关于砂轮地貌双目视觉检测技术的基础研究
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摘要
长期以来,砂轮地貌的研究历来是磨削界的研究热点。因为在磨削加工中,当磨削用量组合一定时,砂轮表面的有效磨粒间距、等高性、磨粒磨损平台面积等地貌参数,对砂轮的加工性能和工件的表面加工质量有着重大影响。若能通过测量量化这些砂轮地貌参数,便可以掌握砂轮的工作状况,进而预测整个磨削过程。因此砂轮地貌的检测对深入研究磨削过程有着重要的意义。
由于砂轮地貌本身和工况的复杂性,使得砂轮地貌的检测比较困难,目前能用于指导实际生产的科研成果鲜见报导。本文在深入分析以往砂轮地貌检测方法的基础上,提出采用计算机双目视觉技术,对砂轮地貌的定量检测开展基础研究。论文完成了以下几项具有创意的基础性研究工作:
(1)根据光轴平行法,研制出砂轮地貌双目视觉检测系统,该系统采用单摄像头,通过被测物体的精确平移,可实现双目图像的采集。
(2)研究数字图像预处理算法。主要包括数字图像的灰度化算法、图像缩放算法和边缘检测算法,分析各种算法的适用场合和优缺点,并对各种算法进行了编程验证及处理效果对比。
(3)研究双目视觉的灰度模板匹配算法。对匹配模板大小进行优化,优化的匹配算法,具有匹配精度高、抗噪声能力强、运算速度高等的综合优势。
(4)采用自制的标准试块,对砂轮地貌双目视觉检测系统进行了标定。分析了系统的误差源并相应提出了改良办法,高度测量误差可控制在5%以下。
(5)改进了基于角点响应函数的快速角点提取算法,该算法不仅省去了改变图像分辨率的步骤,而且保证了高度估算精度;通过测量磨粒表面多个特征点的高度,重构出磨粒的三维形状。
(6)将砂轮地貌双目视觉检测系统应用于单层钎焊金刚石端面砂轮表面形貌的测量中,实现了砂轮形貌主要参数(磨粒高度、磨粒间距)的检测。这种直接检测的方法具有非接触、检测精度较高、检测结果直观等优点。
The research on the grinding wheel topography has been regarded as one of the important fields in the grinding industry for a long time. When the grinding wheel topography parameters are acquired, such as the effective distance between two adjacent grains, uniform grain height and wear-flat areas, the grinding parameters can be optimized, the wear situation can be controlled, and the working condition can be forecasted. In the grinding process, the grinding wheel topography has both great impact on machinability of the grinding wheel and the surface finished quality of the product, so the detection of the grinding wheel topography is significant to reveal further grinding principles.
It is difficult to detect the grinding wheel topography because of its complexity and varying machining conditions, and there are few reports on the production. On the basis of analyzing former methods, the binocular vision technology is used to inspect quantificationally the grinding wheel topography in this paper.
The main creative contents in this paper are as follows:
(1)According to the optical axis parallel approach, the binocular vision detection system of grinding wheel topography is developed. By using a single camera, the binocular images acquisition is realized with the precise translation of the object.
(2)The preprocessing algorithms of digital image are studied. It mainly includes the gray degree processing, scaling and edge extraction algorithm. The characteristic and application scopes of each algorithm are analyzed, and the programming verifications are carried out and the processing results are compared.
(3)The template matching algorithm of binocular images based on the light intensity information is studied. By optimizing the template size, there are integrated advantages of high matching precision, the increased noise resistance performance and high speed calculation course of this algorithm.
(4)By using self-made standard sample, the binocular vision detection system of grinding wheel topography is calibrated. The system is improved by analyzing error source. The measuring error is less than 5%.
(5)The fast corner extraction algorithm based on corner response function is improved. Not only the processing of the resolution of an input image is left out, but also the estimation accuracy of height is guaranteed. By measuring the heights of feature points in the grain surface, the 3D shape of grain is reconstructed.
(6) The brazed diamond monolayer grinding wheel topography is detected with the binocular vision detection system. The height of grains, the distance between two adjacent diamond grains are both obtained. This technique has advantages of non-contact, high precision, and intuitionistic measuring results.
