特殊环境下图像测量关键技术研究
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摘要
管道静态参数测量是特种管道设计、研究和使用中必需且重要的技术环节。随着特种管道研制、生产水平和使用要求的不断提高,原来基于光学望远系统的人工测量、人工处理数据的测量方法已经不能满足需要,必须建立高精度、高效率和高度自动化的测量系统。
在对特种管道静态参数的测量原理、测量方法和测量装备的性能及其局限性进行深入研究的基础上,我们设计并实现了一种以爬行器运动控制技术、照明技术、图像预处理方法和高精度图像测量技术为核心的管道静态参数测量系统方案。这些技术和方法是测量特种管道诸多静态参数所需的共性技术,因此论文仅以特种管道静态参数之一的弯曲度参数的测量为例,对系统涉及到的上述关键技术问题进行了深入地研究,主要包括以下五个方面的内容:
1.提出了爬行器运动控制系统进深运动的动力学模型,以及均匀照明模型根据系统实际工作情况,提出了爬行器运动控制系统进深运动的动力学模型,并深入研究了负荷大小、载体转动惯量与电机扭矩的大小及其变化关系。同时还研究了爬行器运动控制系统进深定位精度的影响因素。
根据照度均匀设计原则,提出了均匀照明模型,并针对管道内部空间狭小、测量系统结构尺寸小、成像光学系统物距短、视场角大等特点,研究了在这种特殊环境下实现清晰成像所必需的照度分布条件及实现技术途径。
2.提出了根据局部梯度模值判别靶标图像中各像素的噪声类型,并使用复对数Gabor小波滤波器去除相应噪声的靶标图像噪声抑制方法
通过在降噪之前引入局部梯度模作为阈值,判断图像中每一个像素所受噪声干扰的类型,根据不同的噪声类型使用不同的降噪方法。对受到椒盐噪声干扰的像素,使用中值滤波降噪。然后,对去除椒盐噪声的图像,利用复对数Gabor小波提取各像素的相位信息和幅度信息,确定最小尺度滤波器对的噪声幅度分布的估计值,从而自动地确定各个尺度上的噪声幅度分布的估计值和噪声萎缩阈值,去除靶标图像中的高斯噪声。
3.针对亮度不均匀的靶标图像,提出了利用变分的自适应阈值曲面图像分割算法和基于最大模糊熵的图像阈值分割快速算法
利用变分的自适应阈值曲面图像分割算法可以从非均匀亮度图像中将靶标图案有效地分割出来。但由于偏微分方程迭代运算耗时太大,使这种方法不能满足测量系统的实时性要求,因此我们又提出了基于最大模糊熵的图像阈值分割快速算法,使用了遗传算法和模拟退火算法来加快算法的收敛速度。
4.提出了RBF网络校正法和基于直线特征的非线性畸变校正法,校正靶标图像的几何畸变
基于RBF网络的靶标图像畸变校正方法能够克服多项式变形方法中的方程组奇异解的问题。但这种方法仍然需要准确获得控制点的畸变位置坐标和无畸变位置坐标,否则畸变校正的效果不理想。因此,我们又提出了一种基于直线特征的网格靶标图案的畸变校正方法。这种方法不再需要获取畸变图像与校正图像的控制点对,只要求成像视场中存在不通过视场中心的且具有一定长度的直线特征的空间对象,就可以求解畸变系数,完成畸变校正。
5.提出了一种将LoG算子和Zernike矩算子相结合的两步高精度边缘检测方法
先使用LoG算子将目标(靶标图案和激光光斑)边缘定位到像素级精度,然后对这些边缘像素的邻域使用Zernike矩算子进行处理,得到目标边缘的亚像素位置。对光斑边缘的亚像素位置坐标使用最小二乘法拟合,可以得到光斑拟合圆,并计算出光斑形心位置。
同时,对影响管道静态参数测量系统测量精度的误差源也进行了深入地分析,并给出了相应的解决方法,而且对进一步的研究工作进行了展望。
Measurement of tube static parameters is a prerequisite and important technique process in designing, studying and use of the special type of tube. The laggard measurement method based on optics glass and data processing artificially cannot satisfy new requirements that following progression of the development, manufacture level and use of the special type of tube. A high efficiency, precision and automatism measurement system need be constructed.
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 the core of crawler control, illumination, image pretreatment and image measurement at high accuracy has been designed and realized. These technologies are absolutely necessary for all the static parameters measurement, so key technologies for camber measurement are only studied thoroughly in this dissertation. The following is the main contents that studied in the dissertation.
1. A kinetic model on the movement of the crawler control system and an even illumination model are proposed
According to the operation demands of the crawler, a kinetic model of the crawler entrance motion is proposed. And the load, the carrier’s moment of inertia and motor torque are studied thoroughly. At the same time, the factors effecting motion precision have also been studied.
According to even illumination method, an even illumination model is proposed. In view of narrowness of interior space, small size of the measurement system, short object distance and large field of view of imaging optical system, the condition illuminating evenly and realization method are studied in the special environment.
2. Many kinds of noise can be distinguished by the local gradient module of pixels, and the corresponding noise can be denoised with complex-valued log Gabor wavelet filter
The local gradient module of pixels is regarded as the threshold value, by which many kinds of noise can be distinguished. Then the corresponding method can be chosen to remove noise. The median filter is worked on pixels polluted by salt and pepper noise. With the use of complex-valued log Gabor wavelet, phase information and amplitude information of a pixel can be obtained in the partially denoised image. And an estimate of noise amplitude distribution for the smallest scale filter pair can be got. From the statistics of the smallest scale filter response, estimates of the noise amplitude distributions at all the other scales can be obtained, and shrinkage thresholds can be set automatically. Thus the noisy image can be denoised completely.
3. In view of target images with nonuniform illumination, an adaptive threshold surface method based on variation and a fast image segmentation method based on maximum fuzzy entropy are proposed
An adaptive threshold surface method based on variation can segment target images accurately. Because of time-consuming iterative operations of partial differential equations, the adaptive threshold surface method based on variation cannot satisfy adequately the requirement of real-time image segmentation. Subsequently, a fast algorithm of image segmentation based on maximum fuzzy entropy is proposed to satisfy the requirement of real-time segmentation. Genetic algorithm and simulated annealing algorithm are adopted to not only generate better solutions but also accelerate the speed of convergence in the proposed fast algorithm.
4. Nonlinear distortion correction methods based on Radial Basis Function neural network and linear characteristic are proposed to correct target images A nonlinear distortion correction method based on Radial Basis Function neural network can keep away from the difficult problem of singular solutions of equations in the polynomial warping method. However, the method requires the accurate determination of point correspondences between a scene and an image of that scene, or else corrected images are unsatisfying. Thus a nonlinear distortion correction method based on linear characteristic in the target pattern is proposed. The method does not require the determination of point correspondence between a scene and an image of that scene. The method simply requires images of known linear segments, which do not pass through the center of field of view and are long enough to be calculated in the distortion correction operation.
5. A two-step edge detection method at high accuracy combined with LoG operator and Zernike moment-based operators is proposed
Edges of the target pattern and the laser spot are reached at pixel accuracy by using LoG operator, then they are reached at sub-pixel accuracy by using Zernike moment-based operators in the extending concourse of edge pixels. The edge pixels coordinates at sub-pixel accuracy are used to fit spot circle and calculate the centroid of spot in least square estimation method.
At the same time, error sources affecting measurement accuracy in the tube static parameters measurement system have been analyzed thoroughly, and the corresponding methods have been proposed to overcome the deficiencies. And we look forward to more researches on the system.
