多传感器融合智能检测机器人的研究及应用
|
摘要
视觉是人类最完善的感知系统之一，而模拟视觉的机器视觉也是人工智能领域中最活跃的分支之一。多传感器融合智能检测机器人是以机器视觉技术为基础，光机电一体化的智能检测设备。目前，我国机器视觉技术的研究和应用仍处于初级阶段，与发达国家相比还有不小的差距。对多传感器融合智能检测机器人的研究不仅符合市场对此类大型智能检测设备的需求，也有利于促进机器视觉技术的研究和应用。
本文在研究国内外相关资料的基础上，提出了一种多传感器融合智能检测机器人的总体设计方案，并详细介绍了各部分的结构和功能。该多传感器智能检测机器人的电气控制系统采用了分层控制体系，主控计算机负责管理和通讯，而PLC负责底层控制。同时为了能在高速流水生产线上平稳分离物品，还研制了一种新型击出器。该击出器采用了阶梯式击打的方法，即使击出对象在高速运动中也不易翻倒。
本文重点论述了多传感器融合智能检测机器人中的机器视觉系统的设计。具体讨论了摄像机和图像采集卡的选择原则，并确定了智能检测机器人的摄像机和图像采集卡。光源及照明方案对机器视觉系统具有重要意义。本文根据实际的需求设计了系统的照明方案，保证了机器视觉系统获取图像的质量。本文还讨论了如何进行机器视觉系统的软件开发的问题。
多传感器融合智能检测机器人具有瓶身检测、瓶口检测、瓶底检测和液位检测等检测功能。为了实现这些功能，针对实际检测的特点，在分析检测对象的基础上，提出了一套实用的检测算法。经过运行表明，在多传感器智能检测机器人上使用这些检测算法，检测速度可达10瓶／秒，平均检测的准确率超过95％。
根据本文研究的各项内容，已经研制成了试验样机，并在样机上对本文所述的理论和算法进行了大量的实验，证明了设计和研究结果的有效性，为今后的进一步研究和开发打下了良好的基础。
Vision is one of the most perfect sensation systems of human, and the machine vision, which can simulate human's vision, is also one of the most active branches in the domain of artificial intelligence. The multi-sensor fusion intelligent inspector based on machine vision technology is the intelligent inspection equipment integrated of optical, mechanical and electronic functions. At present, the research and application of machine vision technology is still in its initial stage in China; compared with the developed country it also has much disparity. Studying the intelligent inspector based on multi-sensor fusion not only conforms to the market demand of this kind of large-type intelligence inspector, but also is advantageous to the promotion of the research and application of the machine vision technology.
Based on the studies of the related data from at home and abroad, this article presents the system design plan for the intelligent inspector based on multi-sensor fusion, and in detail introduces the structures and the function of each part. Multi-layer control system is used in the electric control system of the intelligent inspector based on multi-sensor fusion. The host computer is responsible for higher layer management and communication, while PLC is responsible for lower layer control. In order to separate goods from the high-speed production line steadily, one kind of new ejector is developed. The ejector adopts the method that hits goods with a ladder-type stroke. Thus, the ejected object is not easy to turn over even if it is on the high- speed movement.
This article emphasizes on elaborating the design of the machine vision system in the intelligent inspector based on multi-sensor fusion. Specifically, it discusses the selection principle of the camera and the image capture card, and determines the use of intelligence inspector's cameras and image capture card. Since the light source and the illumination plan is of a vital significance to the machine vision system, this article designs the system illumination plan according to the actual demands, thus ensuring the image quality gained by the machine vision system. This text has also discussed how to carry on the software development of the machine vision system.
The intelligent inspector based on multi-sensor fusion has the examination functions for bottle body, the bottle mouth, the bottle bottom and fluid position. To achieve these functions, with the consideration of the actual inspection features, this article puts forward to a set of practical examination algorithms on the basis of analyzing inspected targets. The operation indicates that, by using these examination algorithms in the intelligent inspector based on multi-sensor fusion, the examination speed can reach 10 bottles/second, and the average examination accurate rate surpasses 95%.
