可重构的机器视觉在线检测方法的研究
|
摘要
质量需求是现代企业的核心需求，产品质量的好坏直接关系着企业的市场竞争力和企业自身的存续。因此，将质量放在首位是目前所有企业的共同选择。质量控制与质量管理的方法与手段多种多样，传统检测与测量往往依赖于人工完成。
本文着眼于流水线上、不间断运动的连续物体视觉可识别量的在线检测。利用机器视觉替代人工检测，运用计算机及软件替代检测工人，通过可重构的视觉检测方法实现在不同被检对象上的快速反应与装备实现。
本文主要研究了可动态增减检测子系统的分布式网络拓扑，支撑采集控制硬件层、图像处理识别层、界面显示层、质量等级评价等多层面的视觉重构理论，建立了基于软件芯片的视觉重构体系，对重构流程、重构方案进行了设计，采用规范化、标准化的方式设计了各个层次间交互接口。
针对市场上不同厂商的数字图像获取设备，本文均根据自身产品而定制、通用性差的缺点进行了相关研究。首先，分析了不同数字图像获取标准的优劣，建立了异构硬件环境下的图像获取通用模型；接着，创建了通用图像获取接口，设计并完善了SDK，包括初始化函数、设置函数、获取函数、图像处理回调函数、存储函数、辅助函数在内的六大类函数的定义；最后，对函数接口及其组态设计进行了研究。
在图像获取的基础上，对图像处理环节进行了探讨。首先，分析了视觉检测的基本需求，梳理了视觉检测的基本流程；接着，根据视觉检测流程的主要需求，设计了机器视觉在线检测算法库，将常用的图像预处理、图像分割、图像复原、特征提取等算法进行了标准化、参数化设计，着重突出优化算法代码、执行效率、鲁棒性等问题；最后，建立并完善了一套基于配置信息的视觉检测运行环境，可实现检测算子的搜索、调用、重载等功能，有利于提高检测系统的柔性，加速针对不同检测对象的设备部署。
为实现图像特征的提取与识别，本文首先研究了常用特征描述方法，分析了其优缺点，并指出单一特征或过小规模特征集在可重构视觉检测方法中的局限性。其后，针对这一核心问题展开，设计了一套基于统计与时频联合的特征提取方法，建立了通用性机器视觉在线检测特征集，并利用该特征集对三种产品的图像图形学特征进行了描述。最后，尝试设计了基于遗传算法的特征解耦方法，并就其中关键技术进行了研究。
最后，利用可重构的机器视觉方法对粘扣带、导爆管、网孔织物外观质量视觉检测进行了研究，开发了具有自主知识产权的检测系统，验证了可重构的产品视觉检测方法的可行性和有效性。
Quality requirements is the core of modern enterprise, the product quality has a directrelationship with the market competitiveness of enterprises and the enterprises themselvessubsisting. Sothe enterprises give top priority to quality is a common choice. Now all thecompanies’ quality control and quality management methods are diverse. And traditionaldetection and measurement is often dependent on artificial completed.
This article focuses on assembly line continuous, uninterrupted movement object vision torecognize the amount of online detection. The alternative to manual inspection is machine vision,using computers and software to replace workers detection. And by using the reconfigurablevisual inspection of the implementation on different objects have been rapid response andequipment implementation.
Mainly studies distributed network topology dynamically increase or decrease the detectionsubsystem, support the collection and control hardware, image processing and recognition layer,interface display layer, quality grade evaluation multifaceted visual reconstructiontheory,established a software which is based on chipvisual reconstruction system, refactoringprocess, the restructuring plan design, using a standardized design interactive interface betweenthe various levels in a standardized way.
According to different vendors of digital image acquisition device in the market, all of themaccording to their own products and custom, the shortcomings of poor universality has carried onthe related research. First analysis of the pros and cons of different standard digital imageacquisition, the establishment of a generic model of heterogeneous hardware environment imageacquisition; Next, create a common image acquisition interface and well-designed SDK,including the initialization function set the function, gain the function, image processing, thecallback function, memory function and auxiliary function, the definition of the six categories offunction. Then the function interface and configuration design were studied.
On the basis of image acquisition, image processing steps are discussed in this paper. Firstlyanalyzes the basic needs of visual inspection, combing the basic process of visual inspection.Then according to the visual inspection process of the main requirements, design the onlinedetection machine vision detection algorithms library,the commonly used image pretreatment,image restoration, image segmentation, feature extraction algorithms such as standardization,parametric design, especially emphasizes optimization algorithm code, execution efficiency,robustness and so on. Finally, establish and improve a set of visual detection based onconfiguration information for the runtime environment, which can realize detection operatorsearch, call, overloading, and other functions, helping improve the detection system flexible,accelerated deployment object for different detection equipment.
To achieve image feature extraction and recognition, the first research to describe thecommon characteristics method, analyzes its advantages and disadvantages, points out that asingle feature or a small feature set limitations in reconfigurable visual inspectionmethod.Subsequently,he core issue is that to start design a feature extraction method based onstatistics and frequency joint,establishment of a general-purpose machine vision detection featureset of online detection and image graphics using the feature set of the three products the characteristics are described. Finally, try to design a decoupling method which ids based on thecharacteristics of genetic algorithm is presented and its key technology was studied.
Finally, the use of reconfigurable machine vision method for fastening belt, detonating tube,the appearance quality of the mesh fabric visual inspection are studied, developed withindependent intellectual property rights of inspection system, verify the reconfigurable productvisual inspection method is feasible and effective.
本文着眼于流水线上、不间断运动的连续物体视觉可识别量的在线检测。利用机器视觉替代人工检测，运用计算机及软件替代检测工人，通过可重构的视觉检测方法实现在不同被检对象上的快速反应与装备实现。
本文主要研究了可动态增减检测子系统的分布式网络拓扑，支撑采集控制硬件层、图像处理识别层、界面显示层、质量等级评价等多层面的视觉重构理论，建立了基于软件芯片的视觉重构体系，对重构流程、重构方案进行了设计，采用规范化、标准化的方式设计了各个层次间交互接口。
针对市场上不同厂商的数字图像获取设备，本文均根据自身产品而定制、通用性差的缺点进行了相关研究。首先，分析了不同数字图像获取标准的优劣，建立了异构硬件环境下的图像获取通用模型；接着，创建了通用图像获取接口，设计并完善了SDK，包括初始化函数、设置函数、获取函数、图像处理回调函数、存储函数、辅助函数在内的六大类函数的定义；最后，对函数接口及其组态设计进行了研究。
在图像获取的基础上，对图像处理环节进行了探讨。首先，分析了视觉检测的基本需求，梳理了视觉检测的基本流程；接着，根据视觉检测流程的主要需求，设计了机器视觉在线检测算法库，将常用的图像预处理、图像分割、图像复原、特征提取等算法进行了标准化、参数化设计，着重突出优化算法代码、执行效率、鲁棒性等问题；最后，建立并完善了一套基于配置信息的视觉检测运行环境，可实现检测算子的搜索、调用、重载等功能，有利于提高检测系统的柔性，加速针对不同检测对象的设备部署。
为实现图像特征的提取与识别，本文首先研究了常用特征描述方法，分析了其优缺点，并指出单一特征或过小规模特征集在可重构视觉检测方法中的局限性。其后，针对这一核心问题展开，设计了一套基于统计与时频联合的特征提取方法，建立了通用性机器视觉在线检测特征集，并利用该特征集对三种产品的图像图形学特征进行了描述。最后，尝试设计了基于遗传算法的特征解耦方法，并就其中关键技术进行了研究。
最后，利用可重构的机器视觉方法对粘扣带、导爆管、网孔织物外观质量视觉检测进行了研究，开发了具有自主知识产权的检测系统，验证了可重构的产品视觉检测方法的可行性和有效性。
Quality requirements is the core of modern enterprise, the product quality has a directrelationship with the market competitiveness of enterprises and the enterprises themselvessubsisting. Sothe enterprises give top priority to quality is a common choice. Now all thecompanies’ quality control and quality management methods are diverse. And traditionaldetection and measurement is often dependent on artificial completed.