由于砂轮地貌本身和工况的复杂性,使得砂轮地貌的检测比较困难,目前能用于指导实际生产的科研成果鲜见报导。本文在深入分析以往砂轮地貌检测方法的基础上,提出采用计算机双目视觉技术,对砂轮地貌的定量检测开展基础研究。论文完成了以下几项具有创意的基础性研究工作:
(1)根据光轴平行法,研制出砂轮地貌双目视觉检测系统,该系统采用单摄像头,通过被测物体的精确平移,可实现双目图像的采集。
(2)研究数字图像预处理算法。主要包括数字图像的灰度化算法、图像缩放算法和边缘检测算法,分析各种算法的适用场合和优缺点,并对各种算法进行了编程验证及处理效果对比。
(3)研究双目视觉的灰度模板匹配算法。对匹配模板大小进行优化,优化的匹配算法,具有匹配精度高、抗噪声能力强、运算速度高等的综合优势。
(4)采用自制的标准试块,对砂轮地貌双目视觉检测系统进行了标定。分析了系统的误差源并相应提出了改良办法,高度测量误差可控制在5%以下。
(5)改进了基于角点响应函数的快速角点提取算法,该算法不仅省去了改变图像分辨率的步骤,而且保证了高度估算精度;通过测量磨粒表面多个特征点的高度,重构出磨粒的三维形状。
(6)将砂轮地貌双目视觉检测系统应用于单层钎焊金刚石端面砂轮表面形貌的测量中,实现了砂轮形貌主要参数(磨粒高度、磨粒间距)的检测。这种直接检测的方法具有非接触、检测精度较高、检测结果直观等优点。
The research on the grinding wheel topography has been regarded as one of the important fields in the grinding industry for a long time. When the grinding wheel topography parameters are acquired, such as the effective distance between two adjacent grains, uniform grain height and wear-flat areas, the grinding parameters can be optimized, the wear situation can be controlled, and the working condition can be forecasted. In the grinding process, the grinding wheel topography has both great impact on machinability of the grinding wheel and the surface finished quality of the product, so the detection of the grinding wheel topography is significant to reveal further grinding principles.
It is difficult to detect the grinding wheel topography because of its complexity and varying machining conditions, and there are few reports on the production. On the basis of analyzing former methods, the binocular vision technology is used to inspect quantificationally the grinding wheel topography in this paper.
The main creative contents in this paper are as follows:
(1)According to the optical axis parallel approach, the binocular vision detection system of grinding wheel topography is developed. By using a single camera, the binocular images acquisition is realized with the precise translation of the object.
(2)The preprocessing algorithms of digital image are studied. It mainly includes the gray degree processing, scaling and edge extraction algorithm. The characteristic and application scopes of each algorithm are analyzed, and the programming verifications are carried out and the processing results are compared.
(3)The template matching algorithm of binocular images based on the light intensity information is studied. By optimizing the template size, there are integrated advantages of high matching precision, the increased noise resistance performance and high speed calculation course of this algorithm.
(4)By using self-made standard sample, the binocular vision detection system of grinding wheel topography is calibrated. The system is improved by analyzing error source. The measuring error is less than 5%.
(5)The fast corner extraction algorithm based on corner response function is improved. Not only the processing of the resolution of an input image is left out, but also the estimation accuracy of height is guaranteed. By measuring the heights of feature points in the grain surface, the 3D shape of grain is reconstructed.
(6) The brazed diamond monolayer grinding wheel topography is detected with the binocular vision detection system. The height of grains, the distance between two adjacent diamond grains are both obtained. This technique has advantages of non-contact, high precision, and intuitionistic measuring results.
引文
[1]诸兴华.磨削原理,北京,机械工业出版社,1988,6
[2]李介谷.计算机视觉的理论与实践,上海,上海交通大学出版社,1992
[3] DA.Forsyth,J.Ponce著,林学言,王宏译.计算机视觉:一种现代方法,北京,电子工业出版社,2004
[4]成方.稻种质量的机器视觉无损检测研究,[博士学位论文],杭州,浙江大学,2004
[5]凌云.基于机器视觉的谷物外观品质检测技术研究,[博士学位论文],北京,中国农业大学,2004
[6]贾云得.机器视觉,北京,科学出版社,2000
[7] David Marr. Machine Vision, W.H.Freeman & Company, 1982
[8]郭东明,邬春秀等.反求工程中基于图像灰度信息的三维曲面重构,机械工程学报,2003,39(1):47~50