According to studies in this article, the experimental equipment has already been developed. The theory and the algorithms stated in this article have been put into use on the massive experiments on the inspector. The results of the experiments have proven the validity of the design and the findings. So, it has laid a good foundation for further research and development in the future.
本文在研究国内外相关资料的基础上，提出了一种多传感器融合智能检测机器人的总体设计方案，并详细介绍了各部分的结构和功能。该多传感器智能检测机器人的电气控制系统采用了分层控制体系，主控计算机负责管理和通讯，而PLC负责底层控制。同时为了能在高速流水生产线上平稳分离物品，还研制了一种新型击出器。该击出器采用了阶梯式击打的方法，即使击出对象在高速运动中也不易翻倒。
本文重点论述了多传感器融合智能检测机器人中的机器视觉系统的设计。具体讨论了摄像机和图像采集卡的选择原则，并确定了智能检测机器人的摄像机和图像采集卡。光源及照明方案对机器视觉系统具有重要意义。本文根据实际的需求设计了系统的照明方案，保证了机器视觉系统获取图像的质量。本文还讨论了如何进行机器视觉系统的软件开发的问题。
多传感器融合智能检测机器人具有瓶身检测、瓶口检测、瓶底检测和液位检测等检测功能。为了实现这些功能，针对实际检测的特点，在分析检测对象的基础上，提出了一套实用的检测算法。经过运行表明，在多传感器智能检测机器人上使用这些检测算法，检测速度可达10瓶／秒，平均检测的准确率超过95％。
根据本文研究的各项内容，已经研制成了试验样机，并在样机上对本文所述的理论和算法进行了大量的实验，证明了设计和研究结果的有效性，为今后的进一步研究和开发打下了良好的基础。
Vision is one of the most perfect sensation systems of human, and the machine vision, which can simulate human's vision, is also one of the most active branches in the domain of artificial intelligence. The multi-sensor fusion intelligent inspector based on machine vision technology is the intelligent inspection equipment integrated of optical, mechanical and electronic functions. At present, the research and application of machine vision technology is still in its initial stage in China; compared with the developed country it also has much disparity. Studying the intelligent inspector based on multi-sensor fusion not only conforms to the market demand of this kind of large-type intelligence inspector, but also is advantageous to the promotion of the research and application of the machine vision technology.
Based on the studies of the related data from at home and abroad, this article presents the system design plan for the intelligent inspector based on multi-sensor fusion, and in detail introduces the structures and the function of each part. Multi-layer control system is used in the electric control system of the intelligent inspector based on multi-sensor fusion. The host computer is responsible for higher layer management and communication, while PLC is responsible for lower layer control. In order to separate goods from the high-speed production line steadily, one kind of new ejector is developed. The ejector adopts the method that hits goods with a ladder-type stroke. Thus, the ejected object is not easy to turn over even if it is on the high- speed movement.
This article emphasizes on elaborating the design of the machine vision system in the intelligent inspector based on multi-sensor fusion. Specifically, it discusses the selection principle of the camera and the image capture card, and determines the use of intelligence inspector's cameras and image capture card. Since the light source and the illumination plan is of a vital significance to the machine vision system, this article designs the system illumination plan according to the actual demands, thus ensuring the image quality gained by the machine vision system. This text has also discussed how to carry on the software development of the machine vision system.
The intelligent inspector based on multi-sensor fusion has the examination functions for bottle body, the bottle mouth, the bottle bottom and fluid position. To achieve these functions, with the consideration of the actual inspection features, this article puts forward to a set of practical examination algorithms on the basis of analyzing inspected targets. The operation indicates that, by using these examination algorithms in the intelligent inspector based on multi-sensor fusion, the examination speed can reach 10 bottles/second, and the average examination accurate rate surpasses 95%.
According to studies in this article, the experimental equipment has already been developed. The theory and the algorithms stated in this article have been put into use on the massive experiments on the inspector. The results of the experiments have proven the validity of the design and the findings. So, it has laid a good foundation for further research and development in the future.