在对特种管道静态参数的测量原理、测量方法和测量装备的性能及其局限性进行深入研究的基础上,我们设计并实现了一种以爬行器运动控制技术、照明技术、图像预处理方法和高精度图像测量技术为核心的管道静态参数测量系统方案。这些技术和方法是测量特种管道诸多静态参数所需的共性技术,因此论文仅以特种管道静态参数之一的弯曲度参数的测量为例,对系统涉及到的上述关键技术问题进行了深入地研究,主要包括以下五个方面的内容:
1.提出了爬行器运动控制系统进深运动的动力学模型,以及均匀照明模型根据系统实际工作情况,提出了爬行器运动控制系统进深运动的动力学模型,并深入研究了负荷大小、载体转动惯量与电机扭矩的大小及其变化关系。同时还研究了爬行器运动控制系统进深定位精度的影响因素。
根据照度均匀设计原则,提出了均匀照明模型,并针对管道内部空间狭小、测量系统结构尺寸小、成像光学系统物距短、视场角大等特点,研究了在这种特殊环境下实现清晰成像所必需的照度分布条件及实现技术途径。
2.提出了根据局部梯度模值判别靶标图像中各像素的噪声类型,并使用复对数Gabor小波滤波器去除相应噪声的靶标图像噪声抑制方法
通过在降噪之前引入局部梯度模作为阈值,判断图像中每一个像素所受噪声干扰的类型,根据不同的噪声类型使用不同的降噪方法。对受到椒盐噪声干扰的像素,使用中值滤波降噪。然后,对去除椒盐噪声的图像,利用复对数Gabor小波提取各像素的相位信息和幅度信息,确定最小尺度滤波器对的噪声幅度分布的估计值,从而自动地确定各个尺度上的噪声幅度分布的估计值和噪声萎缩阈值,去除靶标图像中的高斯噪声。
3.针对亮度不均匀的靶标图像,提出了利用变分的自适应阈值曲面图像分割算法和基于最大模糊熵的图像阈值分割快速算法
利用变分的自适应阈值曲面图像分割算法可以从非均匀亮度图像中将靶标图案有效地分割出来。但由于偏微分方程迭代运算耗时太大,使这种方法不能满足测量系统的实时性要求,因此我们又提出了基于最大模糊熵的图像阈值分割快速算法,使用了遗传算法和模拟退火算法来加快算法的收敛速度。
4.提出了RBF网络校正法和基于直线特征的非线性畸变校正法,校正靶标图像的几何畸变
基于RBF网络的靶标图像畸变校正方法能够克服多项式变形方法中的方程组奇异解的问题。但这种方法仍然需要准确获得控制点的畸变位置坐标和无畸变位置坐标,否则畸变校正的效果不理想。因此,我们又提出了一种基于直线特征的网格靶标图案的畸变校正方法。这种方法不再需要获取畸变图像与校正图像的控制点对,只要求成像视场中存在不通过视场中心的且具有一定长度的直线特征的空间对象,就可以求解畸变系数,完成畸变校正。
5.提出了一种将LoG算子和Zernike矩算子相结合的两步高精度边缘检测方法
先使用LoG算子将目标(靶标图案和激光光斑)边缘定位到像素级精度,然后对这些边缘像素的邻域使用Zernike矩算子进行处理,得到目标边缘的亚像素位置。对光斑边缘的亚像素位置坐标使用最小二乘法拟合,可以得到光斑拟合圆,并计算出光斑形心位置。
同时,对影响管道静态参数测量系统测量精度的误差源也进行了深入地分析,并给出了相应的解决方法,而且对进一步的研究工作进行了展望。
Measurement of tube static parameters is a prerequisite and important technique process in designing, studying and use of the special type of tube. The laggard measurement method based on optics glass and data processing artificially cannot satisfy new requirements that following progression of the development, manufacture level and use of the special type of tube. A high efficiency, precision and automatism measurement system need be constructed.
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 the core of crawler control, illumination, image pretreatment and image measurement at high accuracy has been designed and realized. These technologies are absolutely necessary for all the static parameters measurement, so key technologies for camber measurement are only studied thoroughly in this dissertation. The following is the main contents that studied in the dissertation.
1. A kinetic model on the movement of the crawler control system and an even illumination model are proposed
According to the operation demands of the crawler, a kinetic model of the crawler entrance motion is proposed. And the load, the carrier’s moment of inertia and motor torque are studied thoroughly. At the same time, the factors effecting motion precision have also been studied.
According to even illumination method, an even illumination model is proposed. In view of narrowness of interior space, small size of the measurement system, short object distance and large field of view of imaging optical system, the condition illuminating evenly and realization method are studied in the special environment.
2. Many kinds of noise can be distinguished by the local gradient module of pixels, and the corresponding noise can be denoised with complex-valued log Gabor wavelet filter
The local gradient module of pixels is regarded as the threshold value, by which many kinds of noise can be distinguished. Then the corresponding method can be chosen to remove noise. The median filter is worked on pixels polluted by salt and pepper noise. With the use of complex-valued log Gabor wavelet, phase information and amplitude information of a pixel can be obtained in the partially denoised image. And an estimate of noise amplitude distribution for the smallest scale filter pair can be got. From the statistics of the smallest scale filter response, estimates of the noise amplitude distributions at all the other scales can be obtained, and shrinkage thresholds can be set automatically. Thus the noisy image can be denoised completely.
3. In view of target images with nonuniform illumination, an adaptive threshold surface method based on variation and a fast image segmentation method based on maximum fuzzy entropy are proposed
An adaptive threshold surface method based on variation can segment target images accurately. Because of time-consuming iterative operations of partial differential equations, the adaptive threshold surface method based on variation cannot satisfy adequately the requirement of real-time image segmentation. Subsequently, a fast algorithm of image segmentation based on maximum fuzzy entropy is proposed to satisfy the requirement of real-time segmentation. Genetic algorithm and simulated annealing algorithm are adopted to not only generate better solutions but also accelerate the speed of convergence in the proposed fast algorithm.
4. Nonlinear distortion correction methods based on Radial Basis Function neural network and linear characteristic are proposed to correct target images A nonlinear distortion correction method based on Radial Basis Function neural network can keep away from the difficult problem of singular solutions of equations in the polynomial warping method. However, the method requires the accurate determination of point correspondences between a scene and an image of that scene, or else corrected images are unsatisfying. Thus a nonlinear distortion correction method based on linear characteristic in the target pattern is proposed. The method does not require the determination of point correspondence between a scene and an image of that scene. The method simply requires images of known linear segments, which do not pass through the center of field of view and are long enough to be calculated in the distortion correction operation.
5. A two-step edge detection method at high accuracy combined with LoG operator and Zernike moment-based operators is proposed
Edges of the target pattern and the laser spot are reached at pixel accuracy by using LoG operator, then they are reached at sub-pixel accuracy by using Zernike moment-based operators in the extending concourse of edge pixels. The edge pixels coordinates at sub-pixel accuracy are used to fit spot circle and calculate the centroid of spot in least square estimation method.
At the same time, error sources affecting measurement accuracy in the tube static parameters measurement system have been analyzed thoroughly, and the corresponding methods have been proposed to overcome the deficiencies. And we look forward to more researches on the system.
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]徐永凯,王信义,袁洪芳. EST法测量深孔母线直线度的方案及其误差分析[J].北京理工大学学报,2000,20(4):431-434.
[8]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.
[9] 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.
[10]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.
[11]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.
[12]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.
[13]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.
[14]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.
[15] Hao Qun,Zhao Yang,Li Dacheng,et al. Straightness measurement using laser diode and CCD camera[J]. Chinese Journal of Lasers,1999,B8(3):215-220.
[16]田瑞娟.空间直线度测量方法的研究[D].成都:四川大学,2004. 29-41.
[17]张连存,张国玉,付秀华,等.Φ25mm火炮身管直线度光电测量方法[J].光学精密工程,2004,12(5):485-490.
[18]何鲲.大口径长管内孔的直线度、圆度测量研究[D].合肥:合肥工业大学,2004. 5-6.
[1]胡祐德,马东升,张莉松.伺服系统原理与设计(第2版)[M].北京:北京理工大学出版社,1999. 135-188.