[9]赵东斌,陈善本,吴林.由单目图像获得表面高度算法的分析和实现,计算机学报,2000,23(2):147~151
[10] B.K.P.Horn. Shape from Shading: A method for Obtaining the Shape of a Smooth Opaque Object from One View, Ph.D.dissertaion, Massachusetts Inst of Technology, 1970
[11] A.Bruckstein. On Shape from Shading. CVGIP, 1988, 44:139~154
[12] S.Tandri and Y.H. Yang. Comparison of two Shape from Shading Algorithms. Pattern Recognition Letters, 1990, 11:637~642
[13] K.Ikeuchi and B.Horn. Numerical Shape from Shading and Occluding Boundaries, Artificial Intelligence, 1981, 17:141~184
[14] J. Cayssol and G. Montambaux. Incomplete Andreev reflection in a clean SFS junction. Journal of Magnetism and Magnetic Materials, 2006, 300(1)::94~97
[15]须明,赵荣椿,Maria Petrou.根据形状类型信息由图像中的明暗变化获取物体的表面方向.西北工业大学学报, 2005,23(2): 70~74
[16]廖熠,赵荣椿.从明暗恢复形状的几类典型算法分析与评价.中国图像图形学报,2001,6(A)(10): 953~961
[17] Philip L. Worthington. Reillumination-driven shape from shading. Computer Vision andImage Understanding, 2005, 98(5):325~343
[18] Ghlita O, Mallon J, Whelan PF. Epipolar line extraction using feature matching[A]. Proceeding of the Irish Machine Vision & Image processing (IMVIP)[C]. 2001.87~97
[19] Sang Ho Park, Kyoung Mu Lee and Sang Uk Lee. A Line Feature Matching Technique Based on an Eigenvector Approach. Computer Vision and Image Understanding, 2000, 77(3): 263~283
[20] A.Branca, E. Stella and A. Distante. Feature matching constrained by cross ratio invariance. Pattern Recognition, 2000,33(3):465~481
[21]吴立德.计算机视觉,上海,复旦大学出版社,1993
[22]刘征,赵小松等.自由曲面多目视觉检测技术.天津大学学报,2003,36(2):148~151
[23] Justin. M. Wanek and Chin H Wu. Automated trinocular stereo imaging system for three dimensional surface wave measurements. Ocean Engineering, 2006, 33 (5):723~747
[24] Rafael Garcia, Joan Batlle and Joaquim Salvi. A New Approach to Pose Detection using a Trinocular Stereovision System. Real-Time Imaging, 2002, 8(2):73~93
[25]朱常琳,郭光辉.运动视觉在移动机器人导航与定位中的应用,兰州铁道学院学报,2000,19(6):70~73
[26]宋丽梅.双目立体机器视觉检测系统及其应用,西南科技大学学报,2006,21(3):30~34
[27]段峰,王耀南,雷晓峰,等.机器视觉技术及其应用综述[J],自动化博览,2002,(3):59~62
[28]章炜.机器视觉技术发展及其应用,红外,2006,(2):11~17
[29] Limei song, Xinhua Qu, Yangang Yang, et al. Application of structured Lighting Sensor for online Measurement[J]. Optics and Lasers in Engineering, 2005, 43(10):1118~1126
[30] Alex Ellery. Environment-robot interaction: the basis for mobility in planetary micro-rovers. Robotics and Autonomous Systems, 2005, (51):29~39
[31] Dale C. Ferguson, David M. Wite, el al. The Mars Pathfinder Wheel Abrasion Experiment. Materials & Design, 2001, (22): 555~564
[32]凌云,王一鸣,孙明,孙红,张小超.基于机器视觉的大米外观品质检测装置,农业机械学报,2005,36(9):89~92
[33]张袓勋,张剑清.数字摄影测量发展的机遇与挑战,武汉测绘科技大学学报,2000,25(1):7~11
[34]李智化,王玉文.卡尔曼滤波在图象识别中的应用,哈尔滨师范大学自然科学学报, 2005,21(3):15-17
[35]张海波,伍小平.计算机云纹技术用于三维物体3600C面形轮廓测量,光学学报1994,14(9):998~1000
[36]邓炜,王军安,杨永生.计算机图象识别系统的设计与实现,计算机应用研究2002,17(6):79~80
[37]杨晓波.计算机立体视觉技术及其在纺织业中的应用,上海纺织科技,2001,29(2):61~62
[38]王积分,段世铎.基于小波包分析的催化剂表面图象识别,机器人,1997,19(1):22~27
[39]丘江,杨静,刘波.基于矩和小波变换的目标图象识别,光子学报,2001,30(7):836~840
[40]谢筱华,罗立民,韦钰.基于矩的三维图象边界检测,计算机学报,1994,17(1):1~8
[41]丘江,刘波,杨静.基于高阶胡氏矩的多目标图象识别算法,光子学报,2001,30(9):1141~1145
[42]王耀明,严炜,俞时权.基于Radon变换的图象矩特征抽取及其在图象识别中的应用,计算机工程,2001,27(2):82~89
[43]商立群.基于Zernike矩的图象识别,西安科技学院学报,2000,20(2):151~153
[44]商立群,杜亚娟. Hu矩和Zernike矩在图象识别中的应用,西安科技学院学报,2000,20(1):53~56
[45]胡师彦.混合遗传BP算法在图象识别中的应用,吉首大学学报,2001,22(4):57~59
[46]杨忠根.从单幅透视投影灰度图象识别和复原旋转体,中国图象图形学报:A辑,1998,3(7):538~543
[47]方从富,马云善,徐西鹏.锯切花岗石中金刚石节块形貌变化过程跟踪,金刚石与磨料磨具工程,2006年第5期:20~23
[48]周艳平,阎学伟.添加剂Li8SiN4对CBN晶体形貌的影响,高圧物理学报,1996,10(1): 56~62
[49]倪月琴,璟龄,赵琰,王继锋,袁伟宏.工业用加氢脱氮催化剂MoS2的形貌,催化学报,1994,15(6):421~425
[50]冯克明. Vs=50m/s条件下修整用量对砂轮表面形貌参数和工件表面粗糙度的影响,广西机械,1997年第2期:48~49
[51]王勇,李汉中.用触针法对砂轮表面形貌的检测,江苏工学院学报,1984年第3期:23~34