引文
[1]马项德，张正友．计算机视觉—计算理论与算法基础．北京：科学出版社，1-18 1997
[2]Machine Vision. http://www.mellesgriot.com/products/machinevision/ machineoverview.asp, 2003-12-25
[3]Bruce Batchelor, Paul Whelan. Ethical Environmental and Social Issues of Machine Vision. http://bruce.cs.cf.ac.uk/bruce/General_items_folder/ ethics.html, 2003-12-25
[4]高文，陈熙霖．计算机视觉—算法与系统原理．北京：清华大学出版社，1999，1-20
[5]Roberts L. Machine perception of three-dimensional solids. In: Optical and Electron-optical Information Processing, Cambridge, MA: MIT Press, 1965, 159～197
[6]Huffman D A. Impossible objects as nonsense sentences. In: Machine Intelligence 6, 1971, New York: Elsevier, 295～323
[7]Clowes M B. On seeing things. Artificial Intelligence, 1971 , (2): 79-116,
[8]Marr D．视觉计算理论．姚国正．北京：科学出版社，1988，1-22
[9]Antti Soini. Machine vision technology take-up in industrial applications. In: Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, (ISPA 2001)., 2001.6:332-338
[10]Machine Vision is a distinct entity from Computer Vision. Image Processing, Artificial Intelligence and Pattern Recognition. http://bruce.cs.cf.ac.uk/bruce/ General_items_folder/MV_academic_subject.html, 2003-12-25
[11]Multi-Disciplinary Nature of Machine Vision. http://bruce.cs.cf.ac.uk/bruce/ General_items_folder/Multidisciplinary_nature.html, 2003-12-25
[12]鲁镇恶，谢勇．印刷品外观缺陷机器视觉的检测与识别．包装工程，2002，23(5)：10-11
[13]刘曙光，刘明远，何钺．基于小波分析的仪表板质量检测．信号处理，1999，15(3)：285-288
[14]卢朝阳，周幸妮，顾英．用图像识别的方法检测集成电路的键合点．自动化学报，1999，25(4)：567-570
[15]Bernard C. Jiang, Szu-Lang Tasi, and Chien-Chih Wang. Machine Vision-Based Gray Relational Theory Applied to IC Marking Inspection. IEEE
ON Semicondutor Manufacturing, 2002, 15(4): 531-539
[16]N.Aleixos, J.Blasco, E.Molto. Assessment of citrus fruit quality using a real-time machine vision system. In: International Conference on Pattern Recognition(ICPR'00), 2000, 1:482～485
[17]Processing Medical Images. http://www.bmva.ac.uk/apps/medical.html , 2003-12-25
[18]Pascal Bamford, Brian Lovell. Method for Accurate Unsupervised Cell Nucleus Segmentation. IN: Proceedings of IEEE Engineering in Medicine and Biology, Istanbul, 2001, 10:2704-2708
[19]Vision for Surveillance. http://www.bmva.ac.uk/apps/surveillance.html , 2003-12-25
[20]A.M.Agzamov, V.V.Kulakovskij. TV Space Surveillance System. IEEE AES System Magazine, 2003, 9:17-20
[21]周小四，杨杰，朱一坦．用于监控智能报警系统的图像识别技术．上海交通大学学报，2002，36(4)：498-501
[22]熊志勇，沈理．基于双属性图表示的通用人脸图像识别系统．计算机学报，2001，24(7)：764-769
[23]Phil Harman. Home based 3D entertainment-an overview. IN: Proceedings of International Conference on Image Processing, 2000 : 1-4
[24]J. Konrad. View reconstruction for 3-D video entertainment: issues, algorithms and applications. In: Proc. Int. Conf. on Image Process. and its Applications 1999: 8-12,
[25]Masakazu Ejiri. Robotics and Machine-Vision for the Future-An Industrial View. In: International Conference on Advanced Intelligent Mechatronics Proceedings, Italy, 2001 : 917-922
[26]In-Sight Product Guide. www.cognex.com/pdf/download/ in-sight-brochure.pdf, 2003-12-25
