏等.火炮身管弯曲度、炮口角检测系统的研究[J].弹道学报,1997,9(3)：59-62.
[11]袁红星,吕爱民,王志兴等.PSD法测量直管弯曲度的步长优化研究[J].弹道学报,1998,10(2)：54-57,63.
[12]马宏,潘毓学,车英,等.炮管直线度激光检测设备研究[J].兵工学报,2003,24(2)：261-263.
[13]周韦琴,马宏.炮管直线度测量仪的研制[J].计算机测量与控制,2006,14(7)：929-930,958.
[14]郑浩,刘思海.一种改进的弹体药室容积音频检测仪[J].仪器仪表学报,2006,27(6)：238-240.
[15]柏逢明.音频检测技术[M].北京：中国科学技术出版社,2005.
[16]柏逢明.音频检测弹体药室容积的研究[J].兵工学报.1998,19(2)：167-170.
[17]张文伟,张延忻.基于环形光切图像法的管内壁激光测量系统[J].计量学 报,2001,22(4)：284-287.
[18]Zhang W W, Zhuang B H. Non-contact laser inspection for the inner wall surface of a pipe Meas.Sci.Technol.1998,9:1380-1387.
[19]王坚,单长胜.一种药室参数测量新方法[J].测试技术学报.2006，20(1)：27-40.
[20]Mandayam S, Upda L, Upda S. S et al. Invariance transformations for magnetic flax leakage signals. IEEE Transactions on Magnetics,1996,32(3):1588-1580.
[1]北京嘉恒中自图像技术有限公司.机器视觉系统(第二版)[Z].北京：北京嘉恒中自图像技术有限公司,2006.3-10.
[2]王庆有.CCD应用技术[M].天津：天津大学出版社,2000.27-29,164-166.
[3]浦昭邦,屈玉福,王亚爱.视觉检测系统中照明光源的研究[J].仪器仪表学报,2003,24(S4)：438-439.
[4]李俊.机器视觉照明光源关键技术研究[D].天津：天津理工大学,2006.23-25.
[5]范伟,王毅,扰瑞中.根据表面反射特性进行目标探测波段选择[J].光子学报,2006,35(5)：755-759.
[6]周太明.光源原理与设计[M].上海：复旦大学出版社,1993.15-325.
[7]Kuhn.Gerhard. A new LED light source for projection applications[J], Digest of Technical Papers,2005,1702-1705.
[8]Tsai,Shau-Yu. LED Illumination light source for projection display, Proceedings of the International Display manufacturing Conference and Exhibition, IDMC'05,2005,P240-242.
[9]Moe, Andrew E. Enhanced fluorescence emission using a programmable, reconfigurable LED-array based light source,26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,EMBC 2004,P2920-2093.
[10]李炳乾.LED环形光源的研制[J].上海：照明工程学报,2007.18(1)64-66.
[11]Eberhardt E H. Source detector spectral matching factors[J].Applied optics,196-8,7 (10):2037-3047.
[12]G.e.Giakoumkis. Matching factors for Various Light-Source Photodetector Combinations[J]. Applied Physics A,1991,52:7-9.
[13]张以谟,应用光学[M].北京：机械工业出版社,1982.
[14]Y.Susu,L.Weisi,O.Eeping,et al.Contrast signal-to-noise ratio for image quality assessment. IEEE International Conference on Image Processing,2005,1:397-400.
[15]Zhou Wang, Hamid R.Sheikh,and Alan C.Bovik. No-Reference perceptual quality Assessment of JPEG compressed images[C].Proc.IEEE Int. Conf. Image Proc.Step.2002.
[16]宋璧.无参考数字图像质量评价研究[D]中国科学技术大学硕士论文.2004,63-70.
[17]S. Winkler, P. Vandergheynst, Computing isotropic local contrast from oriented pyramid decompositions[C]. Proceedings of ICIP,1999:420-424.
[18]杨志文.光学测量[M].北京：北京理工大学出版社,1995年8月.
[19]王之江.成像光学[M].北京：科学出版社,1991.
[20]E.Peli. Contrast in complex images[J]. Journal of Optical Society of America. 1990,7(10):2032-2040.
[21]S.winker. Issues in Vision Model for Perceptual Video Quality Assessment[J]. Signal Processing.1999,78(2).
[22]K.Hosaka. A New Picture Quality Evaluation Method. Proceedings of the International Picture Coding Symposium[C],1986.
[23]肖文.空域无参考的图像质量评价[M].江西财经大学硕士学位论文,2006：35-45.
[24]牛建军.特殊环境下光电图像获取与处理技术[D].西安：西安电子科技大学,2007.26-39.
[25]M. A. Cayless, A. M. Marsden.Lamps and Lighting(Thrid Edition)[M].Edward Arnold Ltd,1983.
[26]M Oren,.S.K. Nayar. Generalization of the Lambertian Model and Implication for Machine Vision[J]. Journal of Computer Vision,1995,14(3):277-251.
[27]LB Wolff, SK Nayar, M Oren.Improved Diffuse Reflection Models for Computer Vision[J]. International Journal of Computer Vision,1998,30(1):55-71.
[28]Ning.L, Zongqian. L.Bi-Spectrum Scattering Model for Conducting Randomly Rough Surface[J].Tsinghua Science and Technology,2002,7(3):309-316.
[29]Vesselin Iossifov Shaoulov. Design And Assessment Of Compact Optical Systems Towards Special Effects Imaging [D].Florida:University of Central Orlando Florida,2005.
[1][美]Rafael C Gonzalez, Richard E Woods数字图像处理(第二版)[M].北京：电子工业出版社,2003.176-181.
[2]XU Xiu-zhen, LI Zi-tian, XUE Li-jun. Analysis and processing of CCD noise[J]. Infrared and laser Engineering,2004,33(8),343-347.
[3]ZHANG Li-na, WU Xiao-qin, HU Xue-you. Reach on CCD Noise Signal Proce-ssing [J]. Third International Conference on Natural Computation,2007,1-4.
[4]王庆有.CCD应用技术[M].天津：天津大学出版社,2000,164-166.
[5]佟首峰,阮锦,郝志航.CCD图像传感器降噪技术的研究[J].光学精密工程,2000,8(2)：140-145.
[6]安连生,李林,李全臣.应用光学(第2版)[M].北京：北京理工大学出版社,2000.90-91.
[7]范志刚,左保军,张爱红.光电测试技术(第二版)[M].北京：电子工业出版社,2008.
[8]P.Mackey, F.R.Barone, N.A.Chu.Real-time non-uniformity correction for focal plane arrays using 12-bit digital electronics[J]. SPIE,1981, (292):210.
[9]Stephen D. Ford, B M. Welsh. M C. et al. Reconstruction of low-light images by use the vector filter[J].J Opt Soc Am,1997,14(10):2678-2691.
[10]A.Murli,L. D'Amore,V.De Simone. The Wiener Filter and Regularization Methods for Image Restoration Problems[C].ICASSP-93.Minneapolis USA:[sn],1993:285-288.
[11]谢杰成,张大力,徐文立.小波图象去噪综述[J].中国图象图形学报,2002,7A(3)：209-217.
[12]Raghuram Rangarajan, Ramji Venkataramanan, Siddharth Shah. Image Denoising Using wavelets[EB/OL].http://www-personal.engin.umich.edu/-rangaraj/551/ Report,Pdf,2004-02.