[52]傅玉灿,徐鸿钧.一种适于国内引进开发的新型超硬磨料砂轮——国外单层高温钎焊超硬磨料砂轮制造技术述评.中国机械工程,1999,10(4):375-377
[53] Frederick Mason. High-efficiency deep grinding(HEDG). American Machinist, 1993, 137(5):317-319
[54] Inasaki I, Howes T D. Abrasive machining in the future. Annals of the CIRP, 1993, 42(2):723-731
[55]诸兴华.磨削原理.北京,机械工业出版社,1988,6:16~25
[56]李汉中.砂轮工作面形貌的检测,磨料磨具与磨削,1986,4(34):38~46
[57]傅玉灿,徐鸿钧.高效磨削用砂轮地貌的优化设计研究,应用科学学报,2001,(3):48~52
[58]郁倩,徐文宽.关于砂轮表面形貌的研究,盐城工学院学报,1999,(9):57~60
[59] Katsushi, Furutani, Noriyuki Ohguro, et al. In_process Measurement of Topography of Grinding Wheel by Using Hydrodynamic Pressure [J]. Journal of Engineering manufacture, 2003, 217(2): 153~159
[60]李晓天,孙方宏,陈明,翁世修.树脂结合剂砂轮表面形貌的激光扫描测量与分析,机械科学与分析,2001,(1):115~116
[61]薛伟,李汉中.激光功率谱在检测磨削砂轮形貌中的应用,机电工程,1996,(2):43~44
[62]薛伟.激光功率谱检测磨削砂轮形貌的理论分析,机电工程,1996,(4):30~33
[63]杨勇生,王珉,李汉中.砂轮形貌的激光功率谱特性及其检测,南京航空航天大学学报,1997,(2):79~83
[64]刘传才.图像理解与计算机视觉,厦门,厦门大学出版社,2002
[65]章毓晋.图像处理和分析基础,北京,高等教育出版社,2002
[66]章毓晋.图像工程―图像理解与计算机视觉(下册),北京,清华大学出版社,2000
[67] Pizlo Z, Rosenfeld A, Wiess I, et al. The geometry of visual space: about the incompatibility between science and mathematics. CVIU, 1997,65(3):425~454
[68] Tasi R Y. A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the shelf TV camera and lenses, IEEE J, RA1987,3(4):323~344
[69] Ramesh Jain, Rangachar Kasturi, Brian G. Schunck.Machine Vision,北京:机械工业出版社, 2003
[70]周铭.计算机立体视觉技术在工业测量中的应用研究,计量与测试技术,1999,26(4):9~10
[71]古超英.人脸识别及其在公安工作中的应用研究,中国人民公安大学学报:自然科学版,2006,12(3):25~27
[72]张幼桢.金属切削理论,北京,航空工业出版社,1988,6
[73]周泽华.金属切削原理[M],上海,上海科技出版社,1984:183~186
[74]王登化,陈剑飞,乔国谦.砂轮修整质量的地貌评价研究,中原工学院学报,2006,17(5):53~57
[75]王新成.高级图像处理技术.北京,中国科学技术出版社,2001
[76]李启炎,叶建雄,何文欣.图像处理与图像制作.上海,同济大学出版社,2000
[77]刘骏.数字图像处理及高级应用.北京,科学出版社,2003
[78]胡争辉.Delphi7图形图像多媒体高级控件开发与应用.北京,中国铁道出版社,2003
[79]郭平.数字图像处理讲义.北京,北京师范大学,2004
[80] M. Muneyasu, K. Hotta and T. Hinamoto. Image Restoration by Hopfield Networks Considering the Line Process. Transaction of Neural Networks on IEEE International Conference. 1995:1703~ 1707
[81] Y. Uchiyama, M. Haseyama and H. Kitajima. HOPFIELD NEURAL NETWORKS FOR EDGE DETECTION. Transaction of 2001 IEEE International Symposium on Circuits and Systems. 2001: 607~611
[82]杨新,李介谷等.一种基于梯度算子的图像匹配算法,电子学报,1999,27(10):30~33
[83] Fisher R B, Mackirdy A. Integrating Iconic and Structured Matching[C]//Proceeding of 5th European Conference on Computer Vision, Cambrige, UK, 1998:687~698
[84] D.L.bamea, H.F.Silvermann. A class of algorithms for fast digital image registration. IEEE Trans. Computer. 1972, C21 (2):179~186
[85] Niranjan. K, Prasad, Ramamoorthy. B. Tool wear evaluation by stereo vision and prediction by artificial neural network. Journal of Materials Processing Technology. 2001, 112:43-52
[86]李强,张拨.一种基于图像灰度的快速匹配算法.软件学报, 2006, 17(2): 216-222
[87] Abdel Aziz Y I, Karara H M. Direct linear transformation into object space coordinates in close-range photogrammetry. In: Proc Symposium. Close-Range photogrammetry. 1971. 1～18
[88] Brown D C. Decentring Distortion of Lenses. Photogram metric Eng. Remote Sensing. 1966. 444～462
[89] Wong KW. Mathematical foundation and digital analysis in close range photogrammetry. In:Proc. 13th Congress of the Int. Society for Photogrammetry. 1976,1355～1373
[90]杨文强,张保生.计算机视觉摄像机标定技术及其应用研究.中国科技信息. 2007,6(11):124-127
[91] R. Y. Tsai. A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robotics and Automation. 1987. 3(4): 323～344
[92] David A. Forsyth, Jean Ponce. Computer Vision: A Modern Approach. Beijing: Publish House of Electronic industry. 2004
[93]邱茂林,马颂德,李毅.计算机视觉中摄像机定标综述.自动化学报.2000,26(1):43～55
[94]伍冬雪.计算机视觉中摄像机定标及位姿和运动估计方法的研究. [博士学位论文],长砂,湖南大学,2004
[95]王志强.双目视觉技术在砂轮地貌检测中的可行性研究. [硕士学位论文],南京,南京航空航天大学,2005
[96]刘佳音,王忠立,贾云得.一种双目立体视觉系统的误差分析方法[J].光学技术,2003,29(3):354～355.
