基于机器视觉的智能空瓶检测机器人研究
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摘要
基于机器视觉的智能空瓶检测机器人以其高速度、高精度、智能自动化的特点成为了当今高速啤酒生产线上必不可少的高科技设备之一。而我国机器视觉的研究和应用还处于起步阶段，对智能空瓶检测机器人的研究几乎是空白，远远落后于国外发达国家的水平。我国啤酒生产的大规模发展，急需国人的努力来填补这一空白。为此，本文对智能空瓶检测机器人进行了深入、系统的研究。
论文综述了当今世界机器视觉技术的发展与应用现状，介绍了与空瓶检测相关的重要机器视觉技术，分析了空瓶检测的技术特点和难点，提出了直线式和旋转式两种机器人的传动结构，设计了智能空瓶检测机器人的机械和电气控制方案，深入研究了多种数字图像检测算法，研制了智能空瓶检测机器人实验系统，完成了所有机械、电气控制部分和软件的设计、安装和调试。
主要工作包括以下几方面：
1、对啤酒行业中空瓶检测的生产需求和工艺流程的要求进行了完整而详细的描述，并在此基础上，通过理论分析和实验总结了机器视觉应用与空瓶检测的特点和难点，得到整个智能空瓶检测机器人的技术需求，提出了智能空瓶检测机器人是集光、机、电一体化的高度综合复杂的工业机器视觉应用系统。
2、在总结了目前与空瓶检测机器人相关的机器视觉技术和国外相关成果的基础上，结合空瓶检测的实际需求，提出了智能空瓶检测机器人系统的整体方案。在机械结构方面，提出直线式和旋转式两种机械传动结构，并详细介绍了直线式空瓶检测机器人的机械布局以及光学成像系统和照明方案。对于系统的电气控制部分，本文提出了基于PC机、基于DSP和基于视觉传感器的三种结构，分析了三种结构的特点，并设计了系统的方案。
3、对数字图像处理技术在空瓶检测中的应用进行了详细地研究，并做了大量的实验。针对空瓶检测的高速、高精度、实时性特点，在总结分析传统数字图像边缘检测技术基础上，提出了用于瓶底、瓶口、瓶身检测的实用算法，并通过试验验证了算法的有效性。
4、对智能信息处理技术在空瓶检测中的应用进行了分析研究和试验。将模糊信息处理技术和神经网络技术用于图像检测，将多传感器信息融合技术用于提高CCD摄像机的动态范围。通过实验发现用模糊信息处理技术进行边缘检测具有可行性，但效果一般，没有明显优越性。而BP神经网络在图像检测中效果较好，有一定的优点，但计算耗时多，在基于PC机的应用系统中，实现高速度的实时检测比较困难，必须借助专用运算器。而采用多传感器图像融合算法，能大幅度地提高CCD摄像机的动态范围。
5、研制开发了智能空瓶检测机器人实验系统，完成了整个系统的机械、电气控制部分的设计、安装和调试，用VC6.0和VB6.0开发了基于PC机的一整套测试和研究的软件平台，建立了空瓶图像数据库，完成了本文所有算法的测试、研究工作，为智能空瓶检测机器人的进一步研究奠定了很好的基础。本文还针对智能空瓶检测机器人实验系统的不足，
拍要
提出了新一代开放型机器视觉实验系统的设计方案。
通过理论分析研究和大量实验证明了本文所提出的针对空瓶瓶口、瓶底、瓶身的数字
图像检测算法等有效性和可行性，解决了空瓶检测机器人应用中的大部分问题，为进一步
的研究开发奠定了4for的基础。
Characterized with auto inspecting ability and high inspecting speed and accuracy, intelligent empty bottle inspector (IEBI) is one of the most necessary high technology equipments in high-speed beer production. The research and application of machine vision have been introduced to our country just several years before. Compared with developed country, we achieve fairly limited accomplishment in the research on EBI. At present, ft is necessary for us to develop EBI due to the large and increasing beer production scale in our country. Under such situation, this paper put forward a deeply and systematically research on IEBI.
This paper gives an overview of current machine vision and introduces the key technologies applied in EBI. Based on the analysis of characteristics and difficulties in the realization of EBI, this paper presents line and rotary transfer structures, the mechanical and electrical system scheme of EBI. This paper focuses on the application of digital image processing technology to the defect detection in empty bottle inspection. An experimental equipment including all mechanical, electrical instruments and software is developed. The main contributions of this paper are the followings:
l.An explicit description of empty bottle inspector in the beer production is givea Based on theoretically analysis and experiments, this paper concludes the characteristics and difficulties in the application of machine vision to empty bottle inspection and technology requirements for EBI. It is emphasized that EBI is an integration of optical, mechanical, electrical and digital image processing technology.