[13]张旗,梁德群,樊鑫,等.基于小波域的图像噪声类型识别与估计[J].红外与毫米波学报,2004,23(4)：281-285.
[14]关新平,赵立兴,唐英干.图像去噪混合滤波方法[J].中国图象图形学报,2005,10(3)：332-337.
[15]KWON H,DER S Z,NASRABADI N M. Multisensor target detection using adaptive feature based fusion[C]. SPIE,2001,4379:112-123.
[16]Farook Sattar, Lars Floreby, Goran Salomonsson, et al. Image enhancement based on a nonlinear multiscale method[J]. IEEE Transactions on Image Processing,1997, 6(6):888-895.
[17]杜惊雷,黄奇忠,姚军等.灰阶掩模实现光学邻近校正及计算模拟研究[J].光学学报,1999,(19)5：698-673.
[18][法] Henri Maitre等著,孙洪译.现代数字图像处理[M].北京：电子工业出版社,2006：174-176.
[19]陈冬青.基于照明反射模型的内窥镜图像增强方法研究[J).中国生物医学工程学报,2000,19(4)：20-22.
[20]Sang J. K, Nam Y. K, Byung G. L,etal. Wavelet based uneven illumination compensation for defect detection of flat panel display[C]. Proceedings of the Fourth conference on IASTED International Conference:Signal Processing, Pattern Recognition, and Applications, 2007,104-108.
[21]邹谋炎.反卷积和信号恢复[M].北京：国防工业出版社,2001.
[22]Pan R, Reeves S J. Efficient huber-Markov edge-preserving image restoration[J]. IEEE Transactions on Image Processing,2006,15(12):3728-3735.
[23]Tartakovsky A, Blazek R. Effective adaptive spatial-temporal technique for clutter rejection in IRST[C]. SPIE,2000,4048:85-95.
[24]J. G. M. Schavemaker. Image Sharpening by Morphological Filtering[J]. Pattern Recognition,2000,33:997-1012.
[25]Van den Boomgaard R,Smeulders A. The morphological structure of images,the differential equations of morphological scale-space[J]. IEEE Transactions PAMI,1994,16(11):1101-1113.
[26]S.K.Pal and R.A.King. On edge detection of X-ray images using fuzzy sets[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1983,5(1):69-77.
[27]Pal S K, King R A, Image Enhancement using smoothing with Fuzzy Sets[J]. IEEE Trans syst. Man and cybern,1981,11(7):494-501.
[28]H. R.Tizhoosh,G.Krell,B.Michaelis, Locally Adaptive Fuzzy Image Enhancement [C]. Lecture Notes In Computer Science, Proceeding of International Conference on Computational Intelligence,Theory and Applications.1997,1226:272-276.
[29]Zadeh, L. A., Fuzzy Sets. Information and Control 8[M].1965:338-353.
[30]郭桂容,庄钊文.信息处理中的模糊技术[M].长沙：国防科技大学出版社,1993.
：31]章毓晋.图象分割[M].北京：科学出版社,2001.2-3.
'32] K S Fu, J K Mui. A survey of image segmentation[J]. Pattern Recognition,1981, 13(1):3-16.
33] NR Pal, SK Pal. A review on image segmentation techniques[J]. Pattern Recognition,1993,26(9):1277-1294.
34] Sahoo P K etal. A survey of thresholding techniques[J]. Computer Vision, Graphics and Image Processing,1988,41:233-260.
35] Nikhil R. Pal. Sankar K. Pal, A review on image segmentation techniques [J]. Pattern Rcognition 1993,26(9):1277-1294.
36]王新成.高级图象处理技术[M].北京：中国科学技术出版社.2000.
37] Nobuyuki Otsu. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics,1979, SMC-9(1):62-66.
[38]A Magid, S R Rotman, A M Weiss. Comment on "Picture thresholding using an iterative selection method"[J]. IEEE Transactions on Systems, Man, and Cybernetics,1990,20(5):1238-1239.
[39]D. Liu, et al. A fuzzy classification entropy approach to image thresholding [J].Pattern Recognition Letters,2006,27:1968-1975.
[40]E.David Jansing, Thomas A.Albert, et al. Two-dimensional entropic segmentation [J].Pattern Recognition Letters,1999,20(3):329-336.
[41]Kittler J, Illingworth J. On threshold selection using clustering criteria[J]. IEEE Trans,1985, SMC-15:652-655.
[42]Pun T. A new method for gray-level picture thresholding using the entropy of histogram[J]. Signal Processing,1980,2:223-237.
[43]Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision, Graphics and Image Processing,1985,29:273-285.
[44]高新波.模糊聚类分析及其应用[M].西安：西安电子科技大学出版社2004.37.
[45]Jagath C Rajapakse, Jay N Giedd, Judith L Rapoport. Statistical approach to segmentation of single-channel cerebral MR images[J]. IEEE Transactions on Medical Imaging,1997,16(2):176-186.
[46]W M Wells Ⅲ, W E L Grimson, R Kikinis, et al. Adaptive segmentation of MRI data[J]. IEEE Transactions on Medical Imaging,1996,15(4):429-442.
[47]W E Blanz, Sheri L Gish. A connectionist classifier architecture applied to image segmentation A]. Proceedings of the 10th International Conference on Pattern Recognition[C]. New Jersey, USA,1990,2:272-277.
[48]Noboru Babaguchi, Koji Yamada, Koichi Kise, et al. Connectionist model binarization[A]. Proceedings of the 10th International Conference on Pattern Recognition[C]. New Jersey, USA,1990,2:51-56.
[49]A Ghosh, N R Pal, S K Pal. Image segmentation using a neural network[J]. Biological Cybernetics,1991,66(2):151-158.
[50]Gilles Aubert, Pierre Kornprobst. Mathematical problems in image processing[M].北京：世界图书出版公司北京公司,2005.129-179.
[51]Stanley Osher, Leonid I Rudin. Feature-oriented image enhancement using shock filters[J]. SIAM Journal on Numerical Analysis,1990,27(4):919-940.
[53]Pietro Perona, Jitendra Malik. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(7):629-639.
[54]杨纶标.模糊数学原理及应用[M].广州：华南理工大学出版社,2005.
[55]N R Pal, S K Pal. Object-background segmentation using new definitions of entr-opy[J]. IEE Proceedings on Computers and Digital Techniques, Part E,1989,136 (4):284-295.
[56]Dorigo M.Ant System:Optimization by a Colony of Cooperating Agents[J]. IEEE Transactions on Systems,Man and Cybernetics,PartB,1996,26 (1):29-41.
[57]M.Dorigo,C.Blum.Ant colony optimization theory:A survey[J]. Theor -etical Computer Science,2005,344:243-278.
[58]T.Wengbing, Jin.H.Object segmentation using ant colony optimization algorithm and fuzzy entropy[J].Pattern Recognition Letters,2007,28(7):788-796.
[59]Han.N, Shi.P. An improved ant colony algorithm for fuzzy clustering in image segmentation[J].Neurocomputer,2007,70:665-671.
[60]Blum C.Ant Colony Optimization:Introduction and Hybridizations[C]. Seven International Conference on Hybrid Intelligent System,2007.1-6.
[61]段海滨.蚁群算法—原理及应用[J]北京：科学出版社,2005.