[2]金钰,胡祐德,李向春.伺服系统设计指导[M].北京:北京理工大学出版社,2000. 1-29.
[3]胡建华,廖文和,周儒荣. CNC系统中几种升降速控制曲线的研究与比较[J].南京航空航天大学学报,1999,31(6):706-711.
[4]胡科峰.高速高精度运动控制系统的软件技术研究[D].武汉:华中科技大学,2004. 41-49.
[5]刘胜,彭侠夫,叶瑰昀.现代伺服系统设计[M].哈尔滨:哈尔滨工程大学出版社,2001. 9-29.
[6]董吉洪,徐宏.全自动金丝球焊机X-Y工作台的误差分析[J].光学精密工程,2005,13(S1):148-152.
[7] GB/T 5796.4-2005,梯形螺纹第4部分:公差[S]. 2006.
[8]甘永立.几何量公差与检测(第五版)[M].上海:上海科学技术出版社,2001. 150-153.
[9]陆廷济,费定曜,胡德敬,等.大学物理实验[M].上海:同济大学出版社,1996. 11-13.
[10]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. 3-6.
[11]周太明.光源原理与设计[M].上海:复旦大学出版社,1993. 15-325.
[12]王庆有. CCD应用技术[M].天津:天津大学出版社,2000. 27-29,164-166.
[13]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.
[14]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.
[15]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. 171-173.
[16]李炳乾. LED环形光源研制[J].照明工程学报,2007,18(1):64-66.
[17]王蓉,刘玉玲,余飞鸿. LED光源照明微投影仪系统的设计[J].光学仪器,2006,28(2):22-26.
[18]潘玲玲,叶楠,张晔晖.电源稳定性对LED光源稳定性的影响研究[J].现代显示,2006,13(7):20-22.
[19]周小丽,刘木清.近红外光源的稳定性控制[J].光源与照明,2004,27(4):4-5,8.
[20]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.
[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]吴晓波,安文斗,杨钢.图像测量系统中的误差分析及提高测量精度的途径[J].光学精密工程,1997,5(1):133-141.
[3]王庆有. CCD应用技术[M].天津:天津大学出版社,2000. 164-166.
[4]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. 40-41.
[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]许秀贞,李自田,薛利军. CCD噪声分析及处理技术[J].红外与激光工程,2004,33(4):343-346,357.
[7]丁晓华,李由,于起峰,等. CCD噪声标定及其在边缘定位中的应用[J].光学学报,2008,28(1):99-104.
[8]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.
[9]四维科技,胡小锋,赵辉. Visual C++/MATLAB图像处理与识别实用案例精选[M].北京:人民邮电出版社,2004. 63-87.
[10]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.
[11] Stephane Mallat, Wen Liang Hwang. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory, 1992, 38(2): 617-643.
[12] David L Donoho, Iain M Johnstone. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.
[13] David L Donoho. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
[14] Eero P Simoncelli, Edward H Adelson. Noise removal via Bayesian wavelet coring[A]. IEEE International Conference on Image Processing[C]. Lausanne, Switzerland, 1996, 1: 379-382.
[15] Elwood T Olsen, Biquan Lin. A wavelet phase filter for emission tomography[A]. Proceedings of SPIE[C]. Orlando, USA, 1995, 2491/1: 829-839.
[16] Samuel P Kozaitis, Mark A Getbehead. Optical wavelet feature extraction using a multiple-input phase-only encoded joint-transform correlator[J]. Optics Communications, 1998, 151(1-3): 15-20.
[17] G P Nason. Wavelet shrinkage using cross-validation[J]. Journal of the Royal Statistical Society. Series B (Methodological), 1996, 58(2): 463-479.
[18] X Huang, G A Woolsey. Image denoising using Wiener filtering and wavelet thresholding[A]. 2000 IEEE International Conference on Multimedia and Expo[C]. New York, USA, 2000, 3: 1759-1762.
[19] Vasily Strela, Peter Niels Heller, Gilbert Strang, et al. The application of multiwavelet filterbanks to image processing[J]. IEEE Transactions on Image Processing, 1999, 8(4): 548-563.
[20]刘新文,王惠南,钱志余.小波变换对OCT图像的降噪处理[J].光子学报,2006,35(6):935-939.
[21] Thomas S Huang, James W Burnett, Andrew G Deczky. The importance of phase in image processing filters[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1975, ASSP-23(6): 529-542.
[22] Alan V Oppenheim, Jae S Lim. The importance of phase in signals[J]. Proceedings of the IEEE, 1981, 69(5): 529-541.
[23] Peter Kovesi. Phase preserving denoising of images[A]. The Australian Pattern Recognition Society Conference: DICTA 1999[C]. Perth, Australia, 1999: 212-217.
[24] Stephane Mallat.信号处理的小波导引(原书第2版)[M].北京:机械工业出版社,2002. 2-3.
[25]肖志涛,国澄明,于明,等. log Gabor函数在人类视觉系统特性研究中的应用[J].信号处理,2002,18(5):399-402.
[26]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.
[27]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.
[28] Heesung Kwon, Sandor Z Der, Nasser M Nasrabadi. Multisensor target detection using adaptive feature-based fusion[A]. Proceedings of SPIE[C]. Orlando, USA, 2001, 4379: 112-123.
[29]金炜,魏彪,潘英俊,等.一种新颖的Contourlet域中子辐射图像降噪方法[J].光子学报,2006,35(5):760-765.
[30]蔡敦虎,羿旭明.小波基的选取对图像去噪的影响[J].数学杂志,2005,25(2):185-190.
[31] 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.
[32]侯建华,田金文,柳健.一种改进的图像组合滤波方法[J].光子学报,2005,34(11):1748-1751.
[1]章毓晋图象处理和分析[M]北京：清华大学出版社，1999. 179
[2]章毓晋图象分割[M]北京：科学出版社，2001. 2-3
[3] K S Fu, J K Mui. A survey on image segmentation[J]. Pattern Recognition, 1981,13(1): 3-16.
[4] Theo Pavlidis, Yuh-tay Liow. Integrating region growing and edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(3):225-233.
[5] Ioannis Matalas, Ralph Benjamin, Richard Kitney. An edge detection techniqueusing the facet model and parameterized relaxation labeling[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1997, 19(4): 328-341.
[6] W Doyle. Operation useful for similarity-invariant pattern recognition[J]. Journal ofthe Association for Computing Machinery, 1962, 9(2): 259-267.
[7] Nobuyuki Otsu. A threshold selection method from gray-level histograms [J]. IEEETransactions on Systems, Man, and Cybernetics, 1979, SMC-9(1): 62-66.
[8] T W Ridler, S Calvard. Picture thresholding using an iterative selection method[J].IEEE Transactions on Systems, Man, and Cybernetics, 1978, SMC-8(8): 630-632.
[9] H J Trussell. Comments on "Picture thresholding using an iterative selectionmethod"[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979,SMC-9(5):311.
[10] A Magid, S R Rotman, A M Weiss. Comment on "Picture thresholding using aniterative selection method"[J]. IEEE Transactions on Systems, Man, andCybernetics, 1990,20(5): 1238-1239.
[11] J N Kapur, P K Sahoo, A K C Wong. A new method for gray-level picturethresholding using the entropy of the histogram[J]. Computer Vision, Graphics, andImage Processing, 1985, 29(3): 273-285.
[12]袁占亭.基于一维熵的图像阈值自动选取[J].甘肃工业大学学报，1993，19 (1)：84-88
[13] Luc Vincent, Pierre Soille. Watersheds in digital spaces: An efficient algorithmbased on immersion simulations [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence, 1991, 13(6): 583-598.
[14] Kostas Haris, Serafim N Efstratiadis, Nicos Maglaveras, et al. Hybrid imagesegmentation using watersheds and fast region merging[J]. IEEE Transactions onImage Processing, 1998, 7(12): 1684-1699.
[15] Bhabatosh Chanda, Malay K Kundu, Y Vani Padmaja. A multi-scale morphologicedge detector[J]. Pattern Recognition, 1998, 31(10): 1469-1478.