[27]SIMATIC VS 710 Quick Reference Guide. http://www.ad.siemens.com.cn/ products/as/isensor/mv/download/vs710_4.pdf, 2003-12-25
[28]The Linear Empty Bottle Inspector. http://www.heuft.com/eng/ produkte/linear.htm, 2003-12-25
[29]Krones Linatronic 713-M2 Empty bottle inspector, http://www.krones.de/pdf/ linatronic-713-m2_e.pdf, 2003-12-26
[30]Krones Multitronic 750-M2 Empty bottle inspection machine http://www.krones.de/pdf/multitronic-750_e.pdf, 2003-12-25
[31]原魁，刘伟强．变频器基础及应用．北京：冶金工业出版社，1997，1-35
[32]Siemens Micromaster 420 使用大全. http://www.ad.siemens.com.cn/ download/manual/docu_pdf/sd/0210/119_MM420_manual.pdf, 2003-12-25
[33]Siemens标准驱动产品通讯手册 http://www.ad.siemens.com.cn，2003-12-26
[34]范逸之．Visual Basic与RS—232串行通讯控制．北京：中国青年出版社，2001，100-122
[35]贾志斌，景毅．变频器与计算机的通信在控制系统中的实现．机械管理开发，2002，5(SUM No．69)：1-3
[36]李壮举，刘万里，殷华文．计算机对变频器的通讯及监控．现代电子技术，2002，4(SUM No．135)：43-45
[37]弭洪涛．可编程序控制器(PLC)原理及应用．北京：中国水利水电出版社，1998，1-18
[38]FX1S, FX1N, FX2N, FX2NC Programing Manual. Mitsubishi Electric Corporation
[39]FX_Communction_Guid Mitsubishi Electric Corporation
[40]FX Serials Hardware Manual Mitsubishi Electric Corporation
[41]A Roadmap for Building a Machine Vision System http://www.imagenation.com/pdf/roadmap.pdf, 2003-12-26
[42]摄像机的选择和主要参数 http://www.bjtac.com/jscamery.htm, 2033-12-26
[43]High Speed, Real-Time Machine Vision. http://www.imagenation.com/ pdf/highspeed.pdf, 2003-12-27
[44]镜头选用方法．http://www.htgd.com/xyff.htm, 2003-12-27
[45]镜头的选择和主要参数．http://www.bjtac.com/jslens.htm, 2003-12-27
[46]TM-6703 Progressive Seandouble Speed Shutter Camera. http://www.ftk-image.com/webfiles/pdfs/TM_6703.pdf , 2003-12-27
[47]Choosing a Frame Grabber for Performance and Profitability http://www.imagenation.com/pdf/choose.pdf, 2003--12-27
[48]Matrox Corona-Ⅱ Installation and Hardware Reference Manual. http://www.matrox.com/imaging/products/corona2/corona2_inst_hw_manual.pdf, 2003-12-27
[49]Kane，JS贡树行．光学设计是机器视觉系统的关键．红外，1999，(8)：37-39．
[50]LED光源产品应用实例 http://www.daheng-image.com/indexc.htm,2003-12-27
[51]Common Lighting Techniques. http://www.advancedillumination.com/ category/primer_3.html, 2003-12-27
[51]Common Lighting Techniques. http://www.advancedillumination.com/ category/primer_3.html, 2003-12-27
[52]LED灯及其发光原理．http://www.songye.cc/html/led/fgyl.htm, 2003-12-27
[53]LED Advantages. http://www.advancedillumination.com/category/ primer_2.html, 2003-12-28
[54]何斌，马天予，王运坚等．Visual C++数字图像处理．北京：人民邮电出版社，2001，1-20
[55]陈惟斌，张军．Visual Basic 6.0开发指南．北京：清华大学出版社，2001，100-136
[56]张昆深，晓黄．API for Windows98／2000详解．北京：清华大学出版社，2001，272-303
[57]夏良正．数字图像处理．南京：东南大学出版社，1999，193-240
[58]L.G. Roberts. Machine perception of three-dimensional solids. In: Optical and Electro-Optical Information Processing Tippett J.T., et al. (Eds.), MIT Press, Cambridge, Mass, 1965:159-197
[59]J.F. Canny. Finding edges and lines in images. Technical Report AI-TR-720 MIT. AI Lab, 1983
[60]J. F. Canny. A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, 8(6):112-131.