This article focuses on assembly line continuous, uninterrupted movement object vision torecognize the amount of online detection. The alternative to manual inspection is machine vision,using computers and software to replace workers detection. And by using the reconfigurablevisual inspection of the implementation on different objects have been rapid response andequipment implementation.
Mainly studies distributed network topology dynamically increase or decrease the detectionsubsystem, support the collection and control hardware, image processing and recognition layer,interface display layer, quality grade evaluation multifaceted visual reconstructiontheory,established a software which is based on chipvisual reconstruction system, refactoringprocess, the restructuring plan design, using a standardized design interactive interface betweenthe various levels in a standardized way.
According to different vendors of digital image acquisition device in the market, all of themaccording to their own products and custom, the shortcomings of poor universality has carried onthe related research. First analysis of the pros and cons of different standard digital imageacquisition, the establishment of a generic model of heterogeneous hardware environment imageacquisition; Next, create a common image acquisition interface and well-designed SDK,including the initialization function set the function, gain the function, image processing, thecallback function, memory function and auxiliary function, the definition of the six categories offunction. Then the function interface and configuration design were studied.
On the basis of image acquisition, image processing steps are discussed in this paper. Firstlyanalyzes the basic needs of visual inspection, combing the basic process of visual inspection.Then according to the visual inspection process of the main requirements, design the onlinedetection machine vision detection algorithms library,the commonly used image pretreatment,image restoration, image segmentation, feature extraction algorithms such as standardization,parametric design, especially emphasizes optimization algorithm code, execution efficiency,robustness and so on. Finally, establish and improve a set of visual detection based onconfiguration information for the runtime environment, which can realize detection operatorsearch, call, overloading, and other functions, helping improve the detection system flexible,accelerated deployment object for different detection equipment.
To achieve image feature extraction and recognition, the first research to describe thecommon characteristics method, analyzes its advantages and disadvantages, points out that asingle feature or a small feature set limitations in reconfigurable visual inspectionmethod.Subsequently,he core issue is that to start design a feature extraction method based onstatistics and frequency joint,establishment of a general-purpose machine vision detection featureset of online detection and image graphics using the feature set of the three products the characteristics are described. Finally, try to design a decoupling method which ids based on thecharacteristics of genetic algorithm is presented and its key technology was studied.
Finally, the use of reconfigurable machine vision method for fastening belt, detonating tube,the appearance quality of the mesh fabric visual inspection are studied, developed withindependent intellectual property rights of inspection system, verify the reconfigurable productvisual inspection method is feasible and effective.
引文
[1]尚会超，吴军.织物表面疵点检测算法综述[J].中原工学院学报，2008，19(1)：16-18.
[2] Zhao Daxing，Wang Huang，Zhu Jinlei，Li Jiuling. Research on a new fabric defectidentification method[C]. International Conference on Computer Science and SoftwareEngineering，CSSE2008. USA：IEEE Computer Society，2008.
[3]赵大兴，朱锦雷，李九灵，颜云辉.基于机器视觉的织物检测方法的研究[J].计算机工程与设计，2008，29(1)：99-101.
[4]赵大兴，王璜，朱锦雷，颜云辉.高精度自适应阈值分割算子的设计[J].计算机应用，2008，28(7)：1742-1743.
[5] Li Jiuling，Wang Huang，Zhao Daxing，Zhu Jinlei. Research on fabric image registrationtechnique based on pattern recognition using cross-power spectrum[C].2009WRI WorldCongress on Software Engineering-WCSE2009. USA：IEEE Computer Society，2009.
[6]张正宇.塑料导爆管起爆系统理论与实践[M].中国水利水电出版社.2009.
[7]民用爆炸物品行业“十二五”发展规划.中华人民共和国工业与信息化部.2010.
[8]吴红梅，宋敬埔.塑料导爆管的感度及其研究进展[J].煤矿爆破.2003，4：27-30.
[9]高航，廖小翠.拉细导爆管对传爆可靠性影响的高速摄影试验的研究[J].现代矿业.2010，2：43-45.
[10]贾晓宏，雷智军.浅谈国内外导爆管雷管标准的差异[J].国防技术基础.2009，11：7~10.
[11]王志刚，李国新.塑料导爆管红外无损检测技术研究[J].火工品.1996，23-25.
[12]GUO Jianqin，LI Guoxin，ZHANG Huapeng. Research on Photoelectric and InspectingMethods and Its Effected Factors of the Explosive Content of Nonel Tube[J]. Science Press.2003，5：282-287.
[13]Holmberg R，White T. Nonel NPED detonator (non-primary explosives detonator)[J].Coal international Redhill.1995，232(1).
[14]Golnabi H.，Asadpour A. Design and application of industrial machine vision systems[J].Robotics and Computer-Integrated Manufacturing，2007，23(6)：630-637.
[15]Colet Philip. Machine vision aids quality inspection. Quality[J]，2006，45(10)：22-23.
[16]Halschka Martin，Schwarz Joachim，Wildmann Daniel，Wagner Frank. Machine vision-Thepowerful tool for quality assurance of laser welding and brazing[C]. ICALEO2003–22ndInternational Congress on Applications of Laser and Electro-Optics，Congress Proceedings.USA：Laser Institute of America，2003.
[17]Zhao Yanling，Wang Peng，Hao Huanrui，Liu Xianli. The embedded control system of visioninspecting instrument for steel ball surface defect[C]. Chinese Control and DecisionConference，2008，CCDC2008. USA：IEEE Computer Society，2008.
[18]Li Rui，Turke Thomas，Schaede Johannes，Willeke Harald，Lohweg Volker. FPGA-basedmulti-sensor real time machine vision for banknote printing[C]. Proceedings of SPIE–TheInternational Society for Optical Engineering. USA：SPIE，2009，7251.