[16] Nathalie Giordana, Wojciech Pieczynski. Estimation of generalized multisensorhidden Markov chains and unsupervised image segmentation[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1997, 19(5): 465-475.
[17]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
[18] Jagath C Rajapakse, Jay N Giedd, Judith L Rapoport. Statistical approach tosegmentation of single-channel cerebral MR images[J]. IEEE Transactions onMedical Imaging, 1997, 16(2): 176-186.
[19] W M Wells III, W E L Grimson, R Kikinis, et al. Adaptive segmentation of MRIdata[J]. IEEE Transactions on Medical Imaging, 1996, 15(4): 429-442.
[20] W E Blanz, Sheri L Gish. A connectionist classifier architecture applied to imagesegmentation[A]. Proceedings of the 10th International Conference on PatternRecognition[C]. New Jersey, USA, 1990, 2: 272-277.
[21] Noboru Babaguchi, Koji Yamada, Koichi Kise, et al. Connectionist modelbinarization[A]. Proceedings of the 10th International Conference on PatternRecognition[C]. New Jersey, USA, 1990, 2: 51-56.
[22] A Ghosh, N R Pal, S K Pal. Image segmentation using a neural network[J]. Biological Cybernetics, 1991,66(2): 151-158.
[23] Jayant Shah. Parameter estimation, multiscale representation and algorithms for energy-minimizing segmentations [A]. Proceedings of the 10th International Conference on Pattern Recognition[C]. New Jersey, USA, 1990, 1: 815-819.
[24]杨新.图像偏微分方程的原理与应用[M].上海：上海交通大学出版社，2003 80-105
[25] 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.
[26] Stanley Osher, Leonid I Rudin. Feature-oriented image enhancement using shockfilters[J]. SIAM Journal on Numerical Analysis, 1990, 27(4): 919-940.
[27] Pietro Perona, Jitendra Malik. Scale-space and edge detection using anisotropicdiffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(7): 629-639.
[28] S D Yanowitz, A M Bruckstein. A new method for image segmentation[J].Computer Vision, Graphics, and Image Processing, 1989, 46(1): 82-95.
[29]沈定刚，戚飞虎.基于神经网络的自适应图像分割[J].上海交通大学学报，1994，28(S1)：39-45
[30]薛景浩，章毓晋，林行刚.二维遗传算法用于图象动态分割[J].自动化学报，2000，26(5)：685-689
[31]贺霖，李见为.基于零交叉检测器的动态阈值选取[J].光电工程，2003，30 (4)：65-68
[32]景云华，董才林，杨扬，等复杂背景下的阈值插值方法[J].计算机工程，2003，29(17)：160-161
[33] Ilya Blayvas, Alfred Bruckstein, Ron Kimmel. Efficient computation of adaptive threshold surfaces for image binarization[A]. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. Kauai, USA, 2001, 1: 737-742.
[34] J R Parker, C Jennings, A G Salkauskas. Thresholding using an illumination model [A]. Proceedings of the Second International Conference on Document Analysis and Recognition[C]. Tsukuba Science City, Japan, 1993: 270-273.
[35]涂其远，吴建华，万国金动态阈值结合全局阈值对图像进行分割[J].南昌大学学报(工科版)，2002，24(1)：37-40，70
[36]高阳，李言俊，张科.非均匀背景下的红外图像曲面拟合分割[J].光电工程，2006，33(7)：83-87
[37]张永平，郑南宁，赵荣椿基于变分的图像分割算法[J].中国科学(E辑)，2002，32(1)：133-144
[38] Francis H Y Chan, F K Lam, Hui Zhu. Adaptive thresholding by variationalmethod[J]. IEEE Transactions on Image Processing, 1998, 7(3): 468-473.
[39] Fei Liu, Xiaodan Song, Yupin Luo, et al. Adaptive thresholding based onvariational background[J]. Electronics Letters, 2002, 38(18): 1017-1018.
[40] William H Press, Saul A Teukolsky, William T Vetterling, et al. Numerical recipesin C: The art of scientific computing (Second edition)[M]. Cambridge, UK:Cambridge University Press, 1992. 866-869.
[41] S K Pal, R A King, A A Hashim. Automatic grey level thresholding through indexoffuzziness and entropy[J]. Pattern Recognition Letters, 1983, 1(3): 141-146.
[42] C AMurthy, S K Pal. Histogram thresholding by minimizing graylevel fuzziness[J].Information Sciences, 1992,60(1-2): 107-135.
[43] H D Cheng, Jim-rong Chen, Jiguang Li. Threshold selection based on fuzzyc-partition entropy approach[J]. Pattern Recognition, 1998, 31(7): 857-870.
[44] Zhiwei Zhao, Xueqin Li, R W Gunderson. A novel fuzzy entropy clusteringalgorithm[A]. Proceedings of the Third IEEE Conference on Fuzzy Systems [C].Orlando, USA, 1994, 1: 636-641.
[45] H D Cheng, Jim-rong Chen. Automatically determine the membership functionbased on the maximum entropy principle[J]. Information Sciences, 1997, 96(3-4):163-182.
[46] N R Pal, S K Pal. Object-background segmentation using new definitions ofentropy[J]. IEE Proceedings on Computers and Digital Techniques, Part E, 1989,136(4): 284-295.
[47]邢文训，谢金星现代优化计算方法[M]北京：清华大学出版社，1999 90-192
[48]陶文兵，田金文，柳健，等基于遗传算法和模糊熵的前视红外图像分割[J].红外与毫米波学报，2003，22(6)：465-468
[49]周明，孙树栋遗传算法原理及应用[M].北京：国防工业出版社，1999 32-60
[50]康立山，谢云，尤矢勇，等非数值并行算法(第一册)——模拟退火算法[M]北京：科学出版社，1994.22-55
[51]章毓晋图象处理和分析[M]北京：清华大学出版社，1999.141-143
[52]牛建军，刘上乾，韩宝君，等同心圆检测中的区域划分算法[J].光子学报，2006．35(12)1: 1946-1949
[1]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. 154-158.
[2]廖士中,高培焕,苏艺,等.一种光学镜头摄像机图象几何畸变的修正方法[J].中国图象图形学报, 2000, 5A(7):593-596.
[3] Seong-woo Park, Ki-sang Hong. Practical ways to calculate camera lens distortion for real-time camera calibration[J]. Pattern Recognition, 2001, 34(6): 1199-1206.
[4]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.
[5] 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.
[6] 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.
[7]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.
[8]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.
[9]刘金根.一种基于现场定标的光电图像畸变校正算法[J].光子学报,2004,33(6):732-735.
[10] Juyang Weng, Paul Cohen, Marc Herniou. Camera calibration with distortion models and accuracy evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(10): 965-980.
[11] 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.
[12] Hideaki Haneishi, Yutaka Yagihashi, Yoichi Miyake. A new method for distortion correction of electronic endoscope images[J]. IEEE Transactions on Medical Imaging, 1995, 14(3): 548-555.
[13] 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.
[14]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.
[15]王权岱,段玉岗,丁玉成,等.压印对正系统中标记图像几何畸变的校正[J].光学精密工程,2007,15(3):422-427.
[16] M B Sukhaswami, Arun K Pujari. Restoration of geometrically aberrated images using a self-organising neural network[J]. Pattern Recognition Letters, 1996, 17(1): 1-10.
[17]袁野,欧宗瑛.基于径向基函数网络的二维平面标定方法[J].大连理工大学学报,2003,43(2):248-251.
[18] Chao Hu, Max Meng, Peter Xiaoping Liu, et al. Image distortion correction for wireless capsule endoscope[A]. Proceedings of the 2004 IEEE International Conference on Robotics & Automation[C]. New Orleans, USA, 2004: 4718-4723.
[19]史泽林,康娇,孙锐.基于BP神经网络的大视场成像畸变校正方法[J].光学精密工程,2005,13(3):348-353.