[62]M.Dorigo,G. DiCaro, L.M.Gambardella. Ant algorithms for discrete optimization[J]. Artificial Life,1999,(5)2:137-44.
[63]牛建军,刘上乾等.同心圆检测中的区域划分算法[J].光子学报,2006,35(12)：1945-1949.
[64]Faugeras.Three-Dimensional Computer Vision[M].MIT Press,1993.
[65]刘波.用Hough变换识别二维图像[J].光子学报,1992,22(2)：32-38.
[66]王建勇,周晓光等.一种改进的直线检测算法[J].计算机工程,2006,32(16)：172-174.
[67]Xing Cao, Farzin Deravi. An efficient method for the detection of multiple concentric circles [J].IEEE 3rd.International Conference on Computer Vision,Osaka,Japan,4-7,Dec,1990,744-747.
[68]黄桂平.圆形标志中心子像素定位方法的研究与实现[J].武汉大学学报报(信息科学版),2005,30(5)：388-391.
[1]Z. Zhang. Flexible camera calibration by view a plane from unknown orientation [C].IEEE Intern-ational Conference on Computer Vision,1999,666-673.
[2]E. Trucco, A. Verri. Introductory Techniques for 3-D Computer Vision[M]. New Jersey:Prentice Hall,1998.
[3]S. Hutchinson, G. D. Hanger, and P. I. Corke. A tutorial on visual servo control[C]. IEEE T-ransactions on Robotics and Automation, vol.12, no.5, pp.651-670, Sept. 1996.
[4]马颂德,张正友.计算机视觉[M].北京：科学出版社.
[5]Slama CC. Manual of Photogrammetry.4th ed,American Society of Photogram-metry.1980.
[6]Tsai R Y. A Versatile Camera Calibration Technique for High-accuracy 3D Machine Vision Metrology Using Off the Shelf TV Cameras and Lenses[J]. IEEE Journal of Robotics and Automation,1987,RA-3(4):323-344.
[7]Roger Y Tsai. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. IEEE Journal of Robotics and Automation,1987, RA-3(4):323-344.
[8]Frederic Devernay, Olivier Faugeras. Automatic calibration and removal of distortion from scenes of structured environments[A], Investigative and Trial Image Processing, SPIE[C]. San Diego, USA,1995,2567:62-72.
[9]张以莫.应用光学[M].北京：机械工业出版社,1995.244-245.
[10]H. Hideak i, N.Y Yagihash i, Y. Miyake. A new mothed for distortion correction of electronic endoscope image[J]. IEEE Trans. Med. Imag,1995,14:548-555.
[11]K.Vijayan Asari,Sanjiv Kumar, D.Radhakrishnan. A New Approach for Nonlinear Distortion Correction in Endoscopic Images Based on Least Squares Estimation[J]. IEEE Transactions on Medical Imaging,VOL.18.NO.4,APRIL 1999.345-354.
[12]Lee C B. Radial un-distortion and calibration on an image array[D].Electrical and Computer Science Dept, Massachusetts Institute of Technology,2000.
[13]Brauer-Burchardt C,Voss K. Automatic lens distortion calibration using single views[A].In:G. Sommer,N. Krugre, C.Perwass,eds Musterkennung 2000. Procee-dings of DAGM-Symposium Kiel,Germany,Informatik Aktuell Springer, 2000.187-194.
[14]Devernay F, Faugeras O. Straight lines have to be straight automatic calibration and removal of distortion from scenes of structured environments[J].Machine Vision and Applications,2001,13(1):14-24.
[15]Swaminathan R,Nayar S K. Non-metric calibration of wide-angle lenses and plo-ycameras[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(10):1172-1178.
[16]Farid H, Popescu A C. Blind removal of lens distortion[J].Journal of the Optical Society of America [A],2001,18(9):2072-2078.
[17]席少霖,赵凤治编著.最优化计算方法[M].上海：上海科学技术出版社,1983.
[18]于起峰,陆宏伟等.基于图像的紧密测量和运动测量[M].北京：科学出版社2002,118-166.
[19]张金利,王元庆等.大视角成像系统的快速精确校正[J].光电子·激光,2006,17(2)：158-161.
[20]邓志鹏,杨杰,全勇.景象匹配中的快速畸变校正算法[J].上海交通大学学报,2003,37(S1)：75-78.
[21]陈华旺.图像匹配技术在定位校射中的应用研究[D].西安：西北工业大学,2004.10-11.
[22]Y.Hatada,S.Iwane,H.Tohno,T.Baba,et al. A new method for the measurement of the gastric and colonic lesions with an electronic endoscope and image processor[J],Gastrointestinal Endoscopy,1991,37:275.
[23]C.Margulies,B.Krevsky,et al. How accurate are endoscopic estimates of size[J].Gastrointestinal Endoscopy,1994,40:174-177.
[24]Warren E Smith, Nimish Vakil, Seth A Maislin. Correction of distortion in endoscope images[J]. IEEE Transactions on Medical Imaging,1992,11(1): 117-122.
[25]K Vijayan Asari, Sanjiv Kumar, D Radhakrishnan. A new approach for nonlinear distortion correction in endoscopic images based on least squares estimation[J]. IEEE Transactions on Medical Imaging,1999,18(4):345-354.
[26]牛建军,刘上乾,姚荣辉,等.高准确度光电成像测量系统图像畸变校正算法[J].光子学报,2006,35(9)：1317-1320.
[27]陈鼎才,王定成,查金水.基于机器视觉的现实叶片面积测量方法的研究[J].计算机应用,2006,26(5)：1226-1228.
[28]郑毅,刘上乾.利用直线特征的定标图像非线性畸变校正[J].仪器仪表学报.2007,28(6)：1 1 29-1 1 32.
[29]Frederic Devernay, Olivier Faugeras. Straight lines have to be straight[J]. Machine Vision and Applications,2001,13(1):14-24.
[30]Wolfram Mathworld the web's most extensive mathematics resource. [EB/OL]. (2008-5-07). http://mathworld.wolfram.com/LeastSquaresFitting.html.
[1]段瑞玲,李庆祥,李玉和.图像边缘检测方法研究综述[J].光学技术,2005,31(3)：415-419.
[2]冈萨雷斯著.阮秋琦,阮宇智译.数字图像处理(第二版)[M]北京：电子工业 出版社,2005：460-470.
[3]Canny, J.F.A computational approach to edge detection[J]. IEEE Trans Pattern Ana-lysis and Machine Intelligence,8(6):679-698, Nov 1986.
[5]Stephane Mallat, Wen Liang Hwang. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory,1992,38(2):617-643.
[6]B S Penn, A J Gordon, R F Wendlandt. Using neural networks to locate edges and linear features in satellite images[J]. Computers and Geosciences,1993,19(10): 1545-1565.
[7]Zhengquan He, M Y Siyal. Edge detection with BP neural networks[A].1998 Fourth International Conference on Signal Processing Proceedings[C]. Beijing, China,1998, 2:1382-1384.
[8]Leon O Chua, Lin Yang. Cellular neural networks:theory[J]. IEEE Transactions on Circuits and Systems,1988,35(10):1257-1272.
[9]Yoshifumi Uchiyama, Miki Haseyama, Hideo Kitajima. Hopfield neural networks for edge detection[A]. The 2001 IEEE International Symposium on Circuits and Systems[C]. Sydney, Australia,2001,3:608-611.