[19]Dar lgbal M. Automated vision based quality control for electro-optical modulemanufacturing[C]. Machine Vision Applications，Architecture，and Systems Integration VI.USA：SPIE，1997，3205.
[20]Lotfi Ehsan，Yaghoobi M.，Pourreza H. R. A new approach for automatic quality control offried potatoes using machine learning[C].20087th IEEE International Conference onCybernetic Intelligent Systems，CIS2008. USA：IEEE Computer Society，2008.
[21]Yoshio Shimizu. Expert System to Inspect Fabric Defects by Pattern Recognition[J]. IEEETransactions on Pattern Recognition，1990，46(3)：460-469.
[22]Karathanassi V.，Lossifidis Chr.，Rokos D. Application of machine vision techniques in thequality control of pharmaceutical solutions[J]. Computers in Industry，1996，32(2)：169-179.
[23]Adamo Francesco，Attivissimo Filippo，Di Nisio Attilio，Savino Mario. An online defectsinspection system for satin glass based on machine vision[C].2009IEEE Instrumentationand Measurement Technology Conference，I2MTC2009. USA：IEEE Computer Society，2009.
[24]Martin-Herrero J.，Ferreiro-Arman M.，Alba-Castro J. L. A SOFM improves a real timequality assurance machine vision system[C]. Proceedings of the17th InternationalConference on Pattern Recognition，ICPR2004. IEEE，2004，4.
[25]Taouil Khaled，Chtourou Zied，Kamoun Lotfi. Machine vision based quality monitoring inolive oil conditioning[C].2008lst International Workshops on Image Processing Theory，Tools and Applications，IPTA2008. USA：IEEE Computer Society，2008.
[26]F. S. Cohen，Z. Fan，and S. Attali. Automated inspection of textile fabric using textilemodels[J]. IEEE transaction on Pattern Analysis and machine Intelligence，1991，12(8)：803-808，.
[27]C. Neubauer. Segmentation of defects. in textile fabric[C]. In Proceedings of the7thInternational Conferenceon Recognition，1992，1：688-691.
[28]Jasper WJ, Potlapallih, Image Anlysis of Mispicks in Woven Fabric[J]. Textile Res J,1995,65(11):683-692.
[29]I-Shou Tsai，Ming-Chuan Hu，Automatic inspection of fabric defects using an artificialneural network technique[J]. Textile Research Journal，1996，66(7)：475-481.
[30]A. Picaz，A. Averbuch. An efficient topological characterization of gray-levels textures usinga multi-resolution representation[J]. Graphical Models and Image Processing，1997，59(5)：11-17.
[31]李立轻，黄秀宝.用于疵点检测的织物自适应正交小波的实现[J].东华大学学报，2002，28(2).
[32]王文华.数学形态学在织物疵点检测中的应用[D].中国海洋大学.2007：13-18.
[33]Ozdemir S，Ercil A. Markov random fields and karhunen-loeve transforms for defectinspection of textile products[C]. Proc IEEE Conf Emerging Technologies and FactoryAutomation，EFTA’96，1996，2(2)：698-703.
[34]努尔顿，左保齐.织物疵点智能化判别[J].丝绸，2003，10(3)：36-38.
[35]李立轻，黄秀宝.像处理用于织物疵点自动检测的研究进展[J].东华大学纺织学报，2002，28(4)：118-122.
[36]张瑞林，徐轶峰.基于PCNN的织物疵点识别研究[J].纺织学报，2004，25(6)：69-71.
[37]张瑞林，蒋静坪.人工神经网络在纺织中的应用[J].浙江工程学院学报，2001，18(4)：201-207.
[38]A. Monadjemi，M. Mirmehdi，and B. Thomas. Restructured eigenfilter matching for noveltydetection in random textures[C]. In British Machine Vision Conference，2004，637-646.
[39]惠斌，唐旭晟，罗海波等.基于Gabor小波SDF匹配滤波器的人脸识别[J].信息与控制，2008，37(5)：633-636.
[40]陈洋，王润生.结合Gabor滤波器和ICA技术的纹理分类方法[J].电子学报，2007，35(2)：299-303.
[41]R. Azencott，J. P. Wang，L. Younes. Texture classification using windowed Fourier filters[J].IEEE Trans. Pattern Anal. Mach. Inetll，2004，19(3)：149-153.
[42]Yang X Z，Pang G K H，Yung N H C. Fabric defect detection using adaptive wavelet[C].ICASSP， IEEE International Conference on Acoustics， Speech and SignalProcessing2Proceedings，2001，6：3697-3700
[43]刘呈健，张贵彬.坯布自动检测系统简介[J].棉纺织技术，2005，33(3)：63-64.
[44]王宇旸.基于可重构计算技术的图像识别与分类系统研究[D].中国科学技术大学.2009.
[45]C. Basoglu，Woobin Lee，etal. The Equator MAP-CA DSP：an end-to-end broadband signalprocessor[J]. IEEE Transaction on Circuits and Systems for Video Technology.2002，12(8)：646-659.
[46]Paul Graham and Brent Nelson(1998). Frequency-domain sonar processing in FPGAs andDSPs[C]. Proceeding of the IEEE Symposium on FPGAs for Custom Computing Machines.
[47]Yan Lei，Zhao Gang. etal. The Platform of Image Acquisition and Processing System Basedon DSP and FPGA[C]. International Conference on Smart Manufacturing Application，2008.
[48]Shiqi Huang，Hong You，etal. Application of DSP parallel processing technology in SARimaging[C]. International Conference on Signal Processing，2004
[49]Qi Chang. Sun Fuxiong， etal. The real-time image processing based on DSP[C].International Workshop on Cellular Neural Networks and Their Applications，2005.
[50]Lee G. H. Reconfigurability considerations in design of component and manufacturingsystem[J]. International Journal of Advanced Manufacturing Technology，1997，139(5)：376-386.
[51]Koren Y，Ulsoy A G. Reconfigurable Manufacturing Systems[R]. Engineering ResearchCenter for Reconfigurable Machining System s Report No.1，Ann Arbor：The University ofMichigan，1997.
[52]Koren Y，Heisel U，Moriwoki F，et al. Reconfigurable manufacturing systems[J]. A nnals ofthe CIR P，1999，2：1-13.
[53]徐惠萍.可重构技术综述[J].甘肃科技，2007.10.
[54]National Research Council. Visionary Manufacturing Challenges for2020[M]. WashingtonD C：National Academy Press，1998.
[55]罗振璧，盛伯浩，赵晓波等.快速重组制造系统[J].中国机械工程，2000，11(3)：300-303.
[56]Luo Zhenbi，SHEN G Bohao，ZHAO Xiaobo，et al. Rapidreconfigurable manufacturingsystems[J]. J ChinaMechanicalEngineering，2000，11(3)：300-303.