[20]王跃轩,易红,倪中华.机器视觉系统中图像误差补偿方法[J].数据采集与处理,2005,20(4):472-477.
[21]王珂娜,邹北骥,黄文梅.一种基于神经网络的畸变图像校正方法[J].中国图象图形学报,2005,10(5):603-607.
[22]陈小明,洪军,阎海红,等.基于Elman神经网络的振镜扫描系统误差校正技术研究[J].西安交通大学学报,2006,40(5):587-590.
[23]闻新,周露,王丹力,等. MATLAB神经网络应用设计[M].北京:科学出版社,2000. 207-328.
[24]邢文训,谢金星.现代优化计算方法[M].北京:清华大学出版社,1999. 193-246.
[25]钟堰利,勾瑞,胡宏清.摄像机图像相对畸变系数的计算[J].光学技术,2002,28(4):382-384.
[26]王伟,钟堰利,吴建坤.广角镜头图像畸变校正系统的研究[J].光学技术,2004,30(4):489-490,494.
[27] B Prescott, G F Mclean. Line-based correction of radial lens distortion[J].Graphical Models and Image Processing, 1997, 59(1): 39-47.
[28] Thorsten Thormahlen, Hellward Broszio. Automatic line-based estimation of radial lens distortion[J]. Integrated Computer-Aided Engineering, 2005, 12(2): 177-190.
[29] Frederic Devernay, Olivier Faugeras. Straight lines have to be straight[J]. Machine Vision and Applications, 2001, 13(1): 14-24.
[30] H Tian, T Srikanthan, K Vijayan Asari, et al. Study on the effect of object to camera distance on polynomial expansion coefficients in barrel distortion correction[A]. Fifth IEEE Southwest Symposium on Image Analysis and Interpretation[C]. Santa Fe, USA, 2002: 255-259.
[31]姜大志,孙俊兰,郁倩,等.标准图形法求解相机镜头非线性畸变的研究[J].东南大学学报(自然科学版),2001,31(4):111-116.
[1]贾云得.机器视觉[M].北京:科学出版社,2000. 82.
[2]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.
[3]章毓晋.图象分割[M].北京:科学出版社,2001. 9-19.
[4] Stephane Mallat, Wen Liang Hwang. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory, 1992, 38(2): 617-643.
[5] 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.
[6] 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.
[7]杨海军,梁德群.一种新的基于信息测度和神经网络的边缘检测方法[J].电子学报,2001,29(1):51-53.
[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]于起峰,陆宏伟,刘肖琳.基于图像的精密测量与运动测量[M].北京:科学出版社,2002. 132-134.
[14] Manfred H Hueckel. A local visual operator which recognizes edges and lines[J]. Journal of the Association for Computing Machinery, 1973, 20(4): 634-647.
[15] Ali J Tabatabai, O Robert Mitchell. Edge location to subpixel values in digital imagery[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, PAMI-6(2): 188-201.
[16] Andres Huertas, Gerard Medioni. Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(5): 651-664.
[17] Edward P Lyvers, Owen Robert Mitchell, Mark L Akey, et al. Subpixel measurements using a moment-based edge operator[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(12): 1293-1309.
[18] Sugata Ghosal, Rajiv Mehrotra. Orthogonal moment operators for subpixel edge detection[J]. Pattern Recognition, 1993, 26(2): 295-306.
[19] M Kisworo, S Venkatesh, G West. Modeling edges at subpixel accuracy using the local energy approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(4): 405-410.
[20] Kris Jensen, Dimitris Anastassiou. Subpixel edge localization and the interpolation of still images[J]. IEEE Transactions on Image Processing, 1995, 4(3): 285-295.
[21] Yu-Shan Li, Tzay Y Young, Joseph A Magerl. Subpixel edge detection and estimation with a microprocessor-controlled line scan camera[J]. IEEE Transactions on Industrial Electronics, 1988, 35(1): 105-112.
[22]张洪涛,段发阶,叶声华.一种快速亚像素边缘检测方法研究[J].计量学报,2002,23(4):263-265,270.
[23]刘冲,夏泽邑,裴伟,等. SLM彩色显微图像的亚像素边缘检测方法[J].机械工程学报,2005,41(1):71-76.
[24]张敏,白小军. B样条函数的火箭弹图像边缘检测算法[J].西安工业学院学报,2005,25(3):242-245.
[25]龚昊,吕乃光,娄小平,等.二维视觉测量中光轴与载物台垂直度调节方法[J].北京机械工业学院学报,2006,21(3):35-38.
[26]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.
[27]吴晓波,钟先信,刘厚权,等.应用多项式插值函数提高面阵CCD尺寸测量的分辨力[J].仪器仪表学报,1996,17(2):154-159.
[28] 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.
[29]李艳,彭嘉雄.基于D2样条插值和LOG算子的亚像素边缘检测[J].华中理工大学学报,2000,28(3):77-79.
[30]乔辉,杨勇,方志良,等.用样条插值算法实现集成块引脚的高精度检测[J].仪器仪表学报,2002,23(S5):1-3.
[31]吴志鹏,刘金根,殷世明,等.红外图像的亚像素边缘检测[J].光电工程,2003,30(2):50-52.
[32]朱颖,江泽涛.基于Sobel算子的亚像素边缘检测方法[J].南昌航空工业学院学报(自然科学版),2005,19(2):100-102.
[33]杨旭强,刘洪臣,冯勇,等.基于B样条插值算法的亚像元技术的研究[J].光学技术,2005,31(5):691-694,697.
[34]孔兵,王昭,谭玉山.基于圆拟合的激光光斑中心检测算法[J].红外与激光工程,2002,31(3):275-279.
[35]杨耀权,施仁,于希宁,等.用Hough变换提高激光光斑中心定位精度的算法[J].光学学报,1999,19(12):1655-1660.
[36] Samuel M Thomas, Y T Chan. A simple approach for the estimation of circular arc center and its radius[J]. Computer Vision, Graphics, and Image Processing, 1989, 45(3): 362-370.
[37]张永宏,胡德金,徐俊杰.基于视觉图像的机加工零部件亚像素边缘定位[J].机械工程学报,2004,40(6):179-182.
[38]吴乐茂,舒宁. Marr方法在多波段遥感影像边缘信息分析中的应用[J].武汉大学学报(信息科学版),2001,26(1):34-39.
[39]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.
[40]李金泉,王建伟,陈善本,等.一种改进的Zernike正交矩亚像素边缘检测算法[J].光学技术,2003,29(4):500-503.
[41]邹宁,柳健,周曼丽.基于Zernike矩的边缘检测方法[J].华中理工大学学报,1999,27(5):1-2,8.
[42]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.
[43]曹国华,徐洪吉,苏成志.大气湍流对激光校准轴系的影响[J].兵工学报,2005,26(3):327-329.
[44]杨永跃.异形曲面视觉坐标在线热检系统研究[D].合肥:合肥工业大学,2004. 119-122.
[45]陈向伟,王龙山,刘庆民,等.基于CCD图像的圆度误差测量的研究[J].半导体光电,2004,25(4):313-316.
[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]徐永凯,王信义,袁洪芳. EST法测量深孔母线直线度的方案及其误差分析[J].北京理工大学学报,2000,20(4):431-434.
[8]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.
[9] 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.
[10]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.
[11]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.
[12]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.
[13]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.
[14]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.
[15] Hao Qun,Zhao Yang,Li Dacheng,et al. Straightness measurement using laser diode and CCD camera[J]. Chinese Journal of Lasers,1999,B8(3):215-220.
[16]田瑞娟.空间直线度测量方法的研究[D].成都:四川大学,2004. 29-41.
[17]张连存,张国玉,付秀华,等.Φ25mm火炮身管直线度光电测量方法[J].光学精密工程,2004,12(5):485-490.
[18]何鲲.大口径长管内孔的直线度、圆度测量研究[D].合肥:合肥工业大学,2004. 5-6.