[10]Md Shoaib Bhuiyan, Yuji Iwahori, Akira Iwata. Stepped-down coefficient values associated with Hopfield nets improve optimal edge detection[A].1999 IEEE International Conference on Systems, Man, and Cybernetics[C]. Tokyo, Japan, 1999,5:377-382.
[11]Siwei Lu, Ziqing Wang. Fuzzy neural networks for edge detection[A]. IEEE 1997 Canadian Conference on Electrical and Computer Engineering[C]. St. John's, Canada,1997,2:446-449.
[12]朱铮涛.基于计算机视觉图像精密测量的关键技术研究[D].广州：华南理工大学,2004.54.
[13]P.J.Macvicar-Whelan,T.O.Binford. Line Finding with Sub-pixel Precision[M].Proc. 1981,281:201-206.
[14]V.S.Nalwa,T.O.Binford. On detecting edges[J]. IEEE Trans. Patern Anal. Machine Intell,1986,8:699-714.
[15]S.Suthaharan. Image an edge detail detection algorithm for object-based coding[J]. Pattern Recognition,2000,21:549-557.
[16]R. Machuca,A.L. Gilbert. Finding Edges in Noisy Scenes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1981,3(1):103-111.
[17]Edward P. Lyvers,Owen Robert Mitchell. Sub-pixel Measurements Using a Moment-based Edge Operator[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,1989,11 (12):1293-1307.
[18]张广军.衍射条纹CCD拟合定位方法及精度分析[J].仪器仪表学报,1995,16(1)：107-110.
[19]曹国容,景芳盛.CCD用于莫尔条纹细分的技术[J].计量学报1995,16(2)：116-119.
[20]Yu. Shan Li, Tzay Y. Yong. Sub-pixel Edge Detection and Estimation with a microprocessor Controlled Line Scan Camera[J]. IEEE Transactions on Industrial Electronics,1989,35(1):105-107.
[21]Cuilian Zhao, Yu Chen. A New Liquid Level Measuring System of Standard Metal Tank Based Sub-pixel Edge Detection[C].IEEE International Conference on Control and Automation, Guangzhou China:2007.3206-3212.
[22]Toshiaki Watanabe. Image coding making use of B-spline surfaces[J]. IEEE Transactions on Circuits and Systems for Video Technology,1997,7(2):409-413.
[23]Frederic Bouchara, Marc Bertrand, Sofiane Ramdani,et al. Sub-pixel Edge Fitting Using B-Spline[J].MIRAGE 2007,4418:353-364.
[24]贺忠海,王宝光,廖怡白,等.利用曲线拟合方法的亚像素提取算法[J].仪器仪表学报,2003,24(2)：195-197.
[25]A.J.Tabatabai,O.Robert Mitchell. Edge Location to Sub-pixel values in Digital Imagery[J]. IEEE Transactions on Pattern Analysis Machine Intelligence,1989,11(12):1293-1307.
[26]刘丹平,胡渝.提高光斑图像质心精度的去噪方法[J].光电工程,2005,32(8)：56-58.
[27]Gang Wang, Liang Xiao, Anzhi He. A Sub-pixel Multifractal Method for Image Se-gmentation[C].IEEE Fourth International Conference on FSKD.2007.
[28]王会峰,汪大宝,刘上乾.激光靶标图像识别和测量方法研究[J].激光与红外,2007,37(6)：564-574.
[29]黄桂平.圆形标志中心子像素定位方法的研究与实现[J].武汉大学学报报(信息科学版)[J],2005,30(5)：388-391.
[30]Karrakchou M,Wei L. Optimal ramp edge detection by orthogonal wavelet tra-nsform (A).IEEE International Symposium on Circuits and Systems[C].San Diego:1992,2.967-970.
[31]孙延奎.小波分析及其应用[M].北京：机械工业出版社.2005：199-243.
[32]曲迎东,崔成松,陈善本,等.利用Sobel-Zernike矩算子的快速亚像素边缘检测方法[J_].光电工程,2003,30(5)：59-61.
[33]Mallat S,Zhong S.Characterization of signals from mutiscale edge[J].IEEE Trans. On PAMI,1992,14(7):710-732.
[34]李金泉,王建伟,陈善本,等.一利,改进的Zernike正交矩亚像素边缘检测算法[J].光学技术,2003,29(4)：500-503.
[35]张贤达.现代信号处理(第二版)[M].北京：清华大学出版社,2002.
[36]张广军.机器视觉[M].北京：科学出版社,2005.
[37]DEIHL N.Object-oriented motion estimation and segmentation in image seque-nces[J].Signal Processing:Image Communication,1991,3(1):23-56.
[38]贾志科,崔慧娟,唐昆,等.用于运动位移估值的新匹配准则[J].清华大学学报(自然科学版),1999,39(1)：43-46.
[39]Hangu Yeo,Yu Hen Hu. A motion estimation and image segmentation technique based on the variabole block size.Proc.ICASSP'97,Munich,Germany:1997,3137-3140.
[1]施法中.计算机辅助几何设计与非均匀有理B样条[M].北京：高等教育出版社,2001.
[2]Farin G. Curves and Surfaces for Computer Aided Geometic Design:A Practical Gu-ide[M].Academic Press,1998.
[3]B K Choi, et al.Matrix Representation for NURBS Curves and Surfaces[J].CAD,19-90,22(4):235-240.
[4]Farin G. Curvature continuity and offsets for piecewise concs[J].ACM Trans.on graphics,8,2,1989.
[5]欧阳联渊.计算机数值计算方法[M].北京：国防工业出版社,1997.
[6]易大义,陈道琦.数值分析引论[M].杭州：浙江大学出版社,2000.
[7]朱心雄.自由曲线曲面造型技术[M].北京：科学出版社,2000.
[8]李庆扬,王能超,易大义.数值分析[M].武汉：华中理工大学出版社,1986：16-26.
[9]H.de las Heras etal. Comparison of interpolation functions to improve a rebinning-free CT-reconstruction algorithm[J] Z. Med. Phys.2008,18:7-16.
[10]V.P.K,B.H.O.Constrained space curve interpolation with constraint planes[J] Computer Aided Geometric Design.2007,22(6):531-550.
[11]TMS320C6000 Technical Brief[EB/OL].Recycled Paper.(1999-02). http://focus. T-i.com/lit/ug/spr- u197d/spru 197d.pdf
[12]TMS320C6000 Optimizing Complier User's Guide DSP.ti.com.http://focus.ti.com /lit/ug/spru187n/spru187n.pdf.
[2]浦发.炮口角的形成、估算及其对跳角和射击的影响[J].兵工学报,1983,4(1)：1-8.
[3]谷向阳.PHL96-300型武器系统定向管参数测量原理与方法研究[D].南京：南京理工大学,2001,3.
[4]赵敏,袁志鹏.液压缸缸筒直线度测量仪的设计[J].液压气动与密封,2001,21(6)：24-27.
[5]季立江,廖俊必,史伟.基于图像处理的炮管轴线直线度检测方法[J].兵工自动化,2002,21(6)：29-32.
[6]刘战锋,宁延平,刘雁蜀.精密深孔直线度测量技术的现状与发展[J].新技术新工艺,2004,31(11)：36-39.