[57]刘阶萍，罗振璧，陈恳.快速可重组制造系统的可诊断性设计原理[J].清华大学学报(自然科学版)，2000，40(9)：14-17.
[58]王成恩.制造系统的可重构性[J].计算机集成制造系统.2000.
[59]石双元,张金隆,蔡淑琴..信息系统可重构性研究. COMPUTER APPLICATIONS ANDSOFTWARE，2003，20(6).
[60]艾海舟.机器视觉及其应用[J].科学中国人，1997，9：23-25.
[61]Landers R G，Min B k，Koren Y. Reconfigurable machine tools[J]. Annals of the CIRP，2001，50(1)：269-274.
[62]KorenY. Reconfigurable manufacturing systems[J]. Annals of the CIRP，1999，48(2)：527-540.
[63]张衍均，殷跃红.可重构机器的模块化设计[J].机械与电子，2008，1：13-16.
[64]任树棠，车文龙，张伟.可重构制造系统的发展研究[J]，基点产品开发与创新，2009，22(5)：29-31.
[65]龙智帆，孙志海，孔万增.算法可重构的工业视觉饮料瓶盖缺陷检测[D].杭州电子科技大学学报，2012，32(1)：47-51.
[66]蒋庆，蔡晋辉，边绍辉，周泽魁.基于机器视觉的空瓶图像检测系统[J].粮油加工与食品机械，2003，(10)：51-53.
[67]尹伯彪，周肇飞.基于波前修正的大尺寸测量图像恢复技术研究[J].四川大学学报（工程科学版），2008，40(4)：172-175.
[68]刘怀广.浮法玻璃缺陷在线识别算法的研究及系统实现[D].华中科技大学，2011.
[69]Tsugito Maruyama，Shinji Kanda，Masahiko Sato and Takashi Uchiyama. Hand-eye systemwith three-dimensional vision and microgripper for handling flexible wire[J]，MachineVision and Applications，3(4)：189-199.
[70]T. Kauppi，J. K. Kamarainen，L. Lensu，H. K lvi inen and H. Uusitalo. Detection anddecision-support diagnosis of diabetic retinopathy using machine vision[J]. PatternRecognition and Image Analysis，21(2)：140-143.
[71]Huan Liu. Empty glass bottle inspection method based on fuzzy support vector machineneural network and machine vision[J]. Frontiers of Electrical and Electronic Engineering inChina，5(4)：430-440.
[72]A. Ravishan kar Rao. Future directions in industrial machinevision a case study ofsemiconductor manufacturing applications[J]. Image and Vision Computing，1996，14(1)：13-19.
[73]Joze Derganc，Bostjan Likar，Franjo Pernus. A machine vision system for measuring theeccentricity of bearings[J]. Computers in Industry，2003，50(1)：103-111.
[74]Bernard C. Jiang，S. J. Jiang. Machine vision based inspection of oil seals[J]. Journal ofManufacturing Systems，1998，17(3)：159-166.
[75]Chenglong Wang， Xiaoyu Li， Wei Wanga， Yaoze Fenga， Zhu Zhoua， Hui Zhana.Recognition of worm-eaten chestnuts based on machinevision[J]. Mathematical andComputer Modelling，2011，50(3)：888-894.
[76]蔡开元，白成刚，钟小军.构件软件系统的可靠性评估模型简介[J].西安交通大学学报，2003，37(6)：551-554.
[77]马国华著.监控组态软件及其应用(第一版)[M].北京：清华大学出版社，2001，8.
[78]NATO， NATO Standrad for Software Reuse Procedures[J]. NATO contact numberCO-5957-ADA，1991，3(3).
[79]杨芙清，梅宏，李克勤.软件复用与软件构件技术[J].电子学报.1999，27(2)：68-75.
[80]张杰，关永，孙继平.工控组态软件的可视化[J].中国图像图形学报，200，27(：2)201-204.
[81]艾萍，倪伟新.基于构件的水利领域软件标准化基础研究[J].水利学报，2003，12：104-107.
[82]Michihiro Matsumoto， Kokichi Futasugi. The Support Tool for Highly ReliableComponent-based Software Development[C]. Proceedings of the Seventh Asia-PacificSoftware Engineering Conference(APSEC.00)，2000.
[83]Bridget Spitzagel and David Garlan， A Compositional Approach for ConstructingConnectors[C]. Proceedings of the Working IEEE/IFIP Conference on SoftwareArchitecture(WICSA.01)，2001.
[84]Uwe Abmann etal. Meta-Programming grey-box connectors[C]. Proceedings of theTechnology of Object-Oriented Languages and Systems(TOOLs33)，St. Malo，France，5-8June2000.
[85]王听，金淳兆.软件体系结构的研究与发展现状[J].计算机科学，1998，25(3)：16-21.
[86]Dewayne Perry and Alexander L. Wolf. Foundations for the Study of SoftwareArchitecture[J]. ACM Software Engineering Notes，1992，17(4).
[87]Mary Shaw，David Garlen. An introduction to Software Architecture. Technological reportof SEI[J]. Carnegie Mellon University，1994.
[88]Rich Kazman etal. SAAM： A Method for Analyzing the Properties of SoftwareArchitecture[J]. Technological report of SEI. Carnegie Mellon University，1994.
[89]Robert T. Monroe etal. Architecture Style，Design Pattern，and Objects[J]. IEEE Software，14(1).1997.
[90]叶俊民，赵恒，曹瀚.软件体系结构风格的实例研究[J].小型微型计算机系统，2002，23(10)：1158-1160.
[91]赵会群，孙晶，王国仁.软件体系结构：一个新的研究领域[J].计算机科学，2002，29(11)：146-149.
[92]孙昌受，金茂忠，刘超.软件体系结构研究综述[J].软件学报，2002，13(7)：1228-1237.
[93]Mary Shaw. The Coming-of-age of Software Architecture Research[C]. Proceedings of23thInternational Conference on Software Engineering，January，2001，657-674.
[94]李朝辉.基于构件复用技术的组态模型及平台研究[D].大连理工大学，2005：26-27.
[95]仲萃豪，曹东启，郭荷清.信息系统的开发方法及其体系模型[J].软件学报，1995，vo.6l，StlPPlement：219-225.
[96]邓贵仕，李朝辉.信息系统组态平台及其构件复用技术[J].大连理工大学学报，2004，44(1)：127-131.
[97]高晓兴，王志鹏. TWAIN的构成和编程方法.计算机应用[J].2001，21(2)：74-76.
[98]尹东，王巍. TWAIN的原理及其应用开发[J].信息技术.2001(9)：15-16.
[99]向冬梅，肖佩.基于TWAIN标准的扫描仪接口软件的应用[J].武汉理工大学学报，2003，(06)：25-26.
[100] David Mosberger. The SANE Scanner Interface[J]. Linux Journal.1998，47(3).