2. According to the technology requirement of EBI and referring to the current advanced achievements, a scheme of EBI is proposed. The line and rotary transfer mechanical system are suggested. This paper introduces line empty bottle inspector in its mechanical structure, optical and lighting system. Three type of electric control system are discussed in this paper which are PC-based, DSP-based and vision sensor-based respectively.
3. This paper concerns on the application of digital image processing technology to the detection of defect in empty bottle inspection. After concluding the traditional edge detection method, several
novel and useful algorithms for the inspection of empty bottle bottom, finish and wall are put forward. Experiments have proved their efficiency and feasibility.
4. This paper makes an attempt to using intelligent information processing technologies for empty bottle inspection. Fuzzy information processing, BP neural network and multi-sense information fusion are studied. The results of experiments show that Fuzzy technology is not better than traditional processing method in edge detection while BP neural network better but is time consumption. Multi-sense information technology succeeds to improve the dynamic scale of CCD camera, which is useful in empty inspection.
5.An experimental EBI equipment is developed, which is a useful tool including the whole mechanical, electrical system and software developed with VC6.0 and VB6.0. By the aid of this experimental equipment, an empty bottle image database is built and all algorithms for inspection are tested. Another advanced experimental equipment is devised, which is more capable in various machine-vision experiments.
Theoretical study and practical experiments have proved the efficiency and feasibility of the
algorithm for inspection provided in this paper. Further study is bond to benefit from the
achievements of this paper.
论文综述了当今世界机器视觉技术的发展与应用现状，介绍了与空瓶检测相关的重要机器视觉技术，分析了空瓶检测的技术特点和难点，提出了直线式和旋转式两种机器人的传动结构，设计了智能空瓶检测机器人的机械和电气控制方案，深入研究了多种数字图像检测算法，研制了智能空瓶检测机器人实验系统，完成了所有机械、电气控制部分和软件的设计、安装和调试。
主要工作包括以下几方面：
1、对啤酒行业中空瓶检测的生产需求和工艺流程的要求进行了完整而详细的描述，并在此基础上，通过理论分析和实验总结了机器视觉应用与空瓶检测的特点和难点，得到整个智能空瓶检测机器人的技术需求，提出了智能空瓶检测机器人是集光、机、电一体化的高度综合复杂的工业机器视觉应用系统。
2、在总结了目前与空瓶检测机器人相关的机器视觉技术和国外相关成果的基础上，结合空瓶检测的实际需求，提出了智能空瓶检测机器人系统的整体方案。在机械结构方面，提出直线式和旋转式两种机械传动结构，并详细介绍了直线式空瓶检测机器人的机械布局以及光学成像系统和照明方案。对于系统的电气控制部分，本文提出了基于PC机、基于DSP和基于视觉传感器的三种结构，分析了三种结构的特点，并设计了系统的方案。
3、对数字图像处理技术在空瓶检测中的应用进行了详细地研究，并做了大量的实验。针对空瓶检测的高速、高精度、实时性特点，在总结分析传统数字图像边缘检测技术基础上，提出了用于瓶底、瓶口、瓶身检测的实用算法，并通过试验验证了算法的有效性。
4、对智能信息处理技术在空瓶检测中的应用进行了分析研究和试验。将模糊信息处理技术和神经网络技术用于图像检测，将多传感器信息融合技术用于提高CCD摄像机的动态范围。通过实验发现用模糊信息处理技术进行边缘检测具有可行性，但效果一般，没有明显优越性。而BP神经网络在图像检测中效果较好，有一定的优点，但计算耗时多，在基于PC机的应用系统中，实现高速度的实时检测比较困难，必须借助专用运算器。而采用多传感器图像融合算法，能大幅度地提高CCD摄像机的动态范围。
5、研制开发了智能空瓶检测机器人实验系统，完成了整个系统的机械、电气控制部分的设计、安装和调试，用VC6.0和VB6.0开发了基于PC机的一整套测试和研究的软件平台，建立了空瓶图像数据库，完成了本文所有算法的测试、研究工作，为智能空瓶检测机器人的进一步研究奠定了很好的基础。本文还针对智能空瓶检测机器人实验系统的不足，
拍要
提出了新一代开放型机器视觉实验系统的设计方案。
通过理论分析研究和大量实验证明了本文所提出的针对空瓶瓶口、瓶底、瓶身的数字
图像检测算法等有效性和可行性，解决了空瓶检测机器人应用中的大部分问题，为进一步
的研究开发奠定了4for的基础。
Characterized with auto inspecting ability and high inspecting speed and accuracy, intelligent empty bottle inspector (IEBI) is one of the most necessary high technology equipments in high-speed beer production. The research and application of machine vision have been introduced to our country just several years before. Compared with developed country, we achieve fairly limited accomplishment in the research on EBI. At present, ft is necessary for us to develop EBI due to the large and increasing beer production scale in our country. Under such situation, this paper put forward a deeply and systematically research on IEBI.