[7]沙吉乐,邾继贵,杨学友,等.大直径钢管直线度在线测量研究[J].仪器仪表学报,2001,22(S3)：55-56,77.
[8]R S Lu, Y F Li, Q Yu. On-line measurement of the straightness of seamless steel pipes using machine vision technique[J]. Sensors and Actuators, A:Physical,2001, 94(1-2):95-101.
[9]庞厚金.大缸体内圆直线度的测量与分析[J].工业计量,2001,11(6)：41-42.
[10]贺安之,吕爱民,闫大鹏等.火炮身管弯曲度、炮口角检测系统的研究[J].弹道学报,1997,9(3)：59-62.
[11]袁红星,吕爱民,王志兴等.PSD法测量直管弯曲度的步长优化研究[J].弹道学报,1998,10(2)：54-57,63.
[12]马宏,潘毓学,车英,等.炮管直线度激光检测设备研究[J].兵工学报,2003,24(2)：261-263.
[13]周韦琴,马宏.炮管直线度测量仪的研制[J].计算机测量与控制,2006,14(7)：929-930,958.
[14]郑浩,刘思海.一种改进的弹体药室容积音频检测仪[J].仪器仪表学报,2006,27(6)：238-240.
[15]柏逢明.音频检测技术[M].北京：中国科学技术出版社,2005.
[16]柏逢明.音频检测弹体药室容积的研究[J].兵工学报.1998,19(2)：167-170.
[17]张文伟,张延忻.基于环形光切图像法的管内壁激光测量系统[J].计量学 报,2001,22(4)：284-287.
[18]Zhang W W, Zhuang B H. Non-contact laser inspection for the inner wall surface of a pipe Meas.Sci.Technol.1998,9:1380-1387.
[19]王坚,单长胜.一种药室参数测量新方法[J].测试技术学报.2006，20(1)：27-40.
[20]Mandayam S, Upda L, Upda S. S et al. Invariance transformations for magnetic flax leakage signals. IEEE Transactions on Magnetics,1996,32(3):1588-1580.
[1]北京嘉恒中自图像技术有限公司.机器视觉系统(第二版)[Z].北京：北京嘉恒中自图像技术有限公司,2006.3-10.
[2]王庆有.CCD应用技术[M].天津：天津大学出版社,2000.27-29,164-166.
[3]浦昭邦,屈玉福,王亚爱.视觉检测系统中照明光源的研究[J].仪器仪表学报,2003,24(S4)：438-439.
[4]李俊.机器视觉照明光源关键技术研究[D].天津：天津理工大学,2006.23-25.
[5]范伟,王毅,扰瑞中.根据表面反射特性进行目标探测波段选择[J].光子学报,2006,35(5)：755-759.
[6]周太明.光源原理与设计[M].上海：复旦大学出版社,1993.15-325.
[7]Kuhn.Gerhard. A new LED light source for projection applications[J], Digest of Technical Papers,2005,1702-1705.
[8]Tsai,Shau-Yu. LED Illumination light source for projection display, Proceedings of the International Display manufacturing Conference and Exhibition, IDMC'05,2005,P240-242.
[9]Moe, Andrew E. Enhanced fluorescence emission using a programmable, reconfigurable LED-array based light source,26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society,EMBC 2004,P2920-2093.
[10]李炳乾.LED环形光源的研制[J].上海：照明工程学报,2007.18(1)64-66.
[11]Eberhardt E H. Source detector spectral matching factors[J].Applied optics,196-8,7 (10):2037-3047.
[12]G.e.Giakoumkis. Matching factors for Various Light-Source Photodetector Combinations[J]. Applied Physics A,1991,52:7-9.
[13]张以谟,应用光学[M].北京：机械工业出版社,1982.
[14]Y.Susu,L.Weisi,O.Eeping,et al.Contrast signal-to-noise ratio for image quality assessment. IEEE International Conference on Image Processing,2005,1:397-400.
[15]Zhou Wang, Hamid R.Sheikh,and Alan C.Bovik. No-Reference perceptual quality Assessment of JPEG compressed images[C].Proc.IEEE Int. Conf. Image Proc.Step.2002.
[16]宋璧.无参考数字图像质量评价研究[D]中国科学技术大学硕士论文.2004,63-70.
[17]S. Winkler, P. Vandergheynst, Computing isotropic local contrast from oriented pyramid decompositions[C]. Proceedings of ICIP,1999:420-424.
[18]杨志文.光学测量[M].北京：北京理工大学出版社,1995年8月.
[19]王之江.成像光学[M].北京：科学出版社,1991.
[20]E.Peli. Contrast in complex images[J]. Journal of Optical Society of America. 1990,7(10):2032-2040.
[21]S.winker. Issues in Vision Model for Perceptual Video Quality Assessment[J]. Signal Processing.1999,78(2).
[22]K.Hosaka. A New Picture Quality Evaluation Method. Proceedings of the International Picture Coding Symposium[C],1986.
[23]肖文.空域无参考的图像质量评价[M].江西财经大学硕士学位论文,2006：35-45.
[24]牛建军.特殊环境下光电图像获取与处理技术[D].西安：西安电子科技大学,2007.26-39.
[25]M. A. Cayless, A. M. Marsden.Lamps and Lighting(Thrid Edition)[M].Edward Arnold Ltd,1983.
[26]M Oren,.S.K. Nayar. Generalization of the Lambertian Model and Implication for Machine Vision[J]. Journal of Computer Vision,1995,14(3):277-251.
[27]LB Wolff, SK Nayar, M Oren.Improved Diffuse Reflection Models for Computer Vision[J]. International Journal of Computer Vision,1998,30(1):55-71.
[28]Ning.L, Zongqian. L.Bi-Spectrum Scattering Model for Conducting Randomly Rough Surface[J].Tsinghua Science and Technology,2002,7(3):309-316.
[29]Vesselin Iossifov Shaoulov. Design And Assessment Of Compact Optical Systems Towards Special Effects Imaging [D].Florida:University of Central Orlando Florida,2005.
[1][美]Rafael C Gonzalez, Richard E Woods数字图像处理(第二版)[M].北京：电子工业出版社,2003.176-181.
[2]XU Xiu-zhen, LI Zi-tian, XUE Li-jun. Analysis and processing of CCD noise[J]. Infrared and laser Engineering,2004,33(8),343-347.
[3]ZHANG Li-na, WU Xiao-qin, HU Xue-you. Reach on CCD Noise Signal Proce-ssing [J]. Third International Conference on Natural Computation,2007,1-4.
[4]王庆有.CCD应用技术[M].天津：天津大学出版社,2000,164-166.
[5]佟首峰,阮锦,郝志航.CCD图像传感器降噪技术的研究[J].光学精密工程,2000,8(2)：140-145.
[6]安连生,李林,李全臣.应用光学(第2版)[M].北京：北京理工大学出版社,2000.90-91.
[7]范志刚,左保军,张爱红.光电测试技术(第二版)[M].北京：电子工业出版社,2008.
[8]P.Mackey, F.R.Barone, N.A.Chu.Real-time non-uniformity correction for focal plane arrays using 12-bit digital electronics[J]. SPIE,1981, (292):210.
[9]Stephen D. Ford, B M. Welsh. M C. et al. Reconstruction of low-light images by use the vector filter[J].J Opt Soc Am,1997,14(10):2678-2691.