[101] Emmanuel P. Baltsavias，The AGFA Horizon Scanner-Characteristics，Testing AndEvaluation，Institude of Geodesy and Photogrammetry[J]. Swiss Federal Institute.
[102]叶李，杨听梅，李仙平. VC巨幅数字图像文件快速显示技术[J].微型机与应用.2003(12)，9-12.
[103] Kenneth R. Castleman. Digital image Processing[M]. New Jersey：Prentice Hall.1996.
[104]何斌，马天予等. Visua1C++数字图像处理[M].北京，人民邮电出版社，2001.
[105]周长发.精通Visua1C++图像处理编程（第二版）[M].北京，电子工业出版社，2004.
[106]韩春雷，王库，马健.一种数码相机成像和视频处理前端的设计[J].单片机与嵌入式系统应用，2004，(09)：37-39.
[107]付庆军.数码相机与数字图像的获取[J].山东师范大学学报(自然科学版)，2004，(01)：105-106.
[108][美]Cay Horstmann著.晏海华等译. C++核心思想（第三版）[M].北京，电子工业出版社，2004.
[109]丁贵广，计文评，郭宝龙. VisualC++6.0数字图像编码[M].北京，机械工业出版社，2004.
[110] Castleman K R. Digital Image Processing [M].北京，电子工业出版社，2002.
[111]陆春梅.基于数字图像处理技术的接杆激光环焊焊缝视觉检测系统研究[D].上海交通大学，2008.
[112]王文惠，周良柱等.基于内容的图像检索技术的研究和发展[J].计算机工程与应用，2001，5：54-56.
[113]章毓晋.基于内容的视觉信息检索[M].科学出版社，2003.1.
[114] LI P. An Adaptive Binning Color model for Mean Shift tracking[J]. IEEE Transactions onCircuits and Systems for Video Technology，2008，18(9)：293-1299.
[115] KANKANHALLIMS，MEHTREBM，WUJK. Cluster-based Color Matching for ImageRetrieval[J]. Pattern Recognition，1996，29(4)：701-708.
[116] OHANIANP P，DUBES R C. Performance Evaluation for Four Classes of TexturalFeatures[J]. Pattern Recognition，1992，25(8)：819-833.
[117] CLAUSI D A，YUE B，Co-Occurrence Probabilities and Markov Random Fields forTexture Analysis of SAR Sea Ice Imagery[J]. IEEE Transactions on Geoscience and RemoteSensing，2004，42(1)：215-228.
[118] CLAUSI D A，Comparison and Fusion of Co-Occurrence，Gabor and MRF Texture forClassification of SAR Sea Ice Imagery[J]. Atmosphere Oceans，2001，39(4)：183-194.
[119] RANGARAJ M RANGAYYAN，NEMA M EI-FARAMAWY，LEO DE-SAUTELS J E，etal，Measures of Acutance and Shape for Classification of Breast Tumors[J]. IEEE Trans.on Medical Imaging.1997，16(6)：799-810.
[120] LIANG Shen，RANGARAJ M RANGAYYAN，LEO DESAULTELS J E. Detection andClassification of Mammographic Calcification[J]. International Journal of PatternRecognition and Artificial Intelligence，1993，7(6)：1403-1416.
[121]杜世宏，秦其明，王桥，空间关系及其应用[J].地学前缘，2006，13（3）：69-80.
[122]龚声蓉.基于内容的图像检索方法的研究[M].北京：北京航空航天大学出版社，2001.
[123]张铮，杨文平，石博强，李海鹏. MATLAB程序设计与实例应用[M].中国铁道出版社.2003.
[124]张振平.基于Bayes错误率上界最小的特征选择算法的研究[D].同济大学.2006.
[125]孙杨.基于Boosting思想的文本特征选择研究[D].南开大学.2008.
[126]苏映雪.特征选择算法研究[D].国防科学技术大学.2006.
[127]董慧.手写体数字识别中的特征提取和特征选择研究[D].北京邮电大学.2007.
[128]吕蕾.基于遗传算法的SVM研究及其在小区规划方案评价上的应用[D].山东师范大学.2009.
[129]赵云，刘惟一.基于遗传算法的特征选择方法[J].计算机工程与应用，2004，15：52-54.
[130] LIANG Shen，RANGARAJ M RANGAYYAN，LEO DESAUTELS J E. Application ofShape Analysis to Mammo-graphic Calcification[J]. IEEE Trans. on Medical Imaging.1994，13(2)：263-274.
[131]张振平.基于Bayes错误率上界最小的特征选择算法的研究[D].同济大学，2006
[132]孙杨.基于Boosting思想的文本特征选择研究[D].南开大学，2008
[133]苏映雪.特征选择算法研究[D].国防科学技术大学，2006
[134]董慧.手写体数字识别中的特征提取和特征选择研究[D].北京邮电大学，2007
[135]吕蕾.基于遗传算法的SVM研究及其在小区规划方案评价上的应用[D].山东师范大学，2009
[136]赵云，刘惟一.基于遗传算法的特征选择方法[J].计算机工程与应用，2004,15:52-54.
[137] Information on http://www.intersil.com
[138] Information on http://www.prophotonix.com
[139]朱娜敏，魏宗寿，李红.精通Windows程序设计——基于Visual C++实现[M].人民邮电出版社，2009
[140]原奕等. Visual C++实践与提高-数据库开发与工程应用篇[M].中国铁道出版社，2006
[2] Zhao Daxing，Wang Huang，Zhu Jinlei，Li Jiuling. Research on a new fabric defectidentification method[C]. International Conference on Computer Science and SoftwareEngineering，CSSE2008. USA：IEEE Computer Society，2008.
[3]赵大兴，朱锦雷，李九灵，颜云辉.基于机器视觉的织物检测方法的研究[J].计算机工程与设计，2008，29(1)：99-101.
[4]赵大兴，王璜，朱锦雷，颜云辉.高精度自适应阈值分割算子的设计[J].计算机应用，2008，28(7)：1742-1743.
[5] Li Jiuling，Wang Huang，Zhao Daxing，Zhu Jinlei. Research on fabric image registrationtechnique based on pattern recognition using cross-power spectrum[C].2009WRI WorldCongress on Software Engineering-WCSE2009. USA：IEEE Computer Society，2009.
[6]张正宇.塑料导爆管起爆系统理论与实践[M].中国水利水电出版社.2009.
[7]民用爆炸物品行业“十二五”发展规划.中华人民共和国工业与信息化部.2010.
[8]吴红梅，宋敬埔.塑料导爆管的感度及其研究进展[J].煤矿爆破.2003，4：27-30.
[9]高航，廖小翠.拉细导爆管对传爆可靠性影响的高速摄影试验的研究[J].现代矿业.2010，2：43-45.
[10]贾晓宏，雷智军.浅谈国内外导爆管雷管标准的差异[J].国防技术基础.2009，11：7~10.