[97] Shin S W. When should we consider lens distortion in camera calibration [J]. Pattern Recognition , 1995 ,28 (3): 447～461
[98] Stephen S, Michael B. Ground Plane Estimation. Error Analysis and Applications[J],Robotics and Autonomous Systems, 2002, 39(2):59～71
[99] Ajit Singh, Michael Shneier. Grey level corner detection: A generalization and a robust real time implementation. Computer Vision, Graphics, and Image Processing, 1990, 51(1): 54～69
[100] Ronald Lumia, Robert M, et al. Texture analysis of aerial photographs. Pattern Recognition, 1983, 16(1): 39～46
[101] Les Kitchen, Azriel Rosenfeld. Gray-level corner detection. Pattern Recognition Letters, 1982, 1(2): 95～102
[102] Rachid Deriche, Olivier Faugeras. Tracking line segments. Image and Vision Computing, 1990, 8(4): 261～270
[103] C. Harris, M. Stephens. A combined comer and edge detector. Proc. 4th Alvey Vision Conf,1988, 147～151.
[104] Krishnan Rangarajan, Mubarak Shah and David Van Brackle. Optimal corner detector. Computer Vision, Graphics, and Image Processing, 1989, 48(2): 230～245
[105] Miroslav Trajkovi, Mark Hedley. Fast corner detection. Image and Vision Computing, 1998, 16(2): 75～87
[106] Smith. S M, Brady. J M. SUSAN-a new approach to low level image processing [J]. International Journal of Computer Vision. 1997, 23(1): 45~78.
[107]陈利军.图像角点检测和匹配算法的研究. [硕士学位论文],西安,西安电子科技大学,2005
[108]张坤华,王敬儒,张启衡。多特征复合的角点提取方法。中国图象图形学报,2002,7(4): 319~324
[109] Jaeger M,de Reffye P.Basic concepts of computer simu- lation of plant growth[J]. Biosci, 1992, 17 (3): 275~291.
[110]郭焱,李保国.玉米冠层的数学描述与三维重建研究[J].应用生态学报,1999,10(1):39~41
[111] Levitski R E,Lipsitz D,Chauvet A E. Magnetic resonance imaging of the cervical spine in 27 dogs. Vet Radio Utrasonic und, 1999, 40(4): 332~341
[112]伏晓,宦怡.脊柱三维重建基本方位及其临床应用价值.第四军医大学学报,2001,22(13):1236~1239
[113]郑小强,张小锋,胡景春.SFS的变分方法研究与实现.南昌航空大学学报(自然科学版),2007,21(1): 17~20
[114]高文,陈熙霖。计算机视觉-算法与系统原理。清华大学出版社,1999.2:68~69
[115]王青,王融清,鲍虎军,彭群生.散乱数据点的增量快速曲面重建算法.软件学报,2000,9(12):1221~1227
[116]张宗华,彭翔,史伟强,胡小唐.平面域任意散乱点自动三角化的研究.工程图学学报,2000,2(2):38~45
[117]谢勇君,史铁林等.基于亚像素综合定位匹配算法的MEMS平面运动测量.纳米技术与精密工程,2005,3(2):92~96
[2]李介谷.计算机视觉的理论与实践,上海,上海交通大学出版社,1992
[3] DA.Forsyth,J.Ponce著,林学言,王宏译.计算机视觉:一种现代方法,北京,电子工业出版社,2004
[4]成方.稻种质量的机器视觉无损检测研究,[博士学位论文],杭州,浙江大学,2004
[5]凌云.基于机器视觉的谷物外观品质检测技术研究,[博士学位论文],北京,中国农业大学,2004
[6]贾云得.机器视觉,北京,科学出版社,2000
[7] David Marr. Machine Vision, W.H.Freeman & Company, 1982
[8]郭东明,邬春秀等.反求工程中基于图像灰度信息的三维曲面重构,机械工程学报,2003,39(1):47~50
[9]赵东斌,陈善本,吴林.由单目图像获得表面高度算法的分析和实现,计算机学报,2000,23(2):147~151
[10] B.K.P.Horn. Shape from Shading: A method for Obtaining the Shape of a Smooth Opaque Object from One View, Ph.D.dissertaion, Massachusetts Inst of Technology, 1970
[11] A.Bruckstein. On Shape from Shading. CVGIP, 1988, 44:139~154
[12] S.Tandri and Y.H. Yang. Comparison of two Shape from Shading Algorithms. Pattern Recognition Letters, 1990, 11:637~642
[13] K.Ikeuchi and B.Horn. Numerical Shape from Shading and Occluding Boundaries, Artificial Intelligence, 1981, 17:141~184