[10]A.Murli,L. D'Amore,V.De Simone. The Wiener Filter and Regularization Methods for Image Restoration Problems[C].ICASSP-93.Minneapolis USA:[sn],1993:285-288.
[11]谢杰成,张大力,徐文立.小波图象去噪综述[J].中国图象图形学报,2002,7A(3)：209-217.
[12]Raghuram Rangarajan, Ramji Venkataramanan, Siddharth Shah. Image Denoising Using wavelets[EB/OL].http://www-personal.engin.umich.edu/-rangaraj/551/ Report,Pdf,2004-02.
[13]张旗,梁德群,樊鑫,等.基于小波域的图像噪声类型识别与估计[J].红外与毫米波学报,2004,23(4)：281-285.
[14]关新平,赵立兴,唐英干.图像去噪混合滤波方法[J].中国图象图形学报,2005,10(3)：332-337.
[15]KWON H,DER S Z,NASRABADI N M. Multisensor target detection using adaptive feature based fusion[C]. SPIE,2001,4379:112-123.
[16]Farook Sattar, Lars Floreby, Goran Salomonsson, et al. Image enhancement based on a nonlinear multiscale method[J]. IEEE Transactions on Image Processing,1997, 6(6):888-895.
[17]杜惊雷,黄奇忠,姚军等.灰阶掩模实现光学邻近校正及计算模拟研究[J].光学学报,1999,(19)5：698-673.
[18][法] Henri Maitre等著,孙洪译.现代数字图像处理[M].北京：电子工业出版社,2006：174-176.
[19]陈冬青.基于照明反射模型的内窥镜图像增强方法研究[J).中国生物医学工程学报,2000,19(4)：20-22.
[20]Sang J. K, Nam Y. K, Byung G. L,etal. Wavelet based uneven illumination compensation for defect detection of flat panel display[C]. Proceedings of the Fourth conference on IASTED International Conference:Signal Processing, Pattern Recognition, and Applications, 2007,104-108.
[21]邹谋炎.反卷积和信号恢复[M].北京：国防工业出版社,2001.
[22]Pan R, Reeves S J. Efficient huber-Markov edge-preserving image restoration[J]. IEEE Transactions on Image Processing,2006,15(12):3728-3735.
[23]Tartakovsky A, Blazek R. Effective adaptive spatial-temporal technique for clutter rejection in IRST[C]. SPIE,2000,4048:85-95.
[24]J. G. M. Schavemaker. Image Sharpening by Morphological Filtering[J]. Pattern Recognition,2000,33:997-1012.
[25]Van den Boomgaard R,Smeulders A. The morphological structure of images,the differential equations of morphological scale-space[J]. IEEE Transactions PAMI,1994,16(11):1101-1113.
[26]S.K.Pal and R.A.King. On edge detection of X-ray images using fuzzy sets[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1983,5(1):69-77.
[27]Pal S K, King R A, Image Enhancement using smoothing with Fuzzy Sets[J]. IEEE Trans syst. Man and cybern,1981,11(7):494-501.
[28]H. R.Tizhoosh,G.Krell,B.Michaelis, Locally Adaptive Fuzzy Image Enhancement [C]. Lecture Notes In Computer Science, Proceeding of International Conference on Computational Intelligence,Theory and Applications.1997,1226:272-276.
[29]Zadeh, L. A., Fuzzy Sets. Information and Control 8[M].1965:338-353.
[30]郭桂容,庄钊文.信息处理中的模糊技术[M].长沙：国防科技大学出版社,1993.
：31]章毓晋.图象分割[M].北京：科学出版社,2001.2-3.
'32] K S Fu, J K Mui. A survey of image segmentation[J]. Pattern Recognition,1981, 13(1):3-16.
33] NR Pal, SK Pal. A review on image segmentation techniques[J]. Pattern Recognition,1993,26(9):1277-1294.
34] Sahoo P K etal. A survey of thresholding techniques[J]. Computer Vision, Graphics and Image Processing,1988,41:233-260.
35] Nikhil R. Pal. Sankar K. Pal, A review on image segmentation techniques [J]. Pattern Rcognition 1993,26(9):1277-1294.
36]王新成.高级图象处理技术[M].北京：中国科学技术出版社.2000.
37] Nobuyuki Otsu. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics,1979, SMC-9(1):62-66.
[38]A Magid, S R Rotman, A M Weiss. Comment on "Picture thresholding using an iterative selection method"[J]. IEEE Transactions on Systems, Man, and Cybernetics,1990,20(5):1238-1239.
[39]D. Liu, et al. A fuzzy classification entropy approach to image thresholding [J].Pattern Recognition Letters,2006,27:1968-1975.
[40]E.David Jansing, Thomas A.Albert, et al. Two-dimensional entropic segmentation [J].Pattern Recognition Letters,1999,20(3):329-336.
[41]Kittler J, Illingworth J. On threshold selection using clustering criteria[J]. IEEE Trans,1985, SMC-15:652-655.
[42]Pun T. A new method for gray-level picture thresholding using the entropy of histogram[J]. Signal Processing,1980,2:223-237.
[43]Kapur J N, Sahoo P K, Wong A K C. A new method for gray-level picture thresholding using the entropy of the histogram[J]. Computer Vision, Graphics and Image Processing,1985,29:273-285.
[44]高新波.模糊聚类分析及其应用[M].西安：西安电子科技大学出版社2004.37.
[45]Jagath C Rajapakse, Jay N Giedd, Judith L Rapoport. Statistical approach to segmentation of single-channel cerebral MR images[J]. IEEE Transactions on Medical Imaging,1997,16(2):176-186.
[46]W M Wells Ⅲ, W E L Grimson, R Kikinis, et al. Adaptive segmentation of MRI data[J]. IEEE Transactions on Medical Imaging,1996,15(4):429-442.
[47]W E Blanz, Sheri L Gish. A connectionist classifier architecture applied to image segmentation A]. Proceedings of the 10th International Conference on Pattern Recognition[C]. New Jersey, USA,1990,2:272-277.
[48]Noboru Babaguchi, Koji Yamada, Koichi Kise, et al. Connectionist model binarization[A]. Proceedings of the 10th International Conference on Pattern Recognition[C]. New Jersey, USA,1990,2:51-56.
[49]A Ghosh, N R Pal, S K Pal. Image segmentation using a neural network[J]. Biological Cybernetics,1991,66(2):151-158.
[50]Gilles Aubert, Pierre Kornprobst. Mathematical problems in image processing[M].北京：世界图书出版公司北京公司,2005.129-179.
[51]Stanley Osher, Leonid I Rudin. Feature-oriented image enhancement using shock filters[J]. SIAM Journal on Numerical Analysis,1990,27(4):919-940.
[53]Pietro Perona, Jitendra Malik. Scale-space and edge detection using anisotropic diffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(7):629-639.
[54]杨纶标.模糊数学原理及应用[M].广州：华南理工大学出版社,2005.
[55]N R Pal, S K Pal. Object-background segmentation using new definitions of entr-opy[J]. IEE Proceedings on Computers and Digital Techniques, Part E,1989,136 (4):284-295.
[56]Dorigo M.Ant System:Optimization by a Colony of Cooperating Agents[J]. IEEE Transactions on Systems,Man and Cybernetics,PartB,1996,26 (1):29-41.