[61]Todd Law, Hidenori Itoh, Hirohisa Seki. Image Filtering Edge Detection and Edge Tracing Using Fuzzy Reasoning. IEEE Trans on Pattern Analysis and Machine Intelligence, 1996, 18(5): 481-491
[62]郑小松，刘谋政．数字图像边缘检测的模糊聚类研究．同济大学学报．1999，27(3)：337-341．
[63]熊联欢，胡汉平．用BP网络进行彩色图像分割和边缘检测．华中理工大学学报，1999，27(2)：87-89．
[64]T. Aydin, Y. Yemez, B. Sankur, E. Anarim. Edge Detection by M- Band Wavelet Transform In: 8'th IEEE Conf. on Multidimensional Signal Processing, France, 1993: 192- 193
[65]D.J. Hebert and H. Kim. A Fast Wavelet Compass Edge Detector. In: Wavelet Applications in Signal and Image Processing 5th , Proc. SPIE, 1996, 2825: 432-442.
[66]N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. System, Man Cybernetics, 1979, 9(1): 62—66
[67]N. Ramesh, J. H. Yoo, I. K. Sethi. Thresholding based on histogram approximations, IEE Proceedings Vision, Image and Signal Processing, 1995,
142:271-279
[68]A. Beghdadi, AL Negrate, PV DeLesegno. Entropic Thresholding Using A Block Source Model.Graphical Models and Image Processing, 1995, 57: 197-205
[69]C A Glasbey. An analysis of histogram-based thresholding algorithms. CVGIP-Graphical Models and Image Processing, 1993, 55:532-537
[70]韩思奇，王蕾．图像分割的阈值法综述．系统工程与电子技术，2002，24(6)：91-94
[71]D.H. Ballard. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 1981, 13:111-122
[72]L. Xu, E. Oja. Randomized Hough transform(RHT): Basic mechanisms, algorithms, and computational complexities. CVGIP-Image Understanding, 1993, 57:131-154
[73]陈燕新，戚飞虎．基于随机Hough变换的快速圆检测方法．上海交通大学学报，1998，32(10)：17-20
[74]CF Neveu, CR Dyer, RT Chin. Two-dimensional object recognition using multiresolution models. Computer Vision, Graphics, and Image Processing, 1986, 34(1): 52-65
[75]陈璐云，李玉和，李庆祥等．分层迭代Hough变换法检测微器件圆形轮廓．清华大学学报(自然科学版)，2002，42(6)：795-798
[76]S. Olariu, J.L. Schwing, and J. Zhang. Computing the Hough Transform on Reconfigurable Meshes. Image and Vision Computing Journal, 1993, 11(10): 623-628.