[14] J. Cayssol and G. Montambaux. Incomplete Andreev reflection in a clean SFS junction. Journal of Magnetism and Magnetic Materials, 2006, 300(1)::94~97
[15]须明,赵荣椿,Maria Petrou.根据形状类型信息由图像中的明暗变化获取物体的表面方向.西北工业大学学报, 2005,23(2): 70~74
[16]廖熠,赵荣椿.从明暗恢复形状的几类典型算法分析与评价.中国图像图形学报,2001,6(A)(10): 953~961
[17] Philip L. Worthington. Reillumination-driven shape from shading. Computer Vision andImage Understanding, 2005, 98(5):325~343
[18] Ghlita O, Mallon J, Whelan PF. Epipolar line extraction using feature matching[A]. Proceeding of the Irish Machine Vision & Image processing (IMVIP)[C]. 2001.87~97
[19] Sang Ho Park, Kyoung Mu Lee and Sang Uk Lee. A Line Feature Matching Technique Based on an Eigenvector Approach. Computer Vision and Image Understanding, 2000, 77(3): 263~283
[20] A.Branca, E. Stella and A. Distante. Feature matching constrained by cross ratio invariance. Pattern Recognition, 2000,33(3):465~481
[21]吴立德.计算机视觉,上海,复旦大学出版社,1993
[22]刘征,赵小松等.自由曲面多目视觉检测技术.天津大学学报,2003,36(2):148~151
[23] Justin. M. Wanek and Chin H Wu. Automated trinocular stereo imaging system for three dimensional surface wave measurements. Ocean Engineering, 2006, 33 (5):723~747
[24] Rafael Garcia, Joan Batlle and Joaquim Salvi. A New Approach to Pose Detection using a Trinocular Stereovision System. Real-Time Imaging, 2002, 8(2):73~93
[25]朱常琳,郭光辉.运动视觉在移动机器人导航与定位中的应用,兰州铁道学院学报,2000,19(6):70~73
[26]宋丽梅.双目立体机器视觉检测系统及其应用,西南科技大学学报,2006,21(3):30~34
[27]段峰,王耀南,雷晓峰,等.机器视觉技术及其应用综述[J],自动化博览,2002,(3):59~62
[28]章炜.机器视觉技术发展及其应用,红外,2006,(2):11~17
[29] Limei song, Xinhua Qu, Yangang Yang, et al. Application of structured Lighting Sensor for online Measurement[J]. Optics and Lasers in Engineering, 2005, 43(10):1118~1126
[30] Alex Ellery. Environment-robot interaction: the basis for mobility in planetary micro-rovers. Robotics and Autonomous Systems, 2005, (51):29~39
[31] Dale C. Ferguson, David M. Wite, el al. The Mars Pathfinder Wheel Abrasion Experiment. Materials & Design, 2001, (22): 555~564
[32]凌云,王一鸣,孙明,孙红,张小超.基于机器视觉的大米外观品质检测装置,农业机械学报,2005,36(9):89~92
[33]张袓勋,张剑清.数字摄影测量发展的机遇与挑战,武汉测绘科技大学学报,2000,25(1):7~11
[34]李智化,王玉文.卡尔曼滤波在图象识别中的应用,哈尔滨师范大学自然科学学报, 2005,21(3):15-17
[35]张海波,伍小平.计算机云纹技术用于三维物体3600C面形轮廓测量,光学学报1994,14(9):998~1000
[36]邓炜,王军安,杨永生.计算机图象识别系统的设计与实现,计算机应用研究2002,17(6):79~80
[37]杨晓波.计算机立体视觉技术及其在纺织业中的应用,上海纺织科技,2001,29(2):61~62
[38]王积分,段世铎.基于小波包分析的催化剂表面图象识别,机器人,1997,19(1):22~27
[39]丘江,杨静,刘波.基于矩和小波变换的目标图象识别,光子学报,2001,30(7):836~840
[40]谢筱华,罗立民,韦钰.基于矩的三维图象边界检测,计算机学报,1994,17(1):1~8
[41]丘江,刘波,杨静.基于高阶胡氏矩的多目标图象识别算法,光子学报,2001,30(9):1141~1145
[42]王耀明,严炜,俞时权.基于Radon变换的图象矩特征抽取及其在图象识别中的应用,计算机工程,2001,27(2):82~89
[43]商立群.基于Zernike矩的图象识别,西安科技学院学报,2000,20(2):151~153
[44]商立群,杜亚娟. Hu矩和Zernike矩在图象识别中的应用,西安科技学院学报,2000,20(1):53~56
[45]胡师彦.混合遗传BP算法在图象识别中的应用,吉首大学学报,2001,22(4):57~59
[46]杨忠根.从单幅透视投影灰度图象识别和复原旋转体,中国图象图形学报:A辑,1998,3(7):538~543
[47]方从富,马云善,徐西鹏.锯切花岗石中金刚石节块形貌变化过程跟踪,金刚石与磨料磨具工程,2006年第5期:20~23
[48]周艳平,阎学伟.添加剂Li8SiN4对CBN晶体形貌的影响,高圧物理学报,1996,10(1): 56~62
[49]倪月琴,璟龄,赵琰,王继锋,袁伟宏.工业用加氢脱氮催化剂MoS2的形貌,催化学报,1994,15(6):421~425