[57]M.Dorigo,C.Blum.Ant colony optimization theory:A survey[J]. Theor -etical Computer Science,2005,344:243-278.
[58]T.Wengbing, Jin.H.Object segmentation using ant colony optimization algorithm and fuzzy entropy[J].Pattern Recognition Letters,2007,28(7):788-796.
[59]Han.N, Shi.P. An improved ant colony algorithm for fuzzy clustering in image segmentation[J].Neurocomputer,2007,70:665-671.
[60]Blum C.Ant Colony Optimization:Introduction and Hybridizations[C]. Seven International Conference on Hybrid Intelligent System,2007.1-6.
[61]段海滨.蚁群算法—原理及应用[J]北京：科学出版社,2005.
[62]M.Dorigo,G. DiCaro, L.M.Gambardella. Ant algorithms for discrete optimization[J]. Artificial Life,1999,(5)2:137-44.
[63]牛建军,刘上乾等.同心圆检测中的区域划分算法[J].光子学报,2006,35(12)：1945-1949.
[64]Faugeras.Three-Dimensional Computer Vision[M].MIT Press,1993.
[65]刘波.用Hough变换识别二维图像[J].光子学报,1992,22(2)：32-38.
[66]王建勇,周晓光等.一种改进的直线检测算法[J].计算机工程,2006,32(16)：172-174.
[67]Xing Cao, Farzin Deravi. An efficient method for the detection of multiple concentric circles [J].IEEE 3rd.International Conference on Computer Vision,Osaka,Japan,4-7,Dec,1990,744-747.
[68]黄桂平.圆形标志中心子像素定位方法的研究与实现[J].武汉大学学报报(信息科学版),2005,30(5)：388-391.
[1]Z. Zhang. Flexible camera calibration by view a plane from unknown orientation [C].IEEE Intern-ational Conference on Computer Vision,1999,666-673.
[2]E. Trucco, A. Verri. Introductory Techniques for 3-D Computer Vision[M]. New Jersey:Prentice Hall,1998.
[3]S. Hutchinson, G. D. Hanger, and P. I. Corke. A tutorial on visual servo control[C]. IEEE T-ransactions on Robotics and Automation, vol.12, no.5, pp.651-670, Sept. 1996.
[4]马颂德,张正友.计算机视觉[M].北京：科学出版社.
[5]Slama CC. Manual of Photogrammetry.4th ed,American Society of Photogram-metry.1980.
[6]Tsai R Y. A Versatile Camera Calibration Technique for High-accuracy 3D Machine Vision Metrology Using Off the Shelf TV Cameras and Lenses[J]. IEEE Journal of Robotics and Automation,1987,RA-3(4):323-344.
[7]Roger Y Tsai. A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses[J]. IEEE Journal of Robotics and Automation,1987, RA-3(4):323-344.
[8]Frederic Devernay, Olivier Faugeras. Automatic calibration and removal of distortion from scenes of structured environments[A], Investigative and Trial Image Processing, SPIE[C]. San Diego, USA,1995,2567:62-72.
[9]张以莫.应用光学[M].北京：机械工业出版社,1995.244-245.
[10]H. Hideak i, N.Y Yagihash i, Y. Miyake. A new mothed for distortion correction of electronic endoscope image[J]. IEEE Trans. Med. Imag,1995,14:548-555.
[11]K.Vijayan Asari,Sanjiv Kumar, D.Radhakrishnan. A New Approach for Nonlinear Distortion Correction in Endoscopic Images Based on Least Squares Estimation[J]. IEEE Transactions on Medical Imaging,VOL.18.NO.4,APRIL 1999.345-354.
[12]Lee C B. Radial un-distortion and calibration on an image array[D].Electrical and Computer Science Dept, Massachusetts Institute of Technology,2000.
[13]Brauer-Burchardt C,Voss K. Automatic lens distortion calibration using single views[A].In:G. Sommer,N. Krugre, C.Perwass,eds Musterkennung 2000. Procee-dings of DAGM-Symposium Kiel,Germany,Informatik Aktuell Springer, 2000.187-194.
[14]Devernay F, Faugeras O. Straight lines have to be straight automatic calibration and removal of distortion from scenes of structured environments[J].Machine Vision and Applications,2001,13(1):14-24.
[15]Swaminathan R,Nayar S K. Non-metric calibration of wide-angle lenses and plo-ycameras[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(10):1172-1178.
[16]Farid H, Popescu A C. Blind removal of lens distortion[J].Journal of the Optical Society of America [A],2001,18(9):2072-2078.
[17]席少霖,赵凤治编著.最优化计算方法[M].上海：上海科学技术出版社,1983.
[18]于起峰,陆宏伟等.基于图像的紧密测量和运动测量[M].北京：科学出版社2002,118-166.
[19]张金利,王元庆等.大视角成像系统的快速精确校正[J].光电子·激光,2006,17(2)：158-161.
[20]邓志鹏,杨杰,全勇.景象匹配中的快速畸变校正算法[J].上海交通大学学报,2003,37(S1)：75-78.
[21]陈华旺.图像匹配技术在定位校射中的应用研究[D].西安：西北工业大学,2004.10-11.
[22]Y.Hatada,S.Iwane,H.Tohno,T.Baba,et al. A new method for the measurement of the gastric and colonic lesions with an electronic endoscope and image processor[J],Gastrointestinal Endoscopy,1991,37:275.
[23]C.Margulies,B.Krevsky,et al. How accurate are endoscopic estimates of size[J].Gastrointestinal Endoscopy,1994,40:174-177.
[24]Warren E Smith, Nimish Vakil, Seth A Maislin. Correction of distortion in endoscope images[J]. IEEE Transactions on Medical Imaging,1992,11(1): 117-122.
[25]K Vijayan Asari, Sanjiv Kumar, D Radhakrishnan. A new approach for nonlinear distortion correction in endoscopic images based on least squares estimation[J]. IEEE Transactions on Medical Imaging,1999,18(4):345-354.
[26]牛建军,刘上乾,姚荣辉,等.高准确度光电成像测量系统图像畸变校正算法[J].光子学报,2006,35(9)：1317-1320.
[27]陈鼎才,王定成,查金水.基于机器视觉的现实叶片面积测量方法的研究[J].计算机应用,2006,26(5)：1226-1228.
[28]郑毅,刘上乾.利用直线特征的定标图像非线性畸变校正[J].仪器仪表学报.2007,28(6)：1 1 29-1 1 32.
[29]Frederic Devernay, Olivier Faugeras. Straight lines have to be straight[J]. Machine Vision and Applications,2001,13(1):14-24.
[30]Wolfram Mathworld the web's most extensive mathematics resource. [EB/OL]. (2008-5-07). http://mathworld.wolfram.com/LeastSquaresFitting.html.
[1]段瑞玲,李庆祥,李玉和.图像边缘检测方法研究综述[J].光学技术,2005,31(3)：415-419.
[2]冈萨雷斯著.阮秋琦,阮宇智译.数字图像处理(第二版)[M]北京：电子工业 出版社,2005：460-470.
[3]Canny, J.F.A computational approach to edge detection[J]. IEEE Trans Pattern Ana-lysis and Machine Intelligence,8(6):679-698, Nov 1986.
[5]Stephane Mallat, Wen Liang Hwang. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory,1992,38(2):617-643.