[1]胡祐德,马东升,张莉松.伺服系统原理与设计(第2版)[M].北京:北京理工大学出版社,1999. 135-188.
[2]金钰,胡祐德,李向春.伺服系统设计指导[M].北京:北京理工大学出版社,2000. 1-29.
[3]胡建华,廖文和,周儒荣. CNC系统中几种升降速控制曲线的研究与比较[J].南京航空航天大学学报,1999,31(6):706-711.
[4]胡科峰.高速高精度运动控制系统的软件技术研究[D].武汉:华中科技大学,2004. 41-49.
[5]刘胜,彭侠夫,叶瑰昀.现代伺服系统设计[M].哈尔滨:哈尔滨工程大学出版社,2001. 9-29.
[6]董吉洪,徐宏.全自动金丝球焊机X-Y工作台的误差分析[J].光学精密工程,2005,13(S1):148-152.
[7] GB/T 5796.4-2005,梯形螺纹第4部分:公差[S]. 2006.
[8]甘永立.几何量公差与检测(第五版)[M].上海:上海科学技术出版社,2001. 150-153.
[9]陆廷济,费定曜,胡德敬,等.大学物理实验[M].上海:同济大学出版社,1996. 11-13.
[10]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. 3-6.
[11]周太明.光源原理与设计[M].上海:复旦大学出版社,1993. 15-325.
[12]王庆有. CCD应用技术[M].天津:天津大学出版社,2000. 27-29,164-166.
[13]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.
[14]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.
[15]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. 171-173.
[16]李炳乾. LED环形光源研制[J].照明工程学报,2007,18(1):64-66.
[17]王蓉,刘玉玲,余飞鸿. LED光源照明微投影仪系统的设计[J].光学仪器,2006,28(2):22-26.
[18]潘玲玲,叶楠,张晔晖.电源稳定性对LED光源稳定性的影响研究[J].现代显示,2006,13(7):20-22.
[19]周小丽,刘木清.近红外光源的稳定性控制[J].光源与照明,2004,27(4):4-5,8.
[20]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.
[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]吴晓波,安文斗,杨钢.图像测量系统中的误差分析及提高测量精度的途径[J].光学精密工程,1997,5(1):133-141.
[3]王庆有. CCD应用技术[M].天津:天津大学出版社,2000. 164-166.
[4]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. 40-41.
[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]许秀贞,李自田,薛利军. CCD噪声分析及处理技术[J].红外与激光工程,2004,33(4):343-346,357.
[7]丁晓华,李由,于起峰,等. CCD噪声标定及其在边缘定位中的应用[J].光学学报,2008,28(1):99-104.
[8]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.
[9]四维科技,胡小锋,赵辉. Visual C++/MATLAB图像处理与识别实用案例精选[M].北京:人民邮电出版社,2004. 63-87.
[10]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.
[11] Stephane Mallat, Wen Liang Hwang. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory, 1992, 38(2): 617-643.
[12] David L Donoho, Iain M Johnstone. Ideal spatial adaptation by wavelet shrinkage[J]. Biometrika, 1994, 81(3): 425-455.
[13] David L Donoho. De-noising by soft-thresholding[J]. IEEE Transactions on Information Theory, 1995, 41(3): 613-627.
[14] Eero P Simoncelli, Edward H Adelson. Noise removal via Bayesian wavelet coring[A]. IEEE International Conference on Image Processing[C]. Lausanne, Switzerland, 1996, 1: 379-382.
[15] Elwood T Olsen, Biquan Lin. A wavelet phase filter for emission tomography[A]. Proceedings of SPIE[C]. Orlando, USA, 1995, 2491/1: 829-839.
[16] Samuel P Kozaitis, Mark A Getbehead. Optical wavelet feature extraction using a multiple-input phase-only encoded joint-transform correlator[J]. Optics Communications, 1998, 151(1-3): 15-20.
[17] G P Nason. Wavelet shrinkage using cross-validation[J]. Journal of the Royal Statistical Society. Series B (Methodological), 1996, 58(2): 463-479.
[18] X Huang, G A Woolsey. Image denoising using Wiener filtering and wavelet thresholding[A]. 2000 IEEE International Conference on Multimedia and Expo[C]. New York, USA, 2000, 3: 1759-1762.
[19] Vasily Strela, Peter Niels Heller, Gilbert Strang, et al. The application of multiwavelet filterbanks to image processing[J]. IEEE Transactions on Image Processing, 1999, 8(4): 548-563.
[20]刘新文,王惠南,钱志余.小波变换对OCT图像的降噪处理[J].光子学报,2006,35(6):935-939.
[21] Thomas S Huang, James W Burnett, Andrew G Deczky. The importance of phase in image processing filters[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1975, ASSP-23(6): 529-542.
[22] Alan V Oppenheim, Jae S Lim. The importance of phase in signals[J]. Proceedings of the IEEE, 1981, 69(5): 529-541.
[23] Peter Kovesi. Phase preserving denoising of images[A]. The Australian Pattern Recognition Society Conference: DICTA 1999[C]. Perth, Australia, 1999: 212-217.
[24] Stephane Mallat.信号处理的小波导引(原书第2版)[M].北京:机械工业出版社,2002. 2-3.
[25]肖志涛,国澄明,于明,等. log Gabor函数在人类视觉系统特性研究中的应用[J].信号处理,2002,18(5):399-402.
[26]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.
[27]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.
[28] Heesung Kwon, Sandor Z Der, Nasser M Nasrabadi. Multisensor target detection using adaptive feature-based fusion[A]. Proceedings of SPIE[C]. Orlando, USA, 2001, 4379: 112-123.
[29]金炜,魏彪,潘英俊,等.一种新颖的Contourlet域中子辐射图像降噪方法[J].光子学报,2006,35(5):760-765.
[30]蔡敦虎,羿旭明.小波基的选取对图像去噪的影响[J].数学杂志,2005,25(2):185-190.
[31] 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.
[32]侯建华,田金文,柳健.一种改进的图像组合滤波方法[J].光子学报,2005,34(11):1748-1751.
[1]章毓晋图象处理和分析[M]北京：清华大学出版社，1999. 179
[2]章毓晋图象分割[M]北京：科学出版社，2001. 2-3
[3] K S Fu, J K Mui. A survey on image segmentation[J]. Pattern Recognition, 1981,13(1): 3-16.
[4] Theo Pavlidis, Yuh-tay Liow. Integrating region growing and edge detection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(3):225-233.
[5] Ioannis Matalas, Ralph Benjamin, Richard Kitney. An edge detection techniqueusing the facet model and parameterized relaxation labeling[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1997, 19(4): 328-341.
[6] W Doyle. Operation useful for similarity-invariant pattern recognition[J]. Journal ofthe Association for Computing Machinery, 1962, 9(2): 259-267.
[7] Nobuyuki Otsu. A threshold selection method from gray-level histograms [J]. IEEETransactions on Systems, Man, and Cybernetics, 1979, SMC-9(1): 62-66.
[8] T W Ridler, S Calvard. Picture thresholding using an iterative selection method[J].IEEE Transactions on Systems, Man, and Cybernetics, 1978, SMC-8(8): 630-632.
[9] H J Trussell. Comments on "Picture thresholding using an iterative selectionmethod"[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979,SMC-9(5):311.
[10] A Magid, S R Rotman, A M Weiss. Comment on "Picture thresholding using aniterative selection method"[J]. IEEE Transactions on Systems, Man, andCybernetics, 1990,20(5): 1238-1239.
[11] J N Kapur, P K Sahoo, A K C Wong. A new method for gray-level picturethresholding using the entropy of the histogram[J]. Computer Vision, Graphics, andImage Processing, 1985, 29(3): 273-285.