This paper gives an overview of current machine vision and introduces the key technologies applied in EBI. Based on the analysis of characteristics and difficulties in the realization of EBI, this paper presents line and rotary transfer structures, the mechanical and electrical system scheme of EBI. This paper focuses on the application of digital image processing technology to the defect detection in empty bottle inspection. An experimental equipment including all mechanical, electrical instruments and software is developed. The main contributions of this paper are the followings:
l.An explicit description of empty bottle inspector in the beer production is givea Based on theoretically analysis and experiments, this paper concludes the characteristics and difficulties in the application of machine vision to empty bottle inspection and technology requirements for EBI. It is emphasized that EBI is an integration of optical, mechanical, electrical and digital image processing technology.
2. According to the technology requirement of EBI and referring to the current advanced achievements, a scheme of EBI is proposed. The line and rotary transfer mechanical system are suggested. This paper introduces line empty bottle inspector in its mechanical structure, optical and lighting system. Three type of electric control system are discussed in this paper which are PC-based, DSP-based and vision sensor-based respectively.
3. This paper concerns on the application of digital image processing technology to the detection of defect in empty bottle inspection. After concluding the traditional edge detection method, several
novel and useful algorithms for the inspection of empty bottle bottom, finish and wall are put forward. Experiments have proved their efficiency and feasibility.
4. This paper makes an attempt to using intelligent information processing technologies for empty bottle inspection. Fuzzy information processing, BP neural network and multi-sense information fusion are studied. The results of experiments show that Fuzzy technology is not better than traditional processing method in edge detection while BP neural network better but is time consumption. Multi-sense information technology succeeds to improve the dynamic scale of CCD camera, which is useful in empty inspection.
5.An experimental EBI equipment is developed, which is a useful tool including the whole mechanical, electrical system and software developed with VC6.0 and VB6.0. By the aid of this experimental equipment, an empty bottle image database is built and all algorithms for inspection are tested. Another advanced experimental equipment is devised, which is more capable in various machine-vision experiments.
Theoretical study and practical experiments have proved the efficiency and feasibility of the
algorithm for inspection provided in this paper. Further study is bond to benefit from the
achievements of this paper.
引文
[1]. An Overview of Machine Vision. http://www.age.uiuc.edu/age315/intro/overview, html
[2]. Bruce Batchelor, Paul Whelan. Ethical, Environmental and Social Issues of Machine Vision. http://bruce.cs.cf.ac.uk/bruce/Geneml_items_folder/ethics.html.