[6]B S Penn, A J Gordon, R F Wendlandt. Using neural networks to locate edges and linear features in satellite images[J]. Computers and Geosciences,1993,19(10): 1545-1565.
[7]Zhengquan He, M Y Siyal. Edge detection with BP neural networks[A].1998 Fourth International Conference on Signal Processing Proceedings[C]. Beijing, China,1998, 2:1382-1384.
[8]Leon O Chua, Lin Yang. Cellular neural networks:theory[J]. IEEE Transactions on Circuits and Systems,1988,35(10):1257-1272.
[9]Yoshifumi Uchiyama, Miki Haseyama, Hideo Kitajima. Hopfield neural networks for edge detection[A]. The 2001 IEEE International Symposium on Circuits and Systems[C]. Sydney, Australia,2001,3:608-611.
[10]Md Shoaib Bhuiyan, Yuji Iwahori, Akira Iwata. Stepped-down coefficient values associated with Hopfield nets improve optimal edge detection[A].1999 IEEE International Conference on Systems, Man, and Cybernetics[C]. Tokyo, Japan, 1999,5:377-382.
[11]Siwei Lu, Ziqing Wang. Fuzzy neural networks for edge detection[A]. IEEE 1997 Canadian Conference on Electrical and Computer Engineering[C]. St. John's, Canada,1997,2:446-449.
[12]朱铮涛.基于计算机视觉图像精密测量的关键技术研究[D].广州：华南理工大学,2004.54.
[13]P.J.Macvicar-Whelan,T.O.Binford. Line Finding with Sub-pixel Precision[M].Proc. 1981,281:201-206.
[14]V.S.Nalwa,T.O.Binford. On detecting edges[J]. IEEE Trans. Patern Anal. Machine Intell,1986,8:699-714.
[15]S.Suthaharan. Image an edge detail detection algorithm for object-based coding[J]. Pattern Recognition,2000,21:549-557.
[16]R. Machuca,A.L. Gilbert. Finding Edges in Noisy Scenes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1981,3(1):103-111.
[17]Edward P. Lyvers,Owen Robert Mitchell. Sub-pixel Measurements Using a Moment-based Edge Operator[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence,1989,11 (12):1293-1307.
[18]张广军.衍射条纹CCD拟合定位方法及精度分析[J].仪器仪表学报,1995,16(1)：107-110.
[19]曹国容,景芳盛.CCD用于莫尔条纹细分的技术[J].计量学报1995,16(2)：116-119.
[20]Yu. Shan Li, Tzay Y. Yong. Sub-pixel Edge Detection and Estimation with a microprocessor Controlled Line Scan Camera[J]. IEEE Transactions on Industrial Electronics,1989,35(1):105-107.
[21]Cuilian Zhao, Yu Chen. A New Liquid Level Measuring System of Standard Metal Tank Based Sub-pixel Edge Detection[C].IEEE International Conference on Control and Automation, Guangzhou China:2007.3206-3212.
[22]Toshiaki Watanabe. Image coding making use of B-spline surfaces[J]. IEEE Transactions on Circuits and Systems for Video Technology,1997,7(2):409-413.
[23]Frederic Bouchara, Marc Bertrand, Sofiane Ramdani,et al. Sub-pixel Edge Fitting Using B-Spline[J].MIRAGE 2007,4418:353-364.
[24]贺忠海,王宝光,廖怡白,等.利用曲线拟合方法的亚像素提取算法[J].仪器仪表学报,2003,24(2)：195-197.
[25]A.J.Tabatabai,O.Robert Mitchell. Edge Location to Sub-pixel values in Digital Imagery[J]. IEEE Transactions on Pattern Analysis Machine Intelligence,1989,11(12):1293-1307.
[26]刘丹平,胡渝.提高光斑图像质心精度的去噪方法[J].光电工程,2005,32(8)：56-58.
[27]Gang Wang, Liang Xiao, Anzhi He. A Sub-pixel Multifractal Method for Image Se-gmentation[C].IEEE Fourth International Conference on FSKD.2007.
[28]王会峰,汪大宝,刘上乾.激光靶标图像识别和测量方法研究[J].激光与红外,2007,37(6)：564-574.
[29]黄桂平.圆形标志中心子像素定位方法的研究与实现[J].武汉大学学报报(信息科学版)[J],2005,30(5)：388-391.
[30]Karrakchou M,Wei L. Optimal ramp edge detection by orthogonal wavelet tra-nsform (A).IEEE International Symposium on Circuits and Systems[C].San Diego:1992,2.967-970.
[31]孙延奎.小波分析及其应用[M].北京：机械工业出版社.2005：199-243.
[32]曲迎东,崔成松,陈善本,等.利用Sobel-Zernike矩算子的快速亚像素边缘检测方法[J_].光电工程,2003,30(5)：59-61.
[33]Mallat S,Zhong S.Characterization of signals from mutiscale edge[J].IEEE Trans. On PAMI,1992,14(7):710-732.
[34]李金泉,王建伟,陈善本,等.一利,改进的Zernike正交矩亚像素边缘检测算法[J].光学技术,2003,29(4)：500-503.
[35]张贤达.现代信号处理(第二版)[M].北京：清华大学出版社,2002.
[36]张广军.机器视觉[M].北京：科学出版社,2005.
[37]DEIHL N.Object-oriented motion estimation and segmentation in image seque-nces[J].Signal Processing:Image Communication,1991,3(1):23-56.
[38]贾志科,崔慧娟,唐昆,等.用于运动位移估值的新匹配准则[J].清华大学学报(自然科学版),1999,39(1)：43-46.
[39]Hangu Yeo,Yu Hen Hu. A motion estimation and image segmentation technique based on the variabole block size.Proc.ICASSP'97,Munich,Germany:1997,3137-3140.
[1]施法中.计算机辅助几何设计与非均匀有理B样条[M].北京：高等教育出版社,2001.
[2]Farin G. Curves and Surfaces for Computer Aided Geometic Design:A Practical Gu-ide[M].Academic Press,1998.
[3]B K Choi, et al.Matrix Representation for NURBS Curves and Surfaces[J].CAD,19-90,22(4):235-240.
[4]Farin G. Curvature continuity and offsets for piecewise concs[J].ACM Trans.on graphics,8,2,1989.
[5]欧阳联渊.计算机数值计算方法[M].北京：国防工业出版社,1997.
[6]易大义,陈道琦.数值分析引论[M].杭州：浙江大学出版社,2000.
[7]朱心雄.自由曲线曲面造型技术[M].北京：科学出版社,2000.
[8]李庆扬,王能超,易大义.数值分析[M].武汉：华中理工大学出版社,1986：16-26.
[9]H.de las Heras etal. Comparison of interpolation functions to improve a rebinning-free CT-reconstruction algorithm[J] Z. Med. Phys.2008,18:7-16.
[10]V.P.K,B.H.O.Constrained space curve interpolation with constraint planes[J] Computer Aided Geometric Design.2007,22(6):531-550.
[11]TMS320C6000 Technical Brief[EB/OL].Recycled Paper.(1999-02). http://focus. T-i.com/lit/ug/spr- u197d/spru 197d.pdf
[12]TMS320C6000 Optimizing Complier User's Guide DSP.ti.com.http://focus.ti.com /lit/ug/spru187n/spru187n.pdf.
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