[50]冯克明. Vs=50m/s条件下修整用量对砂轮表面形貌参数和工件表面粗糙度的影响,广西机械,1997年第2期:48~49
[51]王勇,李汉中.用触针法对砂轮表面形貌的检测,江苏工学院学报,1984年第3期:23~34
[52]傅玉灿,徐鸿钧.一种适于国内引进开发的新型超硬磨料砂轮——国外单层高温钎焊超硬磨料砂轮制造技术述评.中国机械工程,1999,10(4):375-377
[53] Frederick Mason. High-efficiency deep grinding(HEDG). American Machinist, 1993, 137(5):317-319
[54] Inasaki I, Howes T D. Abrasive machining in the future. Annals of the CIRP, 1993, 42(2):723-731
[55]诸兴华.磨削原理.北京,机械工业出版社,1988,6:16~25
[56]李汉中.砂轮工作面形貌的检测,磨料磨具与磨削,1986,4(34):38~46
[57]傅玉灿,徐鸿钧.高效磨削用砂轮地貌的优化设计研究,应用科学学报,2001,(3):48~52
[58]郁倩,徐文宽.关于砂轮表面形貌的研究,盐城工学院学报,1999,(9):57~60
[59] Katsushi, Furutani, Noriyuki Ohguro, et al. In_process Measurement of Topography of Grinding Wheel by Using Hydrodynamic Pressure [J]. Journal of Engineering manufacture, 2003, 217(2): 153~159
[60]李晓天,孙方宏,陈明,翁世修.树脂结合剂砂轮表面形貌的激光扫描测量与分析,机械科学与分析,2001,(1):115~116
[61]薛伟,李汉中.激光功率谱在检测磨削砂轮形貌中的应用,机电工程,1996,(2):43~44
[62]薛伟.激光功率谱检测磨削砂轮形貌的理论分析,机电工程,1996,(4):30~33
[63]杨勇生,王珉,李汉中.砂轮形貌的激光功率谱特性及其检测,南京航空航天大学学报,1997,(2):79~83
[64]刘传才.图像理解与计算机视觉,厦门,厦门大学出版社,2002
[65]章毓晋.图像处理和分析基础,北京,高等教育出版社,2002
[66]章毓晋.图像工程―图像理解与计算机视觉(下册),北京,清华大学出版社,2000
[67] Pizlo Z, Rosenfeld A, Wiess I, et al. The geometry of visual space: about the incompatibility between science and mathematics. CVIU, 1997,65(3):425~454
[68] Tasi R Y. A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the shelf TV camera and lenses, IEEE J, RA1987,3(4):323~344
[69] Ramesh Jain, Rangachar Kasturi, Brian G. Schunck.Machine Vision,北京:机械工业出版社, 2003
[70]周铭.计算机立体视觉技术在工业测量中的应用研究,计量与测试技术,1999,26(4):9~10
[71]古超英.人脸识别及其在公安工作中的应用研究,中国人民公安大学学报:自然科学版,2006,12(3):25~27
[72]张幼桢.金属切削理论,北京,航空工业出版社,1988,6
[73]周泽华.金属切削原理[M],上海,上海科技出版社,1984:183~186
[74]王登化,陈剑飞,乔国谦.砂轮修整质量的地貌评价研究,中原工学院学报,2006,17(5):53~57
[75]王新成.高级图像处理技术.北京,中国科学技术出版社,2001
[76]李启炎,叶建雄,何文欣.图像处理与图像制作.上海,同济大学出版社,2000
[77]刘骏.数字图像处理及高级应用.北京,科学出版社,2003
[78]胡争辉.Delphi7图形图像多媒体高级控件开发与应用.北京,中国铁道出版社,2003
[79]郭平.数字图像处理讲义.北京,北京师范大学,2004
[80] M. Muneyasu, K. Hotta and T. Hinamoto. Image Restoration by Hopfield Networks Considering the Line Process. Transaction of Neural Networks on IEEE International Conference. 1995:1703~ 1707
[81] Y. Uchiyama, M. Haseyama and H. Kitajima. HOPFIELD NEURAL NETWORKS FOR EDGE DETECTION. Transaction of 2001 IEEE International Symposium on Circuits and Systems. 2001: 607~611
[82]杨新,李介谷等.一种基于梯度算子的图像匹配算法,电子学报,1999,27(10):30~33
[83] Fisher R B, Mackirdy A. Integrating Iconic and Structured Matching[C]//Proceeding of 5th European Conference on Computer Vision, Cambrige, UK, 1998:687~698
[84] D.L.bamea, H.F.Silvermann. A class of algorithms for fast digital image registration. IEEE Trans. Computer. 1972, C21 (2):179~186
[85] Niranjan. K, Prasad, Ramamoorthy. B. Tool wear evaluation by stereo vision and prediction by artificial neural network. Journal of Materials Processing Technology. 2001, 112:43-52
[86]李强,张拨.一种基于图像灰度的快速匹配算法.软件学报, 2006, 17(2): 216-222
[87] Abdel Aziz Y I, Karara H M. Direct linear transformation into object space coordinates in close-range photogrammetry. In: Proc Symposium. Close-Range photogrammetry. 1971. 1～18