[3]．王红军．机器视觉—现代工业的眼睛．机电一体化，1999，5(3)：26-27．
[4]．戴君，赵海洋．机器视觉．机械设计与制造工程，1998，27(4)：-52-53．
[5]．贾云得．机器视觉．科学出版社．北京：2000．
[6]. 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
[7]. Multi-Disciplinary Nature of Machine Vision. http://bruce.cs.cf.ac.uk/bruce/General_items_folder/Multidisciplinary_nature.html
[8]. http://www.machinevisiononline.org
[9]. R.Allen Bums. MACHINE VISION LIGHTING TECHNIQUES FOR ELECTRONICS ASSEMBLY. http://www.machinevisiononline.org/public/articles/RVSI_LightingforMachineVision.pdf
[10]. MVA/SME Lighting and Optics Poster. http://www.sme.org/downloads/mva/mvaposter.pdf
[11]. The Benefits of LED Light Sources. http://www.machinevisiononline.org/public/articles/NER_TheBenefitsofLEDLightSources.pdf
[12]．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．
[13]. http://www.daheng-image.com
[14]. http://www.microview.com.cn
[15]．镜头的选择和主要参数．http://www.bjtac.com/jslens.htm
[16]．镜头选用方法．http://www.htgd.com/xyff.htm
[17]．摄像机的选择和主要参数．http://www.bjtac.com/jscamery.htm
[18]. TM-6703 PROGRESSIVE SCANDOUBLE SPEED SHUTTER CAMERA. http://www.ftk-image.com/webfiles./pdfs/TM_6703.pdf
[19]. Camera Interface Guide. http://www.matrox.com/imaging/products/camera_guide.pdf
[20]. Matrox Corona-Ⅱ. http://www.matrox.com/imaging/products/corona2/home.cfm
[21]. Peter McHugh. Vision Sensors Combine High-Speed Pattern Match Algorithm with Compact Optics and Lighting. http://www.machinevisiononline.org/public/articles/Peter_McHugh.PDF
[22]. 机器视觉系统应用．http://www.stone-electric.com/chenggonganli/shijuyingyong.htm
[23]. Vision for Autonomous Vehicles. http://www.bmva.ac.uk/apps/av.html
[24]. Vision and the Human Face. http://www.bmva.ac.uk/apps/faces.html
[25]. Industrial Inspection. http://www.bmva.ac.uk/apps/inspection.html
[26]. Processing Medical Images. http://www.bmva.ac.uk/apps/medical.html
[27]. Vision and Remote Sensing. http://www.bmva.ac.uk/apps/rs.html
[28]. Vision for Surveillance. http://www.bmva.ac.uk/apps/surveillance.html
[29]. Vision in Transport. http://www.bmva.ac.uk/apps/transport.html
[30]. http://www.cs.columbia.edu/robotics/
[31]．刘曙光，刘明．远机器视觉及其应用．机械制造．2000，38(7)：20-22．
[32]．金隼，洪海涛机．器视觉检测在电子接插件制造工业中的应用．上海交通大学仪表技术与传感器．2000，(2)：-13-16
[33]. The Linear Empty Bottle Inspector. http://www.heuft.com/eng/produkte/linear.htm
[34]. LINATRCNIC 713 M1 In-line Empty Bottle Inspector. http://www.krones.de/pdf/713ml_e.pdf
[35]. SPECTROSCAN Empty Bottle Inspection System. http://www.filtec.com/pdf/spectroscan.pdf
[36]．任谋．玻璃空瓶检测技术与应用概念．中国食品工业．1999，(2)：42-42
[37]．毛建旭．微机遥感图象处理系统的应用研究．华东地质学院硕士学位论文，1999年4月
[38]．王耀南．计算智能信息处理技术及其应用．湖南大学出版社．湖南长沙：1999
[39]．方如明，蔡健荣，许俐．计算机图像处理技术及其在农业工程中的应用．清华大学出版社．北京：1999年7月第一版．
[40]．王耀南，李树涛，毛建旭．计算机图像处理与识别技术．高等教育出版社．北京：2001年6月第一版．
[41]．刘强，陈吉红．基于传感器信息融合的机器视觉检测系统．华中理工大学学报 1997，25(3)：-5-7．
[42]．王搏，潘泉．图象平滑与边缘检测的模糊向量描述．小型微型计算机系统．．1999，20(3)：218-221．
[43]．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．
[44]. Todd law, Hidenori Itoh, Hirohisa Seki, Image Filtering Edge Detection and Edge Tracing Using Fuzzy Reasoning IEEE Trans on PA&MI, 1996,Vol.18,No.5,PP.481-491
[45]．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．
[46]．栾新，朱铁一．不规则类圆形目标图象识别新策略．中国图像图形学报．1999，4(3)：202-206．
[47]．王金鹤，夏晓东．扫描图象的圆弧的定位识别算法．中国图像图形学报．1999，4(6)：-497-501．
[48]．吴剑锋，林强．一种图像边缘检测的新算法．福州大学学报：自科版．2000，28(4)：-26-28．
[49]．杨伦标，高英仪．模糊数学原理及应用．华南理工大学出版社．广州：1998年7月第二版．
[50]．胡守仁主编神经网络丛书．1：神经网络导论．国防科技大学出版社．长沙：1993．10
[51]．胡守仁主编：戴葵编著神经网络丛书．2：神经网络实现技术．国防科技大学出版社，长沙：1998．7．
[52]．胡江华，柏连发，张保民．象素级多传感器图像融合技术．南京理工大学学报，1996，20(5)：453-456．
[53]．崔岩梅，倪国强，王毅．图像融合算法在高速DSP中的实现．激光与光电子学进展1999，(S1)：192-195．
[54]. Ehlers M. Multi-sensor image fusion technique in remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 1991, 46:19-30.