[12]袁占亭.基于一维熵的图像阈值自动选取[J].甘肃工业大学学报，1993，19 (1)：84-88
[13] Luc Vincent, Pierre Soille. Watersheds in digital spaces: An efficient algorithmbased on immersion simulations [J]. IEEE Transactions on Pattern Analysis andMachine Intelligence, 1991, 13(6): 583-598.
[14] Kostas Haris, Serafim N Efstratiadis, Nicos Maglaveras, et al. Hybrid imagesegmentation using watersheds and fast region merging[J]. IEEE Transactions onImage Processing, 1998, 7(12): 1684-1699.
[15] Bhabatosh Chanda, Malay K Kundu, Y Vani Padmaja. A multi-scale morphologicedge detector[J]. Pattern Recognition, 1998, 31(10): 1469-1478.
[16] Nathalie Giordana, Wojciech Pieczynski. Estimation of generalized multisensorhidden Markov chains and unsupervised image segmentation[J]. IEEE Transactionson Pattern Analysis and Machine Intelligence, 1997, 19(5): 465-475.
[17]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
[18] Jagath C Rajapakse, Jay N Giedd, Judith L Rapoport. Statistical approach tosegmentation of single-channel cerebral MR images[J]. IEEE Transactions onMedical Imaging, 1997, 16(2): 176-186.
[19] W M Wells III, W E L Grimson, R Kikinis, et al. Adaptive segmentation of MRIdata[J]. IEEE Transactions on Medical Imaging, 1996, 15(4): 429-442.
[20] W E Blanz, Sheri L Gish. A connectionist classifier architecture applied to imagesegmentation[A]. Proceedings of the 10th International Conference on PatternRecognition[C]. New Jersey, USA, 1990, 2: 272-277.
[21] Noboru Babaguchi, Koji Yamada, Koichi Kise, et al. Connectionist modelbinarization[A]. Proceedings of the 10th International Conference on PatternRecognition[C]. New Jersey, USA, 1990, 2: 51-56.
[22] A Ghosh, N R Pal, S K Pal. Image segmentation using a neural network[J]. Biological Cybernetics, 1991,66(2): 151-158.
[23] Jayant Shah. Parameter estimation, multiscale representation and algorithms for energy-minimizing segmentations [A]. Proceedings of the 10th International Conference on Pattern Recognition[C]. New Jersey, USA, 1990, 1: 815-819.
[24]杨新.图像偏微分方程的原理与应用[M].上海：上海交通大学出版社，2003 80-105
[25] 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.
[26] Stanley Osher, Leonid I Rudin. Feature-oriented image enhancement using shockfilters[J]. SIAM Journal on Numerical Analysis, 1990, 27(4): 919-940.
[27] Pietro Perona, Jitendra Malik. Scale-space and edge detection using anisotropicdiffusion[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990,12(7): 629-639.
[28] S D Yanowitz, A M Bruckstein. A new method for image segmentation[J].Computer Vision, Graphics, and Image Processing, 1989, 46(1): 82-95.
[29]沈定刚，戚飞虎.基于神经网络的自适应图像分割[J].上海交通大学学报，1994，28(S1)：39-45
[30]薛景浩，章毓晋，林行刚.二维遗传算法用于图象动态分割[J].自动化学报，2000，26(5)：685-689
[31]贺霖，李见为.基于零交叉检测器的动态阈值选取[J].光电工程，2003，30 (4)：65-68
[32]景云华，董才林，杨扬，等复杂背景下的阈值插值方法[J].计算机工程，2003，29(17)：160-161
[33] Ilya Blayvas, Alfred Bruckstein, Ron Kimmel. Efficient computation of adaptive threshold surfaces for image binarization[A]. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition[C]. Kauai, USA, 2001, 1: 737-742.
[34] J R Parker, C Jennings, A G Salkauskas. Thresholding using an illumination model [A]. Proceedings of the Second International Conference on Document Analysis and Recognition[C]. Tsukuba Science City, Japan, 1993: 270-273.
[35]涂其远，吴建华，万国金动态阈值结合全局阈值对图像进行分割[J].南昌大学学报(工科版)，2002，24(1)：37-40，70
[36]高阳，李言俊，张科.非均匀背景下的红外图像曲面拟合分割[J].光电工程，2006，33(7)：83-87
[37]张永平，郑南宁，赵荣椿基于变分的图像分割算法[J].中国科学(E辑)，2002，32(1)：133-144
[38] Francis H Y Chan, F K Lam, Hui Zhu. Adaptive thresholding by variationalmethod[J]. IEEE Transactions on Image Processing, 1998, 7(3): 468-473.
[39] Fei Liu, Xiaodan Song, Yupin Luo, et al. Adaptive thresholding based onvariational background[J]. Electronics Letters, 2002, 38(18): 1017-1018.
[40] William H Press, Saul A Teukolsky, William T Vetterling, et al. Numerical recipesin C: The art of scientific computing (Second edition)[M]. Cambridge, UK:Cambridge University Press, 1992. 866-869.
[41] S K Pal, R A King, A A Hashim. Automatic grey level thresholding through indexoffuzziness and entropy[J]. Pattern Recognition Letters, 1983, 1(3): 141-146.
[42] C AMurthy, S K Pal. Histogram thresholding by minimizing graylevel fuzziness[J].Information Sciences, 1992,60(1-2): 107-135.
[43] H D Cheng, Jim-rong Chen, Jiguang Li. Threshold selection based on fuzzyc-partition entropy approach[J]. Pattern Recognition, 1998, 31(7): 857-870.
[44] Zhiwei Zhao, Xueqin Li, R W Gunderson. A novel fuzzy entropy clusteringalgorithm[A]. Proceedings of the Third IEEE Conference on Fuzzy Systems [C].Orlando, USA, 1994, 1: 636-641.
[45] H D Cheng, Jim-rong Chen. Automatically determine the membership functionbased on the maximum entropy principle[J]. Information Sciences, 1997, 96(3-4):163-182.
[46] N R Pal, S K Pal. Object-background segmentation using new definitions ofentropy[J]. IEE Proceedings on Computers and Digital Techniques, Part E, 1989,136(4): 284-295.
[47]邢文训，谢金星现代优化计算方法[M]北京：清华大学出版社，1999 90-192
[48]陶文兵，田金文，柳健，等基于遗传算法和模糊熵的前视红外图像分割[J].红外与毫米波学报，2003，22(6)：465-468
[49]周明，孙树栋遗传算法原理及应用[M].北京：国防工业出版社，1999 32-60
[50]康立山，谢云，尤矢勇，等非数值并行算法(第一册)——模拟退火算法[M]北京：科学出版社，1994.22-55
[51]章毓晋图象处理和分析[M]北京：清华大学出版社，1999.141-143
[52]牛建军，刘上乾，韩宝君，等同心圆检测中的区域划分算法[J].光子学报，2006．35(12)1: 1946-1949
[1]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. 154-158.
[2]廖士中,高培焕,苏艺,等.一种光学镜头摄像机图象几何畸变的修正方法[J].中国图象图形学报, 2000, 5A(7):593-596.
[3] Seong-woo Park, Ki-sang Hong. Practical ways to calculate camera lens distortion for real-time camera calibration[J]. Pattern Recognition, 2001, 34(6): 1199-1206.
[4]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.
[5] 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.
[6] 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.
[7]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.
[8]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.
[9]刘金根.一种基于现场定标的光电图像畸变校正算法[J].光子学报,2004,33(6):732-735.
[10] Juyang Weng, Paul Cohen, Marc Herniou. Camera calibration with distortion models and accuracy evaluation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(10): 965-980.
[11] 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.
[12] Hideaki Haneishi, Yutaka Yagihashi, Yoichi Miyake. A new method for distortion correction of electronic endoscope images[J]. IEEE Transactions on Medical Imaging, 1995, 14(3): 548-555.
[13] 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.
[14]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.
[15]王权岱,段玉岗,丁玉成,等.压印对正系统中标记图像几何畸变的校正[J].光学精密工程,2007,15(3):422-427.