[11]王志刚，李国新.塑料导爆管红外无损检测技术研究[J].火工品.1996，23-25.
[12]GUO Jianqin，LI Guoxin，ZHANG Huapeng. Research on Photoelectric and InspectingMethods and Its Effected Factors of the Explosive Content of Nonel Tube[J]. Science Press.2003，5：282-287.
[13]Holmberg R，White T. Nonel NPED detonator (non-primary explosives detonator)[J].Coal international Redhill.1995，232(1).
[14]Golnabi H.，Asadpour A. Design and application of industrial machine vision systems[J].Robotics and Computer-Integrated Manufacturing，2007，23(6)：630-637.
[15]Colet Philip. Machine vision aids quality inspection. Quality[J]，2006，45(10)：22-23.
[16]Halschka Martin，Schwarz Joachim，Wildmann Daniel，Wagner Frank. Machine vision-Thepowerful tool for quality assurance of laser welding and brazing[C]. ICALEO2003–22ndInternational Congress on Applications of Laser and Electro-Optics，Congress Proceedings.USA：Laser Institute of America，2003.
[17]Zhao Yanling，Wang Peng，Hao Huanrui，Liu Xianli. The embedded control system of visioninspecting instrument for steel ball surface defect[C]. Chinese Control and DecisionConference，2008，CCDC2008. USA：IEEE Computer Society，2008.
[18]Li Rui，Turke Thomas，Schaede Johannes，Willeke Harald，Lohweg Volker. FPGA-basedmulti-sensor real time machine vision for banknote printing[C]. Proceedings of SPIE–TheInternational Society for Optical Engineering. USA：SPIE，2009，7251.
[19]Dar lgbal M. Automated vision based quality control for electro-optical modulemanufacturing[C]. Machine Vision Applications，Architecture，and Systems Integration VI.USA：SPIE，1997，3205.
[20]Lotfi Ehsan，Yaghoobi M.，Pourreza H. R. A new approach for automatic quality control offried potatoes using machine learning[C].20087th IEEE International Conference onCybernetic Intelligent Systems，CIS2008. USA：IEEE Computer Society，2008.
[21]Yoshio Shimizu. Expert System to Inspect Fabric Defects by Pattern Recognition[J]. IEEETransactions on Pattern Recognition，1990，46(3)：460-469.
[22]Karathanassi V.，Lossifidis Chr.，Rokos D. Application of machine vision techniques in thequality control of pharmaceutical solutions[J]. Computers in Industry，1996，32(2)：169-179.
[23]Adamo Francesco，Attivissimo Filippo，Di Nisio Attilio，Savino Mario. An online defectsinspection system for satin glass based on machine vision[C].2009IEEE Instrumentationand Measurement Technology Conference，I2MTC2009. USA：IEEE Computer Society，2009.
[24]Martin-Herrero J.，Ferreiro-Arman M.，Alba-Castro J. L. A SOFM improves a real timequality assurance machine vision system[C]. Proceedings of the17th InternationalConference on Pattern Recognition，ICPR2004. IEEE，2004，4.
[25]Taouil Khaled，Chtourou Zied，Kamoun Lotfi. Machine vision based quality monitoring inolive oil conditioning[C].2008lst International Workshops on Image Processing Theory，Tools and Applications，IPTA2008. USA：IEEE Computer Society，2008.
[26]F. S. Cohen，Z. Fan，and S. Attali. Automated inspection of textile fabric using textilemodels[J]. IEEE transaction on Pattern Analysis and machine Intelligence，1991，12(8)：803-808，.
[27]C. Neubauer. Segmentation of defects. in textile fabric[C]. In Proceedings of the7thInternational Conferenceon Recognition，1992，1：688-691.
[28]Jasper WJ, Potlapallih, Image Anlysis of Mispicks in Woven Fabric[J]. Textile Res J,1995,65(11):683-692.
[29]I-Shou Tsai，Ming-Chuan Hu，Automatic inspection of fabric defects using an artificialneural network technique[J]. Textile Research Journal，1996，66(7)：475-481.
[30]A. Picaz，A. Averbuch. An efficient topological characterization of gray-levels textures usinga multi-resolution representation[J]. Graphical Models and Image Processing，1997，59(5)：11-17.
[31]李立轻，黄秀宝.用于疵点检测的织物自适应正交小波的实现[J].东华大学学报，2002，28(2).
[32]王文华.数学形态学在织物疵点检测中的应用[D].中国海洋大学.2007：13-18.
[33]Ozdemir S，Ercil A. Markov random fields and karhunen-loeve transforms for defectinspection of textile products[C]. Proc IEEE Conf Emerging Technologies and FactoryAutomation，EFTA’96，1996，2(2)：698-703.
[34]努尔顿，左保齐.织物疵点智能化判别[J].丝绸，2003，10(3)：36-38.
[35]李立轻，黄秀宝.像处理用于织物疵点自动检测的研究进展[J].东华大学纺织学报，2002，28(4)：118-122.
[36]张瑞林，徐轶峰.基于PCNN的织物疵点识别研究[J].纺织学报，2004，25(6)：69-71.
[37]张瑞林，蒋静坪.人工神经网络在纺织中的应用[J].浙江工程学院学报，2001，18(4)：201-207.
[38]A. Monadjemi，M. Mirmehdi，and B. Thomas. Restructured eigenfilter matching for noveltydetection in random textures[C]. In British Machine Vision Conference，2004，637-646.
[39]惠斌，唐旭晟，罗海波等.基于Gabor小波SDF匹配滤波器的人脸识别[J].信息与控制，2008，37(5)：633-636.
[40]陈洋，王润生.结合Gabor滤波器和ICA技术的纹理分类方法[J].电子学报，2007，35(2)：299-303.
[41]R. Azencott，J. P. Wang，L. Younes. Texture classification using windowed Fourier filters[J].IEEE Trans. Pattern Anal. Mach. Inetll，2004，19(3)：149-153.
[42]Yang X Z，Pang G K H，Yung N H C. Fabric defect detection using adaptive wavelet[C].ICASSP， IEEE International Conference on Acoustics， Speech and SignalProcessing2Proceedings，2001，6：3697-3700
[43]刘呈健，张贵彬.坯布自动检测系统简介[J].棉纺织技术，2005，33(3)：63-64.
[44]王宇旸.基于可重构计算技术的图像识别与分类系统研究[D].中国科学技术大学.2009.
[45]C. Basoglu，Woobin Lee，etal. The Equator MAP-CA DSP：an end-to-end broadband signalprocessor[J]. IEEE Transaction on Circuits and Systems for Video Technology.2002，12(8)：646-659.
[46]Paul Graham and Brent Nelson(1998). Frequency-domain sonar processing in FPGAs andDSPs[C]. Proceeding of the IEEE Symposium on FPGAs for Custom Computing Machines.