[55]．郭文阁，姚胜利，高风，高应俊，刘德森．CCD图像象采集处理系统性能评价．光子学报，1998，27(9)期：851-854．
[56]. DUAN Feng, WANG Yaonan, DUAN Wei, DUAN Zhenghua. Super Dynamic CCD Camera Based On Mutil-sensor Image Fusion. The 4th World Congress on Intelligent Control and Automation, 2OO2,6, Shanghai, China. Accepted.
[57]．崔屹．数字图象处理技术与应用．电子工业出版社．北京：1997
[58]．王煦法等．C语言图象处理程序设计．中国科学技术大学出版社．合肥：1994．1
[59]．黄智．图象处理与识别实用程序．天津科学技术出版社．天津：1989．
[60]．清宏计算机工作室．Visual Basic编程技巧(多媒体与系统篇)．机械工业出版社 北京：2001
[61]．李兰友，万振凯．Visual Basic 6图像处理开发与应用实例．电子工业出版社．北京：2000
[62]．李玉东，李罡，李雷．Visual Basic 6.0中文版控件大全．电子工业出版社 北京：2000
[63]．陈永利、赵霞、陈利军．PLC在机床电气传动系统中的应用．微计算机信息，1999，6
[64]．S7-200可编程控制器系统手册．西门子有限公司
[65]．新编Windows API参考大全[M]．本书编写组编著．北京：电子工业出版社，2000
[2]. Bruce Batchelor, Paul Whelan. Ethical, Environmental and Social Issues of Machine Vision. http://bruce.cs.cf.ac.uk/bruce/Geneml_items_folder/ethics.html.
[3]．王红军．机器视觉—现代工业的眼睛．机电一体化，1999，5(3)：26-27．
[4]．戴君，赵海洋．机器视觉．机械设计与制造工程，1998，27(4)：-52-53．
[5]．贾云得．机器视觉．科学出版社．北京：2000．
[6]. 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
[7]. Multi-Disciplinary Nature of Machine Vision. http://bruce.cs.cf.ac.uk/bruce/General_items_folder/Multidisciplinary_nature.html
[8]. http://www.machinevisiononline.org
[9]. R.Allen Bums. MACHINE VISION LIGHTING TECHNIQUES FOR ELECTRONICS ASSEMBLY. http://www.machinevisiononline.org/public/articles/RVSI_LightingforMachineVision.pdf
[10]. MVA/SME Lighting and Optics Poster. http://www.sme.org/downloads/mva/mvaposter.pdf
[11]. The Benefits of LED Light Sources. http://www.machinevisiononline.org/public/articles/NER_TheBenefitsofLEDLightSources.pdf
[12]．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．
[13]. http://www.daheng-image.com
[14]. http://www.microview.com.cn
[15]．镜头的选择和主要参数．http://www.bjtac.com/jslens.htm
[16]．镜头选用方法．http://www.htgd.com/xyff.htm
[17]．摄像机的选择和主要参数．http://www.bjtac.com/jscamery.htm
[18]. TM-6703 PROGRESSIVE SCANDOUBLE SPEED SHUTTER CAMERA. http://www.ftk-image.com/webfiles./pdfs/TM_6703.pdf
[19]. Camera Interface Guide. http://www.matrox.com/imaging/products/camera_guide.pdf
[20]. Matrox Corona-Ⅱ. http://www.matrox.com/imaging/products/corona2/home.cfm
[21]. Peter McHugh. Vision Sensors Combine High-Speed Pattern Match Algorithm with Compact Optics and Lighting. http://www.machinevisiononline.org/public/articles/Peter_McHugh.PDF
[22]. 机器视觉系统应用．http://www.stone-electric.com/chenggonganli/shijuyingyong.htm
[23]. Vision for Autonomous Vehicles. http://www.bmva.ac.uk/apps/av.html
[24]. Vision and the Human Face. http://www.bmva.ac.uk/apps/faces.html
[25]. Industrial Inspection. http://www.bmva.ac.uk/apps/inspection.html