[16] M B Sukhaswami, Arun K Pujari. Restoration of geometrically aberrated images using a self-organising neural network[J]. Pattern Recognition Letters, 1996, 17(1): 1-10.
[17]袁野,欧宗瑛.基于径向基函数网络的二维平面标定方法[J].大连理工大学学报,2003,43(2):248-251.
[18] Chao Hu, Max Meng, Peter Xiaoping Liu, et al. Image distortion correction for wireless capsule endoscope[A]. Proceedings of the 2004 IEEE International Conference on Robotics & Automation[C]. New Orleans, USA, 2004: 4718-4723.
[19]史泽林,康娇,孙锐.基于BP神经网络的大视场成像畸变校正方法[J].光学精密工程,2005,13(3):348-353.
[20]王跃轩,易红,倪中华.机器视觉系统中图像误差补偿方法[J].数据采集与处理,2005,20(4):472-477.
[21]王珂娜,邹北骥,黄文梅.一种基于神经网络的畸变图像校正方法[J].中国图象图形学报,2005,10(5):603-607.
[22]陈小明,洪军,阎海红,等.基于Elman神经网络的振镜扫描系统误差校正技术研究[J].西安交通大学学报,2006,40(5):587-590.
[23]闻新,周露,王丹力,等. MATLAB神经网络应用设计[M].北京:科学出版社,2000. 207-328.
[24]邢文训,谢金星.现代优化计算方法[M].北京:清华大学出版社,1999. 193-246.
[25]钟堰利,勾瑞,胡宏清.摄像机图像相对畸变系数的计算[J].光学技术,2002,28(4):382-384.
[26]王伟,钟堰利,吴建坤.广角镜头图像畸变校正系统的研究[J].光学技术,2004,30(4):489-490,494.
[27] B Prescott, G F Mclean. Line-based correction of radial lens distortion[J].Graphical Models and Image Processing, 1997, 59(1): 39-47.
[28] Thorsten Thormahlen, Hellward Broszio. Automatic line-based estimation of radial lens distortion[J]. Integrated Computer-Aided Engineering, 2005, 12(2): 177-190.
[29] Frederic Devernay, Olivier Faugeras. Straight lines have to be straight[J]. Machine Vision and Applications, 2001, 13(1): 14-24.
[30] H Tian, T Srikanthan, K Vijayan Asari, et al. Study on the effect of object to camera distance on polynomial expansion coefficients in barrel distortion correction[A]. Fifth IEEE Southwest Symposium on Image Analysis and Interpretation[C]. Santa Fe, USA, 2002: 255-259.
[31]姜大志,孙俊兰,郁倩,等.标准图形法求解相机镜头非线性畸变的研究[J].东南大学学报(自然科学版),2001,31(4):111-116.
[1]贾云得.机器视觉[M].北京:科学出版社,2000. 82.
[2]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.
[3]章毓晋.图象分割[M].北京:科学出版社,2001. 9-19.
[4] Stephane Mallat, Wen Liang Hwang. Singularity detection and processing with wavelets[J]. IEEE Transactions on Information Theory, 1992, 38(2): 617-643.
[5] 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.
[6] 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.
[7]杨海军,梁德群.一种新的基于信息测度和神经网络的边缘检测方法[J].电子学报,2001,29(1):51-53.
[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]于起峰,陆宏伟,刘肖琳.基于图像的精密测量与运动测量[M].北京:科学出版社,2002. 132-134.
[14] Manfred H Hueckel. A local visual operator which recognizes edges and lines[J]. Journal of the Association for Computing Machinery, 1973, 20(4): 634-647.
[15] Ali J Tabatabai, O Robert Mitchell. Edge location to subpixel values in digital imagery[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, PAMI-6(2): 188-201.
[16] Andres Huertas, Gerard Medioni. Detection of intensity changes with subpixel accuracy using Laplacian-Gaussian masks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, PAMI-8(5): 651-664.
[17] Edward P Lyvers, Owen Robert Mitchell, Mark L Akey, et al. Subpixel measurements using a moment-based edge operator[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(12): 1293-1309.
[18] Sugata Ghosal, Rajiv Mehrotra. Orthogonal moment operators for subpixel edge detection[J]. Pattern Recognition, 1993, 26(2): 295-306.
[19] M Kisworo, S Venkatesh, G West. Modeling edges at subpixel accuracy using the local energy approach[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(4): 405-410.
[20] Kris Jensen, Dimitris Anastassiou. Subpixel edge localization and the interpolation of still images[J]. IEEE Transactions on Image Processing, 1995, 4(3): 285-295.
[21] Yu-Shan Li, Tzay Y Young, Joseph A Magerl. Subpixel edge detection and estimation with a microprocessor-controlled line scan camera[J]. IEEE Transactions on Industrial Electronics, 1988, 35(1): 105-112.
[22]张洪涛,段发阶,叶声华.一种快速亚像素边缘检测方法研究[J].计量学报,2002,23(4):263-265,270.
[23]刘冲,夏泽邑,裴伟,等. SLM彩色显微图像的亚像素边缘检测方法[J].机械工程学报,2005,41(1):71-76.
[24]张敏,白小军. B样条函数的火箭弹图像边缘检测算法[J].西安工业学院学报,2005,25(3):242-245.
[25]龚昊,吕乃光,娄小平,等.二维视觉测量中光轴与载物台垂直度调节方法[J].北京机械工业学院学报,2006,21(3):35-38.
[26]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.
[27]吴晓波,钟先信,刘厚权,等.应用多项式插值函数提高面阵CCD尺寸测量的分辨力[J].仪器仪表学报,1996,17(2):154-159.
[28] 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.
[29]李艳,彭嘉雄.基于D2样条插值和LOG算子的亚像素边缘检测[J].华中理工大学学报,2000,28(3):77-79.
[30]乔辉,杨勇,方志良,等.用样条插值算法实现集成块引脚的高精度检测[J].仪器仪表学报,2002,23(S5):1-3.
[31]吴志鹏,刘金根,殷世明,等.红外图像的亚像素边缘检测[J].光电工程,2003,30(2):50-52.
[32]朱颖,江泽涛.基于Sobel算子的亚像素边缘检测方法[J].南昌航空工业学院学报(自然科学版),2005,19(2):100-102.
[33]杨旭强,刘洪臣,冯勇,等.基于B样条插值算法的亚像元技术的研究[J].光学技术,2005,31(5):691-694,697.
[34]孔兵,王昭,谭玉山.基于圆拟合的激光光斑中心检测算法[J].红外与激光工程,2002,31(3):275-279.
[35]杨耀权,施仁,于希宁,等.用Hough变换提高激光光斑中心定位精度的算法[J].光学学报,1999,19(12):1655-1660.
[36] Samuel M Thomas, Y T Chan. A simple approach for the estimation of circular arc center and its radius[J]. Computer Vision, Graphics, and Image Processing, 1989, 45(3): 362-370.
[37]张永宏,胡德金,徐俊杰.基于视觉图像的机加工零部件亚像素边缘定位[J].机械工程学报,2004,40(6):179-182.
[38]吴乐茂,舒宁. Marr方法在多波段遥感影像边缘信息分析中的应用[J].武汉大学学报(信息科学版),2001,26(1):34-39.
[39]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.
[40]李金泉,王建伟,陈善本,等.一种改进的Zernike正交矩亚像素边缘检测算法[J].光学技术,2003,29(4):500-503.
[41]邹宁,柳健,周曼丽.基于Zernike矩的边缘检测方法[J].华中理工大学学报,1999,27(5):1-2,8.
[42]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.
[43]曹国华,徐洪吉,苏成志.大气湍流对激光校准轴系的影响[J].兵工学报,2005,26(3):327-329.
[44]杨永跃.异形曲面视觉坐标在线热检系统研究[D].合肥:合肥工业大学,2004. 119-122.
[45]陈向伟,王龙山,刘庆民,等.基于CCD图像的圆度误差测量的研究[J].半导体光电,2004,25(4):313-316.
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