[47]Yan Lei，Zhao Gang. etal. The Platform of Image Acquisition and Processing System Basedon DSP and FPGA[C]. International Conference on Smart Manufacturing Application，2008.
[48]Shiqi Huang，Hong You，etal. Application of DSP parallel processing technology in SARimaging[C]. International Conference on Signal Processing，2004
[49]Qi Chang. Sun Fuxiong， etal. The real-time image processing based on DSP[C].International Workshop on Cellular Neural Networks and Their Applications，2005.
[50]Lee G. H. Reconfigurability considerations in design of component and manufacturingsystem[J]. International Journal of Advanced Manufacturing Technology，1997，139(5)：376-386.
[51]Koren Y，Ulsoy A G. Reconfigurable Manufacturing Systems[R]. Engineering ResearchCenter for Reconfigurable Machining System s Report No.1，Ann Arbor：The University ofMichigan，1997.
[52]Koren Y，Heisel U，Moriwoki F，et al. Reconfigurable manufacturing systems[J]. A nnals ofthe CIR P，1999，2：1-13.
[53]徐惠萍.可重构技术综述[J].甘肃科技，2007.10.
[54]National Research Council. Visionary Manufacturing Challenges for2020[M]. WashingtonD C：National Academy Press，1998.
[55]罗振璧，盛伯浩，赵晓波等.快速重组制造系统[J].中国机械工程，2000，11(3)：300-303.
[56]Luo Zhenbi，SHEN G Bohao，ZHAO Xiaobo，et al. Rapidreconfigurable manufacturingsystems[J]. J ChinaMechanicalEngineering，2000，11(3)：300-303.
[57]刘阶萍，罗振璧，陈恳.快速可重组制造系统的可诊断性设计原理[J].清华大学学报(自然科学版)，2000，40(9)：14-17.
[58]王成恩.制造系统的可重构性[J].计算机集成制造系统.2000.
[59]石双元,张金隆,蔡淑琴..信息系统可重构性研究. COMPUTER APPLICATIONS ANDSOFTWARE，2003，20(6).
[60]艾海舟.机器视觉及其应用[J].科学中国人，1997，9：23-25.
[61]Landers R G，Min B k，Koren Y. Reconfigurable machine tools[J]. Annals of the CIRP，2001，50(1)：269-274.
[62]KorenY. Reconfigurable manufacturing systems[J]. Annals of the CIRP，1999，48(2)：527-540.
[63]张衍均，殷跃红.可重构机器的模块化设计[J].机械与电子，2008，1：13-16.
[64]任树棠，车文龙，张伟.可重构制造系统的发展研究[J]，基点产品开发与创新，2009，22(5)：29-31.
[65]龙智帆，孙志海，孔万增.算法可重构的工业视觉饮料瓶盖缺陷检测[D].杭州电子科技大学学报，2012，32(1)：47-51.
[66]蒋庆，蔡晋辉，边绍辉，周泽魁.基于机器视觉的空瓶图像检测系统[J].粮油加工与食品机械，2003，(10)：51-53.
[67]尹伯彪，周肇飞.基于波前修正的大尺寸测量图像恢复技术研究[J].四川大学学报（工程科学版），2008，40(4)：172-175.
[68]刘怀广.浮法玻璃缺陷在线识别算法的研究及系统实现[D].华中科技大学，2011.
[69]Tsugito Maruyama，Shinji Kanda，Masahiko Sato and Takashi Uchiyama. Hand-eye systemwith three-dimensional vision and microgripper for handling flexible wire[J]，MachineVision and Applications，3(4)：189-199.
[70]T. Kauppi，J. K. Kamarainen，L. Lensu，H. K lvi inen and H. Uusitalo. Detection anddecision-support diagnosis of diabetic retinopathy using machine vision[J]. PatternRecognition and Image Analysis，21(2)：140-143.
[71]Huan Liu. Empty glass bottle inspection method based on fuzzy support vector machineneural network and machine vision[J]. Frontiers of Electrical and Electronic Engineering inChina，5(4)：430-440.
[72]A. Ravishan kar Rao. Future directions in industrial machinevision a case study ofsemiconductor manufacturing applications[J]. Image and Vision Computing，1996，14(1)：13-19.
[73]Joze Derganc，Bostjan Likar，Franjo Pernus. A machine vision system for measuring theeccentricity of bearings[J]. Computers in Industry，2003，50(1)：103-111.
[74]Bernard C. Jiang，S. J. Jiang. Machine vision based inspection of oil seals[J]. Journal ofManufacturing Systems，1998，17(3)：159-166.
[75]Chenglong Wang， Xiaoyu Li， Wei Wanga， Yaoze Fenga， Zhu Zhoua， Hui Zhana.Recognition of worm-eaten chestnuts based on machinevision[J]. Mathematical andComputer Modelling，2011，50(3)：888-894.
[76]蔡开元，白成刚，钟小军.构件软件系统的可靠性评估模型简介[J].西安交通大学学报，2003，37(6)：551-554.
[77]马国华著.监控组态软件及其应用(第一版)[M].北京：清华大学出版社，2001，8.
[78]NATO， NATO Standrad for Software Reuse Procedures[J]. NATO contact numberCO-5957-ADA，1991，3(3).
[79]杨芙清，梅宏，李克勤.软件复用与软件构件技术[J].电子学报.1999，27(2)：68-75.
[80]张杰，关永，孙继平.工控组态软件的可视化[J].中国图像图形学报，200，27(：2)201-204.
[81]艾萍，倪伟新.基于构件的水利领域软件标准化基础研究[J].水利学报，2003，12：104-107.
[82]Michihiro Matsumoto， Kokichi Futasugi. The Support Tool for Highly ReliableComponent-based Software Development[C]. Proceedings of the Seventh Asia-PacificSoftware Engineering Conference(APSEC.00)，2000.
[83]Bridget Spitzagel and David Garlan， A Compositional Approach for ConstructingConnectors[C]. Proceedings of the Working IEEE/IFIP Conference on SoftwareArchitecture(WICSA.01)，2001.
[84]Uwe Abmann etal. Meta-Programming grey-box connectors[C]. Proceedings of theTechnology of Object-Oriented Languages and Systems(TOOLs33)，St. Malo，France，5-8June2000.
[85]王听，金淳兆.软件体系结构的研究与发展现状[J].计算机科学，1998，25(3)：16-21.
[86]Dewayne Perry and Alexander L. Wolf. Foundations for the Study of SoftwareArchitecture[J]. ACM Software Engineering Notes，1992，17(4).
[87]Mary Shaw，David Garlen. An introduction to Software Architecture. Technological reportof SEI[J]. Carnegie Mellon University，1994.
[88]Rich Kazman etal. SAAM： A Method for Analyzing the Properties of SoftwareArchitecture[J]. Technological report of SEI. Carnegie Mellon University，1994.
[89]Robert T. Monroe etal. Architecture Style，Design Pattern，and Objects[J]. IEEE Software，14(1).1997.