[26]. Processing Medical Images. http://www.bmva.ac.uk/apps/medical.html
[27]. Vision and Remote Sensing. http://www.bmva.ac.uk/apps/rs.html
[28]. Vision for Surveillance. http://www.bmva.ac.uk/apps/surveillance.html
[29]. Vision in Transport. http://www.bmva.ac.uk/apps/transport.html
[30]. http://www.cs.columbia.edu/robotics/
[31]．刘曙光，刘明．远机器视觉及其应用．机械制造．2000，38(7)：20-22．
[32]．金隼，洪海涛机．器视觉检测在电子接插件制造工业中的应用．上海交通大学仪表技术与传感器．2000，(2)：-13-16
[33]. The Linear Empty Bottle Inspector. http://www.heuft.com/eng/produkte/linear.htm
[34]. LINATRCNIC 713 M1 In-line Empty Bottle Inspector. http://www.krones.de/pdf/713ml_e.pdf
[35]. SPECTROSCAN Empty Bottle Inspection System. http://www.filtec.com/pdf/spectroscan.pdf
[36]．任谋．玻璃空瓶检测技术与应用概念．中国食品工业．1999，(2)：42-42
[37]．毛建旭．微机遥感图象处理系统的应用研究．华东地质学院硕士学位论文，1999年4月
[38]．王耀南．计算智能信息处理技术及其应用．湖南大学出版社．湖南长沙：1999
[39]．方如明，蔡健荣，许俐．计算机图像处理技术及其在农业工程中的应用．清华大学出版社．北京：1999年7月第一版．
[40]．王耀南，李树涛，毛建旭．计算机图像处理与识别技术．高等教育出版社．北京：2001年6月第一版．
[41]．刘强，陈吉红．基于传感器信息融合的机器视觉检测系统．华中理工大学学报 1997，25(3)：-5-7．
[42]．王搏，潘泉．图象平滑与边缘检测的模糊向量描述．小型微型计算机系统．．1999，20(3)：218-221．
[43]．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．
[44]. Todd law, Hidenori Itoh, Hirohisa Seki, Image Filtering Edge Detection and Edge Tracing Using Fuzzy Reasoning IEEE Trans on PA&MI, 1996,Vol.18,No.5,PP.481-491
[45]．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．
[46]．栾新，朱铁一．不规则类圆形目标图象识别新策略．中国图像图形学报．1999，4(3)：202-206．
[47]．王金鹤，夏晓东．扫描图象的圆弧的定位识别算法．中国图像图形学报．1999，4(6)：-497-501．
[48]．吴剑锋，林强．一种图像边缘检测的新算法．福州大学学报：自科版．2000，28(4)：-26-28．
[49]．杨伦标，高英仪．模糊数学原理及应用．华南理工大学出版社．广州：1998年7月第二版．
[50]．胡守仁主编神经网络丛书．1：神经网络导论．国防科技大学出版社．长沙：1993．10
[51]．胡守仁主编：戴葵编著神经网络丛书．2：神经网络实现技术．国防科技大学出版社，长沙：1998．7．
[52]．胡江华，柏连发，张保民．象素级多传感器图像融合技术．南京理工大学学报，1996，20(5)：453-456．
[53]．崔岩梅，倪国强，王毅．图像融合算法在高速DSP中的实现．激光与光电子学进展1999，(S1)：192-195．
[54]. Ehlers M. Multi-sensor image fusion technique in remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 1991, 46:19-30.
[55]．郭文阁，姚胜利，高风，高应俊，刘德森．CCD图像象采集处理系统性能评价．光子学报，1998，27(9)期：851-854．
[56]. DUAN Feng, WANG Yaonan, DUAN Wei, DUAN Zhenghua. Super Dynamic CCD Camera Based On Mutil-sensor Image Fusion. The 4th World Congress on Intelligent Control and Automation, 2OO2,6, Shanghai, China. Accepted.
[57]．崔屹．数字图象处理技术与应用．电子工业出版社．北京：1997
[58]．王煦法等．C语言图象处理程序设计．中国科学技术大学出版社．合肥：1994．1
[59]．黄智．图象处理与识别实用程序．天津科学技术出版社．天津：1989．
[60]．清宏计算机工作室．Visual Basic编程技巧(多媒体与系统篇)．机械工业出版社 北京：2001
[61]．李兰友，万振凯．Visual Basic 6图像处理开发与应用实例．电子工业出版社．北京：2000
[62]．李玉东，李罡，李雷．Visual Basic 6.0中文版控件大全．电子工业出版社 北京：2000
[63]．陈永利、赵霞、陈利军．PLC在机床电气传动系统中的应用．微计算机信息，1999，6
[64]．S7-200可编程控制器系统手册．西门子有限公司
[65]．新编Windows API参考大全[M]．本书编写组编著．北京：电子工业出版社，2000
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