[77]陈戍，张延忻，郭鹏毅，盛积德．实现Hough变换的一种光学方法．激光杂志，2000，21(3)：65-66
[78]袁曾任．人工神经元网络及其应用．北京：清华大学出版社，1999，1-17
[79]王耀南．计算智能信息处理技术及其应用．长沙：湖南大学出版社，1999，90-176
[80]K. Shiranita, K. Hayashi, A. Otsubo, T. Miyajima, R. Takiyama. Determination of meat quality by image processing and neural network techniques. In: Fuzzy Systems FUZZ IEEE The Ninth IEEE International Conference, 2000, 2:989 -992
[81]Kuk Won Ko, Young Jun Roh, Hyung Suck Cho, Hyung Cheol Kimn. A neural network approach to the inspection of ball grid array solder joints on printed circuit boards. In: Neural Networks, 2000. IJCNN International Joint Conference on Proceedings of the IEEE-INNS-ENNS, 2000, 5:233 -238
[82]蔡俊，任德官．基于BP神经网络的指纹模板分类器分类算法．微电子学与计算机，2002，9：1-3
[2]Machine Vision. http://www.mellesgriot.com/products/machinevision/ machineoverview.asp, 2003-12-25
[3]Bruce Batchelor, Paul Whelan. Ethical Environmental and Social Issues of Machine Vision. http://bruce.cs.cf.ac.uk/bruce/General_items_folder/ ethics.html, 2003-12-25
[4]高文，陈熙霖．计算机视觉—算法与系统原理．北京：清华大学出版社，1999，1-20
[5]Roberts L. Machine perception of three-dimensional solids. In: Optical and Electron-optical Information Processing, Cambridge, MA: MIT Press, 1965, 159～197
[6]Huffman D A. Impossible objects as nonsense sentences. In: Machine Intelligence 6, 1971, New York: Elsevier, 295～323
[7]Clowes M B. On seeing things. Artificial Intelligence, 1971 , (2): 79-116,
[8]Marr D．视觉计算理论．姚国正．北京：科学出版社，1988，1-22
[9]Antti Soini. Machine vision technology take-up in industrial applications. In: Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis, (ISPA 2001)., 2001.6:332-338
[10]Machine Vision is a distinct entity from Computer Vision. Image Processing, Artificial Intelligence and Pattern Recognition. http://bruce.cs.cf.ac.uk/bruce/ General_items_folder/MV_academic_subject.html, 2003-12-25
[11]Multi-Disciplinary Nature of Machine Vision. http://bruce.cs.cf.ac.uk/bruce/ General_items_folder/Multidisciplinary_nature.html, 2003-12-25
[12]鲁镇恶，谢勇．印刷品外观缺陷机器视觉的检测与识别．包装工程，2002，23(5)：10-11
[13]刘曙光，刘明远，何钺．基于小波分析的仪表板质量检测．信号处理，1999，15(3)：285-288
[14]卢朝阳，周幸妮，顾英．用图像识别的方法检测集成电路的键合点．自动化学报，1999，25(4)：567-570
[15]Bernard C. Jiang, Szu-Lang Tasi, and Chien-Chih Wang. Machine Vision-Based Gray Relational Theory Applied to IC Marking Inspection. IEEE
ON Semicondutor Manufacturing, 2002, 15(4): 531-539
[16]N.Aleixos, J.Blasco, E.Molto. Assessment of citrus fruit quality using a real-time machine vision system. In: International Conference on Pattern Recognition(ICPR'00), 2000, 1:482～485
[17]Processing Medical Images. http://www.bmva.ac.uk/apps/medical.html , 2003-12-25
[18]Pascal Bamford, Brian Lovell. Method for Accurate Unsupervised Cell Nucleus Segmentation. IN: Proceedings of IEEE Engineering in Medicine and Biology, Istanbul, 2001, 10:2704-2708
[19]Vision for Surveillance. http://www.bmva.ac.uk/apps/surveillance.html , 2003-12-25
[20]A.M.Agzamov, V.V.Kulakovskij. TV Space Surveillance System. IEEE AES System Magazine, 2003, 9:17-20
[21]周小四，杨杰，朱一坦．用于监控智能报警系统的图像识别技术．上海交通大学学报，2002，36(4)：498-501
[22]熊志勇，沈理．基于双属性图表示的通用人脸图像识别系统．计算机学报，2001，24(7)：764-769
[23]Phil Harman. Home based 3D entertainment-an overview. IN: Proceedings of International Conference on Image Processing, 2000 : 1-4
[24]J. Konrad. View reconstruction for 3-D video entertainment: issues, algorithms and applications. In: Proc. Int. Conf. on Image Process. and its Applications 1999: 8-12,