[88] Brown D C. Decentring Distortion of Lenses. Photogram metric Eng. Remote Sensing. 1966. 444～462
[89] Wong KW. Mathematical foundation and digital analysis in close range photogrammetry. In:Proc. 13th Congress of the Int. Society for Photogrammetry. 1976,1355～1373
[90]杨文强,张保生.计算机视觉摄像机标定技术及其应用研究.中国科技信息. 2007,6(11):124-127
[91] R. Y. Tsai. A versatile camera calibration technique for high accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robotics and Automation. 1987. 3(4): 323～344
[92] David A. Forsyth, Jean Ponce. Computer Vision: A Modern Approach. Beijing: Publish House of Electronic industry. 2004
[93]邱茂林,马颂德,李毅.计算机视觉中摄像机定标综述.自动化学报.2000,26(1):43～55
[94]伍冬雪.计算机视觉中摄像机定标及位姿和运动估计方法的研究. [博士学位论文],长砂,湖南大学,2004
[95]王志强.双目视觉技术在砂轮地貌检测中的可行性研究. [硕士学位论文],南京,南京航空航天大学,2005
[96]刘佳音,王忠立,贾云得.一种双目立体视觉系统的误差分析方法[J].光学技术,2003,29(3):354～355.
[97] Shin S W. When should we consider lens distortion in camera calibration [J]. Pattern Recognition , 1995 ,28 (3): 447～461
[98] Stephen S, Michael B. Ground Plane Estimation. Error Analysis and Applications[J],Robotics and Autonomous Systems, 2002, 39(2):59～71
[99] Ajit Singh, Michael Shneier. Grey level corner detection: A generalization and a robust real time implementation. Computer Vision, Graphics, and Image Processing, 1990, 51(1): 54～69
[100] Ronald Lumia, Robert M, et al. Texture analysis of aerial photographs. Pattern Recognition, 1983, 16(1): 39～46
[101] Les Kitchen, Azriel Rosenfeld. Gray-level corner detection. Pattern Recognition Letters, 1982, 1(2): 95～102
[102] Rachid Deriche, Olivier Faugeras. Tracking line segments. Image and Vision Computing, 1990, 8(4): 261～270
[103] C. Harris, M. Stephens. A combined comer and edge detector. Proc. 4th Alvey Vision Conf,1988, 147～151.
[104] Krishnan Rangarajan, Mubarak Shah and David Van Brackle. Optimal corner detector. Computer Vision, Graphics, and Image Processing, 1989, 48(2): 230～245
[105] Miroslav Trajkovi, Mark Hedley. Fast corner detection. Image and Vision Computing, 1998, 16(2): 75～87
[106] Smith. S M, Brady. J M. SUSAN-a new approach to low level image processing [J]. International Journal of Computer Vision. 1997, 23(1): 45~78.
[107]陈利军.图像角点检测和匹配算法的研究. [硕士学位论文],西安,西安电子科技大学,2005
[108]张坤华,王敬儒,张启衡。多特征复合的角点提取方法。中国图象图形学报,2002,7(4): 319~324
[109] Jaeger M,de Reffye P.Basic concepts of computer simu- lation of plant growth[J]. Biosci, 1992, 17 (3): 275~291.
[110]郭焱,李保国.玉米冠层的数学描述与三维重建研究[J].应用生态学报,1999,10(1):39~41
[111] Levitski R E,Lipsitz D,Chauvet A E. Magnetic resonance imaging of the cervical spine in 27 dogs. Vet Radio Utrasonic und, 1999, 40(4): 332~341
[112]伏晓,宦怡.脊柱三维重建基本方位及其临床应用价值.第四军医大学学报,2001,22(13):1236~1239
[113]郑小强,张小锋,胡景春.SFS的变分方法研究与实现.南昌航空大学学报(自然科学版),2007,21(1): 17~20
[114]高文,陈熙霖。计算机视觉-算法与系统原理。清华大学出版社,1999.2:68~69
[115]王青,王融清,鲍虎军,彭群生.散乱数据点的增量快速曲面重建算法.软件学报,2000,9(12):1221~1227
[116]张宗华,彭翔,史伟强,胡小唐.平面域任意散乱点自动三角化的研究.工程图学学报,2000,2(2):38~45
[117]谢勇君,史铁林等.基于亚像素综合定位匹配算法的MEMS平面运动测量.纳米技术与精密工程,2005,3(2):92~96