[90]叶俊民，赵恒，曹瀚.软件体系结构风格的实例研究[J].小型微型计算机系统，2002，23(10)：1158-1160.
[91]赵会群，孙晶，王国仁.软件体系结构：一个新的研究领域[J].计算机科学，2002，29(11)：146-149.
[92]孙昌受，金茂忠，刘超.软件体系结构研究综述[J].软件学报，2002，13(7)：1228-1237.
[93]Mary Shaw. The Coming-of-age of Software Architecture Research[C]. Proceedings of23thInternational Conference on Software Engineering，January，2001，657-674.
[94]李朝辉.基于构件复用技术的组态模型及平台研究[D].大连理工大学，2005：26-27.
[95]仲萃豪，曹东启，郭荷清.信息系统的开发方法及其体系模型[J].软件学报，1995，vo.6l，StlPPlement：219-225.
[96]邓贵仕，李朝辉.信息系统组态平台及其构件复用技术[J].大连理工大学学报，2004，44(1)：127-131.
[97]高晓兴，王志鹏. TWAIN的构成和编程方法.计算机应用[J].2001，21(2)：74-76.
[98]尹东，王巍. TWAIN的原理及其应用开发[J].信息技术.2001(9)：15-16.
[99]向冬梅，肖佩.基于TWAIN标准的扫描仪接口软件的应用[J].武汉理工大学学报，2003，(06)：25-26.
[100] David Mosberger. The SANE Scanner Interface[J]. Linux Journal.1998，47(3).
[101] Emmanuel P. Baltsavias，The AGFA Horizon Scanner-Characteristics，Testing AndEvaluation，Institude of Geodesy and Photogrammetry[J]. Swiss Federal Institute.
[102]叶李，杨听梅，李仙平. VC巨幅数字图像文件快速显示技术[J].微型机与应用.2003(12)，9-12.
[103] Kenneth R. Castleman. Digital image Processing[M]. New Jersey：Prentice Hall.1996.
[104]何斌，马天予等. Visua1C++数字图像处理[M].北京，人民邮电出版社，2001.
[105]周长发.精通Visua1C++图像处理编程（第二版）[M].北京，电子工业出版社，2004.
[106]韩春雷，王库，马健.一种数码相机成像和视频处理前端的设计[J].单片机与嵌入式系统应用，2004，(09)：37-39.
[107]付庆军.数码相机与数字图像的获取[J].山东师范大学学报(自然科学版)，2004，(01)：105-106.
[108][美]Cay Horstmann著.晏海华等译. C++核心思想（第三版）[M].北京，电子工业出版社，2004.
[109]丁贵广，计文评，郭宝龙. VisualC++6.0数字图像编码[M].北京，机械工业出版社，2004.
[110] Castleman K R. Digital Image Processing [M].北京，电子工业出版社，2002.
[111]陆春梅.基于数字图像处理技术的接杆激光环焊焊缝视觉检测系统研究[D].上海交通大学，2008.
[112]王文惠，周良柱等.基于内容的图像检索技术的研究和发展[J].计算机工程与应用，2001，5：54-56.
[113]章毓晋.基于内容的视觉信息检索[M].科学出版社，2003.1.
[114] LI P. An Adaptive Binning Color model for Mean Shift tracking[J]. IEEE Transactions onCircuits and Systems for Video Technology，2008，18(9)：293-1299.
[115] KANKANHALLIMS，MEHTREBM，WUJK. Cluster-based Color Matching for ImageRetrieval[J]. Pattern Recognition，1996，29(4)：701-708.
[116] OHANIANP P，DUBES R C. Performance Evaluation for Four Classes of TexturalFeatures[J]. Pattern Recognition，1992，25(8)：819-833.
[117] CLAUSI D A，YUE B，Co-Occurrence Probabilities and Markov Random Fields forTexture Analysis of SAR Sea Ice Imagery[J]. IEEE Transactions on Geoscience and RemoteSensing，2004，42(1)：215-228.
[118] CLAUSI D A，Comparison and Fusion of Co-Occurrence，Gabor and MRF Texture forClassification of SAR Sea Ice Imagery[J]. Atmosphere Oceans，2001，39(4)：183-194.
[119] RANGARAJ M RANGAYYAN，NEMA M EI-FARAMAWY，LEO DE-SAUTELS J E，etal，Measures of Acutance and Shape for Classification of Breast Tumors[J]. IEEE Trans.on Medical Imaging.1997，16(6)：799-810.
[120] LIANG Shen，RANGARAJ M RANGAYYAN，LEO DESAULTELS J E. Detection andClassification of Mammographic Calcification[J]. International Journal of PatternRecognition and Artificial Intelligence，1993，7(6)：1403-1416.
[121]杜世宏，秦其明，王桥，空间关系及其应用[J].地学前缘，2006，13（3）：69-80.
[122]龚声蓉.基于内容的图像检索方法的研究[M].北京：北京航空航天大学出版社，2001.
[123]张铮，杨文平，石博强，李海鹏. MATLAB程序设计与实例应用[M].中国铁道出版社.2003.
[124]张振平.基于Bayes错误率上界最小的特征选择算法的研究[D].同济大学.2006.
[125]孙杨.基于Boosting思想的文本特征选择研究[D].南开大学.2008.
[126]苏映雪.特征选择算法研究[D].国防科学技术大学.2006.
[127]董慧.手写体数字识别中的特征提取和特征选择研究[D].北京邮电大学.2007.
[128]吕蕾.基于遗传算法的SVM研究及其在小区规划方案评价上的应用[D].山东师范大学.2009.
[129]赵云，刘惟一.基于遗传算法的特征选择方法[J].计算机工程与应用，2004，15：52-54.
[130] LIANG Shen，RANGARAJ M RANGAYYAN，LEO DESAUTELS J E. Application ofShape Analysis to Mammo-graphic Calcification[J]. IEEE Trans. on Medical Imaging.1994，13(2)：263-274.
[131]张振平.基于Bayes错误率上界最小的特征选择算法的研究[D].同济大学，2006
[132]孙杨.基于Boosting思想的文本特征选择研究[D].南开大学，2008
[133]苏映雪.特征选择算法研究[D].国防科学技术大学，2006
[134]董慧.手写体数字识别中的特征提取和特征选择研究[D].北京邮电大学，2007
[135]吕蕾.基于遗传算法的SVM研究及其在小区规划方案评价上的应用[D].山东师范大学，2009
[136]赵云，刘惟一.基于遗传算法的特征选择方法[J].计算机工程与应用，2004,15:52-54.
[137] Information on http://www.intersil.com
[138] Information on http://www.prophotonix.com
[139]朱娜敏，魏宗寿，李红.精通Windows程序设计——基于Visual C++实现[M].人民邮电出版社，2009
[140]原奕等. Visual C++实践与提高-数据库开发与工程应用篇[M].中国铁道出版社，2006
© 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 |