[25]Masakazu Ejiri. Robotics and Machine-Vision for the Future-An Industrial View. In: International Conference on Advanced Intelligent Mechatronics Proceedings, Italy, 2001 : 917-922
[26]In-Sight Product Guide. www.cognex.com/pdf/download/ in-sight-brochure.pdf, 2003-12-25
[27]SIMATIC VS 710 Quick Reference Guide. http://www.ad.siemens.com.cn/ products/as/isensor/mv/download/vs710_4.pdf, 2003-12-25
[28]The Linear Empty Bottle Inspector. http://www.heuft.com/eng/ produkte/linear.htm, 2003-12-25
[29]Krones Linatronic 713-M2 Empty bottle inspector, http://www.krones.de/pdf/ linatronic-713-m2_e.pdf, 2003-12-26
[30]Krones Multitronic 750-M2 Empty bottle inspection machine http://www.krones.de/pdf/multitronic-750_e.pdf, 2003-12-25
[31]原魁，刘伟强．变频器基础及应用．北京：冶金工业出版社，1997，1-35
[32]Siemens Micromaster 420 使用大全. http://www.ad.siemens.com.cn/ download/manual/docu_pdf/sd/0210/119_MM420_manual.pdf, 2003-12-25
[33]Siemens标准驱动产品通讯手册 http://www.ad.siemens.com.cn，2003-12-26
[34]范逸之．Visual Basic与RS—232串行通讯控制．北京：中国青年出版社，2001，100-122
[35]贾志斌，景毅．变频器与计算机的通信在控制系统中的实现．机械管理开发，2002，5(SUM No．69)：1-3
[36]李壮举，刘万里，殷华文．计算机对变频器的通讯及监控．现代电子技术，2002，4(SUM No．135)：43-45
[37]弭洪涛．可编程序控制器(PLC)原理及应用．北京：中国水利水电出版社，1998，1-18
[38]FX1S, FX1N, FX2N, FX2NC Programing Manual. Mitsubishi Electric Corporation
[39]FX_Communction_Guid Mitsubishi Electric Corporation
[40]FX Serials Hardware Manual Mitsubishi Electric Corporation
[41]A Roadmap for Building a Machine Vision System http://www.imagenation.com/pdf/roadmap.pdf, 2003-12-26
[42]摄像机的选择和主要参数 http://www.bjtac.com/jscamery.htm, 2033-12-26
[43]High Speed, Real-Time Machine Vision. http://www.imagenation.com/ pdf/highspeed.pdf, 2003-12-27
[44]镜头选用方法．http://www.htgd.com/xyff.htm, 2003-12-27
[45]镜头的选择和主要参数．http://www.bjtac.com/jslens.htm, 2003-12-27
[46]TM-6703 Progressive Seandouble Speed Shutter Camera. http://www.ftk-image.com/webfiles/pdfs/TM_6703.pdf , 2003-12-27
[47]Choosing a Frame Grabber for Performance and Profitability http://www.imagenation.com/pdf/choose.pdf, 2003--12-27
[48]Matrox Corona-Ⅱ Installation and Hardware Reference Manual. http://www.matrox.com/imaging/products/corona2/corona2_inst_hw_manual.pdf, 2003-12-27
[49]Kane，JS贡树行．光学设计是机器视觉系统的关键．红外，1999，(8)：37-39．
[50]LED光源产品应用实例 http://www.daheng-image.com/indexc.htm,2003-12-27
[51]Common Lighting Techniques. http://www.advancedillumination.com/ category/primer_3.html, 2003-12-27
[51]Common Lighting Techniques. http://www.advancedillumination.com/ category/primer_3.html, 2003-12-27
[52]LED灯及其发光原理．http://www.songye.cc/html/led/fgyl.htm, 2003-12-27
[53]LED Advantages. http://www.advancedillumination.com/category/ primer_2.html, 2003-12-28
[54]何斌，马天予，王运坚等．Visual C++数字图像处理．北京：人民邮电出版社，2001，1-20
[55]陈惟斌，张军．Visual Basic 6.0开发指南．北京：清华大学出版社，2001，100-136
[56]张昆深，晓黄．API for Windows98／2000详解．北京：清华大学出版社，2001，272-303
[57]夏良正．数字图像处理．南京：东南大学出版社，1999，193-240
[58]L.G. Roberts. Machine perception of three-dimensional solids. In: Optical and Electro-Optical Information Processing Tippett J.T., et al. (Eds.), MIT Press, Cambridge, Mass, 1965:159-197
[59]J.F. Canny. Finding edges and lines in images. Technical Report AI-TR-720 MIT. AI Lab, 1983
[60]J. F. Canny. A computational approach to edge detection. IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, 8(6):112-131.
[61]Todd Law, Hidenori Itoh, Hirohisa Seki. Image Filtering Edge Detection and Edge Tracing Using Fuzzy Reasoning. IEEE Trans on Pattern Analysis and Machine Intelligence, 1996, 18(5): 481-491
[62]郑小松，刘谋政．数字图像边缘检测的模糊聚类研究．同济大学学报．1999，27(3)：337-341．
[63]熊联欢，胡汉平．用BP网络进行彩色图像分割和边缘检测．华中理工大学学报，1999，27(2)：87-89．
[64]T. Aydin, Y. Yemez, B. Sankur, E. Anarim. Edge Detection by M- Band Wavelet Transform In: 8'th IEEE Conf. on Multidimensional Signal Processing, France, 1993: 192- 193
[65]D.J. Hebert and H. Kim. A Fast Wavelet Compass Edge Detector. In: Wavelet Applications in Signal and Image Processing 5th , Proc. SPIE, 1996, 2825: 432-442.
[66]N. Otsu. A threshold selection method from gray-level histograms. IEEE Trans. System, Man Cybernetics, 1979, 9(1): 62—66
[67]N. Ramesh, J. H. Yoo, I. K. Sethi. Thresholding based on histogram approximations, IEE Proceedings Vision, Image and Signal Processing, 1995,
142:271-279
[68]A. Beghdadi, AL Negrate, PV DeLesegno. Entropic Thresholding Using A Block Source Model.Graphical Models and Image Processing, 1995, 57: 197-205
[69]C A Glasbey. An analysis of histogram-based thresholding algorithms. CVGIP-Graphical Models and Image Processing, 1993, 55:532-537
[70]韩思奇，王蕾．图像分割的阈值法综述．系统工程与电子技术，2002，24(6)：91-94
[71]D.H. Ballard. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 1981, 13:111-122
[72]L. Xu, E. Oja. Randomized Hough transform(RHT): Basic mechanisms, algorithms, and computational complexities. CVGIP-Image Understanding, 1993, 57:131-154
[73]陈燕新，戚飞虎．基于随机Hough变换的快速圆检测方法．上海交通大学学报，1998，32(10)：17-20
[74]CF Neveu, CR Dyer, RT Chin. Two-dimensional object recognition using multiresolution models. Computer Vision, Graphics, and Image Processing, 1986, 34(1): 52-65
[75]陈璐云，李玉和，李庆祥等．分层迭代Hough变换法检测微器件圆形轮廓．清华大学学报(自然科学版)，2002，42(6)：795-798
[76]S. Olariu, J.L. Schwing, and J. Zhang. Computing the Hough Transform on Reconfigurable Meshes. Image and Vision Computing Journal, 1993, 11(10): 623-628.
[77]陈戍，张延忻，郭鹏毅，盛积德．实现Hough变换的一种光学方法．激光杂志，2000，21(3)：65-66
[78]袁曾任．人工神经元网络及其应用．北京：清华大学出版社，1999，1-17
[79]王耀南．计算智能信息处理技术及其应用．长沙：湖南大学出版社，1999，90-176
[80]K. Shiranita, K. Hayashi, A. Otsubo, T. Miyajima, R. Takiyama. Determination of meat quality by image processing and neural network techniques. In: Fuzzy Systems FUZZ IEEE The Ninth IEEE International Conference, 2000, 2:989 -992
[81]Kuk Won Ko, Young Jun Roh, Hyung Suck Cho, Hyung Cheol Kimn. A neural network approach to the inspection of ball grid array solder joints on printed circuit boards. In: Neural Networks, 2000. IJCNN International Joint Conference on Proceedings of the IEEE-INNS-ENNS, 2000, 5:233 -238
[82]蔡俊，任德官．基于BP神经网络的指纹模板分类器分类算法．微电子学与计算机，2002，9：1-3
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |