梳棉机棉网质量计算机视觉检测系统研究
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摘要
本文对基于计算机视觉技术的梳棉机棉网质量检测系统的开发方案与实现算法进行了系统地研究。传统的目光检测很大程度上取决于测试人员的主观判断,且效率低下,费工费时,因此本文探讨了棉网质量检测系统的构建方案和检测算法,实现了用计算机视觉检测代替工人检测,提高了纺织品生产中的自动化程度和效率。论文涉及了检测系统软硬件平台的构建、棉网数字图像的预处理、棉结杂质等疵点的图像分割识别技术、棉网均匀度的计算方法以及后续生条均匀度检测等主要内容。现将论文各章节内容简要介绍如下：
第一章主要概述了计算机视觉及其检测系统应用现状以及对棉结、杂质、棉网质量检测的研究现状。主要有：论述了课题的背景和意义；介绍了计算机视觉的由来及其检测系统的应用发展现状；在介绍了棉网质量检测内容为棉结、杂质等疵点检测和棉网均匀度检测的基础上,着重介绍了国内外一些检测棉结、杂质和棉网均匀度的研究方法,及历史研究中存在的问题；文中还介绍了现有国外一些检测棉结、杂质的设备,国内目前在这方面与国外有一定差距。
第二章主要探讨了梳棉机棉网质量视觉检测系统的构建方案。文中阐述了棉网质量检测系统的组成结构和工作流程,系统主要由图像采集、图像处理识别和用户界面三部分组成。在系统的主要硬件方面,首先介绍了线阵CCD与面阵CCD的区别,根据实际实验条件选择了适合本文的线阵扫描CCD传感器,同时给出了对应搭配线阵CCD的光学镜头；接着比较了几种常见的光源,在此基础上选择了白色条形LED线光源作为系统的照明装置；最后,阐述了为防止棉网出现褶皱现象而在喇叭口前放置了一对辅助压辊,同时介绍了本系统所选用的图像采集卡、计算机主机和显示器等。文中详细阐述了系统工作原理中对图像采集参数的设定。根据凸透镜成像和棉网幅宽,确定了线阵CCD的安装位置；通过前向照明和背向照明的采集效果对比,采用了前向照明作为本系统的照射方案；给出了由棉网输出速度计算图像采集速度的公式；讨论了光照强度、采集速度和光圈大小对背景亮度的关系,并给出了在一定光圈大小下,光照强度和采集速度的线性关系。同时,文中简要介绍了对棉网图像的检测原理及系统的操作步骤和特点。
第三章主要介绍了数字化棉网图像的预处理方法。介绍了棉网图像采样和量化成离散数字图像的过程。对于近距离成像所产生的照度不匀问题,比较了减图像法和高帽变换法,结合后续杂质检测的原因,采用了在减图像的基础上加上图像背景最大列平均灰度值的方法,达到了去除照度不匀的效果；比较了直方图修正和同态滤波的图像增强效果,结合多个指标进行评判,采用直方图线性灰度变换对棉网图像进行增强,使之达到了便于后续检测特别是识别棉结的目的；对各种滤波去噪方法进行了比较,结合去噪效果和处理时间,选用了维纳滤波来去除图像中的噪点。
第四章主要研究了棉网疵点检测的方法。介绍了棉网疵点的分类(棉结、杂质、云斑、破网),在分析各种疵点在图像上具有的特征基础上,确定了检测棉网疵点的思路,即根据灰度阈值来分割图像,面积阈值来识别疵点大小。详细阐述了图像阈值分割的原理和方法。对于棉结的检测,由于棉网的纹理结构较为复杂,采用全局阂值来对图像进行分割。介绍了五种阈值分割方法分割棉网图像的结果,结合面积阈值,通过对其中四种分割算法的比较和评判,选取了高斯拟合阈值法作为最优的分割算法。对于杂质和破网的检测,利用了其灰度比背景灰度相关来进行图像分割。文中用区域生长法对云斑进行了检测。对棉网疵点检测流程进行优化,提高了检测速度和精度。对300幅棉网图像中的棉结和杂质进行了识别,检测的统计结果与人眼目测对比,具有较高的正确检出率和较低的误检率,且算法运算速度快,说明了所选用的检测算法是合理有效的。文中还用实验对比了与AFIS棉结仪的检测结果,由于本文检测方式和原理与AFIS棉结仪的不同,得到了检测结果低度相关的结论。
第五章主要研究了棉网均匀度计算方法,并由此来检测梳棉半制品生条的均匀度。在介绍了棉网均匀度定义及其数字特征的基础上,采用了区域灰度差异法和灰度共生矩阵法来定量计算棉网均匀度,与目测做对比,结果表明了区域灰度差异法中的CV(%)值能较好地评判棉网均匀度,可作为棉网均匀度的表征指标,并给出了棉网均匀度参考等级标准。根据棉网在喇叭口汇聚的实际情况,建立了棉网与生条条干之间的几何关系,并提出了计算机检测生条均匀度的方法。实验表明,计算机检测生条均匀度结果与YG135G条干仪和切段称重法的检测结果线性相关,计算机检测结果可用来表征实际生条不匀。文中还讨论了给棉罗拉和道夫的速度变化对生条不匀的影响。实验表明,检测结果符合实际生产的规律,且检测结果对梳棉工艺参数的优化起了一定的参考作用。
第六章对全文进行了总结,归纳了本文的研究内容和研究结果,指出了本文的不足之处,并对今后的研究工作提出看法与展望。
通过研制梳棉棉网质量计算机视觉检测系统,提供了能够识别棉网中的棉结、杂质等疵点的方法,并实现了对棉网均匀度和后续生条均匀度的检测,检测结果准确率较高。而且系统在算法设计上考虑了实时性的要求,为进一步对棉网质量的实时检测和梳棉机的实时控制打下了一定的基础。
This research focused on the computer vision detecting system of the carded web quality on the cotton carding machine, including the detection and algorithm based on computer. Traditionally, the web quality is subjective visually evaluated by tester or operator, with low efficiency and long period. In this research, the web quality objective detect system is established and the corresponding algorithm is developed, which can evaluating the quality of carded web fast and conveniently, therefore, the efficiency and the degree of automatic in textile processing can be improved. In this research, the establishing of detect system, the pre-processing of detected image of carded web, the image segmentation and recognition on neps and trashes in web, and the calculation of evenness of the web and the resultant carded sliver are all dealt with. The contents of the dissertation are shown as follows:
Chapter1is the general introduction and literatures review, the background of this research, the development of the visual detect system based on computer, and the previous research works focused on detecting on carded web, especially the research works in the detect on nep, trash in the web and the web evenness were reviewed and commented, some application of the detect and the devices were also introduced and commented.
Chapter2focused on the establishing of computer vision detect system of carded web quality on a pilot carded machine. The construction and schematic of the system are introduced. The system is composed of three parts:image collection, image recognition processing and the operate interface of user. In the hardware of the system, the difference between linear array CCD and plane array CCD was compared and analyzed, therefore, the linear array CCD sensor was employed according to the requirement of this research, and the corresponding lens was selected. Then, some commonly used light sources were compared and analyzed, on the base of that, the LED with white line was selected as the light source of the system. Finally, a pair of assistant rollers was designed to amount in front of trumpet in card machine, which is helpful to avoid the crease of the web when it is condensed into a sliver. The parameters used in this system were introduced and optimized, the position of the CCD camera was determined on the width of the web and lens specification; the front light source was used in this system, after comparing the result between back light source and front light source; the speed of image acquisition was figured out on the base of web output speed; with a certain aperture, the relation between the intensity of illumination and the speed of image acquisition was obtained.
Chapter3deals with the pre-processing of image. The collection of web image and its conversion into the discrete data image was introduced. With the reduce image method and top-hat transfer method, and consider the sequent trash detecting, the reduce image method combine average gray value of highest column on the background was used to overcome the unevenness of intensity of illumination; with the comparison of the reinforce result of correction of histogram and homomorphism filtering, the linear transfer of gray value was used to reinforce the image of web, which was convenient to sequent detect and nep recognition; with the comparison of some noise filtering and time consume, the Wiener filtering was employed to remove the noise in the web image.
In Chapter4, the detecting of defaults in web was studied. The classification of web defaults, such as neps, trashes, cloud-like and broken part in web, were introduced. On the base of the characteristics of these defaults, the detecting method of the image was determined. The image segmented by gray threshold, and the size of flaws was detected by area threshold. For the detecting of nep, because the complex of texture, the global threshold was used to segment the image. By comparison the results of five methods used to segment the image, the Gauss fit threshold method was determined as the optimum method. For the detecting of trash and broken webs, the characteristic of their gray value correlation with that of background was used to segment the image. In this chapter, the cloud-like webs were detected by region growing method. Optimize the detecting process of the cotton web flaws, improving the detection speed and accuracy.The results of nep and trash recognition for300images compared to those of test results by people vision, the image recognition was much more faster with high correct detection rate and low false drop rate, which means the method used in this system is reasonable and liable. The test results were also compared with those of instrument, but because of the different mechanism, the detection of this system was different from those detected by AFIS.
In chapter5, the calculation of the evenness of carded web was studied, therefore, the evenness of output sliver was figured out. On the base of the definition and characteristic of the evenness of web, the area gray value differential method and grey level co-occurrence matrix were used to calculate the evenness of the web, compare to the vision test on the web, this CV%value of area gray value differential method can be better to character the evenness of web, hence it not only can be used to evaluate the evenness of the web but also can be the standard level of evenness of the web. According to the web in trumpet, the relation between web and output sliver was established, therefore, the calculation of the evenness of output sliver by computer was proposed. The results calculated by computer and the results tested by YG135G instrument was linear related, that means the calculated results by computer can be used to present the evenness of sliver. In this chapter, the effects of speeds of both feed roller and doffer on the evenness of output sliver were also tested, the results showed that the calculated results meet the knowledge very well, it also means the method by this research was reasonable and feasible.
Chapter6is the conclusion and prospect, the contents and results of this research was concluded, the deficiencies were also pointed out, the suggestion and for further research were proposed.
Generally, this research established the computer vision detect system on carded web quality, the method to recognize the neps and trashes in web, the test was fast and conveniently and the results was much accuracy, which offers the base of online test and real-time control in card machine.
第一章主要概述了计算机视觉及其检测系统应用现状以及对棉结、杂质、棉网质量检测的研究现状。主要有：论述了课题的背景和意义；介绍了计算机视觉的由来及其检测系统的应用发展现状；在介绍了棉网质量检测内容为棉结、杂质等疵点检测和棉网均匀度检测的基础上,着重介绍了国内外一些检测棉结、杂质和棉网均匀度的研究方法,及历史研究中存在的问题；文中还介绍了现有国外一些检测棉结、杂质的设备,国内目前在这方面与国外有一定差距。
第二章主要探讨了梳棉机棉网质量视觉检测系统的构建方案。文中阐述了棉网质量检测系统的组成结构和工作流程,系统主要由图像采集、图像处理识别和用户界面三部分组成。在系统的主要硬件方面,首先介绍了线阵CCD与面阵CCD的区别,根据实际实验条件选择了适合本文的线阵扫描CCD传感器,同时给出了对应搭配线阵CCD的光学镜头；接着比较了几种常见的光源,在此基础上选择了白色条形LED线光源作为系统的照明装置；最后,阐述了为防止棉网出现褶皱现象而在喇叭口前放置了一对辅助压辊,同时介绍了本系统所选用的图像采集卡、计算机主机和显示器等。文中详细阐述了系统工作原理中对图像采集参数的设定。根据凸透镜成像和棉网幅宽,确定了线阵CCD的安装位置；通过前向照明和背向照明的采集效果对比,采用了前向照明作为本系统的照射方案；给出了由棉网输出速度计算图像采集速度的公式；讨论了光照强度、采集速度和光圈大小对背景亮度的关系,并给出了在一定光圈大小下,光照强度和采集速度的线性关系。同时,文中简要介绍了对棉网图像的检测原理及系统的操作步骤和特点。
第三章主要介绍了数字化棉网图像的预处理方法。介绍了棉网图像采样和量化成离散数字图像的过程。对于近距离成像所产生的照度不匀问题,比较了减图像法和高帽变换法,结合后续杂质检测的原因,采用了在减图像的基础上加上图像背景最大列平均灰度值的方法,达到了去除照度不匀的效果；比较了直方图修正和同态滤波的图像增强效果,结合多个指标进行评判,采用直方图线性灰度变换对棉网图像进行增强,使之达到了便于后续检测特别是识别棉结的目的；对各种滤波去噪方法进行了比较,结合去噪效果和处理时间,选用了维纳滤波来去除图像中的噪点。
第四章主要研究了棉网疵点检测的方法。介绍了棉网疵点的分类(棉结、杂质、云斑、破网),在分析各种疵点在图像上具有的特征基础上,确定了检测棉网疵点的思路,即根据灰度阈值来分割图像,面积阈值来识别疵点大小。详细阐述了图像阈值分割的原理和方法。对于棉结的检测,由于棉网的纹理结构较为复杂,采用全局阂值来对图像进行分割。介绍了五种阈值分割方法分割棉网图像的结果,结合面积阈值,通过对其中四种分割算法的比较和评判,选取了高斯拟合阈值法作为最优的分割算法。对于杂质和破网的检测,利用了其灰度比背景灰度相关来进行图像分割。文中用区域生长法对云斑进行了检测。对棉网疵点检测流程进行优化,提高了检测速度和精度。对300幅棉网图像中的棉结和杂质进行了识别,检测的统计结果与人眼目测对比,具有较高的正确检出率和较低的误检率,且算法运算速度快,说明了所选用的检测算法是合理有效的。文中还用实验对比了与AFIS棉结仪的检测结果,由于本文检测方式和原理与AFIS棉结仪的不同,得到了检测结果低度相关的结论。
第五章主要研究了棉网均匀度计算方法,并由此来检测梳棉半制品生条的均匀度。在介绍了棉网均匀度定义及其数字特征的基础上,采用了区域灰度差异法和灰度共生矩阵法来定量计算棉网均匀度,与目测做对比,结果表明了区域灰度差异法中的CV(%)值能较好地评判棉网均匀度,可作为棉网均匀度的表征指标,并给出了棉网均匀度参考等级标准。根据棉网在喇叭口汇聚的实际情况,建立了棉网与生条条干之间的几何关系,并提出了计算机检测生条均匀度的方法。实验表明,计算机检测生条均匀度结果与YG135G条干仪和切段称重法的检测结果线性相关,计算机检测结果可用来表征实际生条不匀。文中还讨论了给棉罗拉和道夫的速度变化对生条不匀的影响。实验表明,检测结果符合实际生产的规律,且检测结果对梳棉工艺参数的优化起了一定的参考作用。
第六章对全文进行了总结,归纳了本文的研究内容和研究结果,指出了本文的不足之处,并对今后的研究工作提出看法与展望。
通过研制梳棉棉网质量计算机视觉检测系统,提供了能够识别棉网中的棉结、杂质等疵点的方法,并实现了对棉网均匀度和后续生条均匀度的检测,检测结果准确率较高。而且系统在算法设计上考虑了实时性的要求,为进一步对棉网质量的实时检测和梳棉机的实时控制打下了一定的基础。
This research focused on the computer vision detecting system of the carded web quality on the cotton carding machine, including the detection and algorithm based on computer. Traditionally, the web quality is subjective visually evaluated by tester or operator, with low efficiency and long period. In this research, the web quality objective detect system is established and the corresponding algorithm is developed, which can evaluating the quality of carded web fast and conveniently, therefore, the efficiency and the degree of automatic in textile processing can be improved. In this research, the establishing of detect system, the pre-processing of detected image of carded web, the image segmentation and recognition on neps and trashes in web, and the calculation of evenness of the web and the resultant carded sliver are all dealt with. The contents of the dissertation are shown as follows:
Chapter1is the general introduction and literatures review, the background of this research, the development of the visual detect system based on computer, and the previous research works focused on detecting on carded web, especially the research works in the detect on nep, trash in the web and the web evenness were reviewed and commented, some application of the detect and the devices were also introduced and commented.
Chapter2focused on the establishing of computer vision detect system of carded web quality on a pilot carded machine. The construction and schematic of the system are introduced. The system is composed of three parts:image collection, image recognition processing and the operate interface of user. In the hardware of the system, the difference between linear array CCD and plane array CCD was compared and analyzed, therefore, the linear array CCD sensor was employed according to the requirement of this research, and the corresponding lens was selected. Then, some commonly used light sources were compared and analyzed, on the base of that, the LED with white line was selected as the light source of the system. Finally, a pair of assistant rollers was designed to amount in front of trumpet in card machine, which is helpful to avoid the crease of the web when it is condensed into a sliver. The parameters used in this system were introduced and optimized, the position of the CCD camera was determined on the width of the web and lens specification; the front light source was used in this system, after comparing the result between back light source and front light source; the speed of image acquisition was figured out on the base of web output speed; with a certain aperture, the relation between the intensity of illumination and the speed of image acquisition was obtained.
Chapter3deals with the pre-processing of image. The collection of web image and its conversion into the discrete data image was introduced. With the reduce image method and top-hat transfer method, and consider the sequent trash detecting, the reduce image method combine average gray value of highest column on the background was used to overcome the unevenness of intensity of illumination; with the comparison of the reinforce result of correction of histogram and homomorphism filtering, the linear transfer of gray value was used to reinforce the image of web, which was convenient to sequent detect and nep recognition; with the comparison of some noise filtering and time consume, the Wiener filtering was employed to remove the noise in the web image.
In Chapter4, the detecting of defaults in web was studied. The classification of web defaults, such as neps, trashes, cloud-like and broken part in web, were introduced. On the base of the characteristics of these defaults, the detecting method of the image was determined. The image segmented by gray threshold, and the size of flaws was detected by area threshold. For the detecting of nep, because the complex of texture, the global threshold was used to segment the image. By comparison the results of five methods used to segment the image, the Gauss fit threshold method was determined as the optimum method. For the detecting of trash and broken webs, the characteristic of their gray value correlation with that of background was used to segment the image. In this chapter, the cloud-like webs were detected by region growing method. Optimize the detecting process of the cotton web flaws, improving the detection speed and accuracy.The results of nep and trash recognition for300images compared to those of test results by people vision, the image recognition was much more faster with high correct detection rate and low false drop rate, which means the method used in this system is reasonable and liable. The test results were also compared with those of instrument, but because of the different mechanism, the detection of this system was different from those detected by AFIS.
In chapter5, the calculation of the evenness of carded web was studied, therefore, the evenness of output sliver was figured out. On the base of the definition and characteristic of the evenness of web, the area gray value differential method and grey level co-occurrence matrix were used to calculate the evenness of the web, compare to the vision test on the web, this CV%value of area gray value differential method can be better to character the evenness of web, hence it not only can be used to evaluate the evenness of the web but also can be the standard level of evenness of the web. According to the web in trumpet, the relation between web and output sliver was established, therefore, the calculation of the evenness of output sliver by computer was proposed. The results calculated by computer and the results tested by YG135G instrument was linear related, that means the calculated results by computer can be used to present the evenness of sliver. In this chapter, the effects of speeds of both feed roller and doffer on the evenness of output sliver were also tested, the results showed that the calculated results meet the knowledge very well, it also means the method by this research was reasonable and feasible.
Chapter6is the conclusion and prospect, the contents and results of this research was concluded, the deficiencies were also pointed out, the suggestion and for further research were proposed.
Generally, this research established the computer vision detect system on carded web quality, the method to recognize the neps and trashes in web, the test was fast and conveniently and the results was much accuracy, which offers the base of online test and real-time control in card machine.
引文
[1]Behera B K. Image-processing in Textiles[J]. Textile Progress,2005,35(2/3/4):1-5
[2]Mital A, Govindaraju M, Subramani B. A comparison between manual and hybrid methods in parts inspection[J]. Integrated Manufacturing Systems,1998,9(6):344-349
[3]Malamas E N, Petrakis E G M, Zervakis M, et al. A survey on industrial vision systems, applications and tools[J]. Image and Vision Computing,2003,21(2):171-188
[4]李学佳.数字图像处理技术在纺织检测中的研究和应用[J].测试与分析,2004,9：33-36
[5]丁志坚.棉纤维纺纱棉结杂质的测试分析[J].棉纺织技术,2001,29(3)：159-162
[6]周宇,王兟.清梳过程中棉结与短绒变化分析[J].棉纺织技术,2003,31(8)：453-457
[7]徐曼.浅析现代纺纱生产质量控制技术[J].棉纺织技术,2004,34(4)：225-228
[8]贾云得.机器视觉[M].北京：科学出版社,2000
[9]章毓晋.计算机视觉教程[M].北京：人民邮电出版社,2011
[10]Roberts L G. Machine perception of three-dimensional solids[J]. Optical and Electron-Optical Information Processing, MIT Press,1965:159-197
[11]刘曙光,刘明远.机器视觉及其应用[J].机械制造,2000,38(7)：20-22
[12]孙瑾,顾宏斌.计算机视觉系统框架结构研究[J].计算机工程与应用,2004,40(12)：44-47
[13]林学言,王宏等译.计算机视觉：一种现代的方法[M].北京：电子工业出版社,2004
[14]张铮,王艳平,薛桂香.数字图像处理与机器视觉：Visual C++与Matlab实现[M].北京：人民邮电出版社,2010
[15]Sonka M, Hlavac V, Boyle R. Image processing, analysis and machine vision[M]. USA: Thomson Asia Pet Led, Second Edition,2002
[16]祝世平,申功勋.工件特征点三维坐标非视觉测量方法综述[J].光学精密工程,1997,7(5)：22-29
[17]王红军.机器视觉：现代工业的眼睛[J].机电一体化,1999,3：26-27
[18]赵志强,熊元姣.计算机视觉检测系统的设计方案[J].工业控制计算机,2005,18(10)：1-2
[19]高文,陈熙霖.计算机视觉一算法与系统原理[M].北京：清华大学出版社,1999
[20]Rafael C. Gonzalez, Richard E. Woods著,阮秋琦,阮宇智等译.数字图像处理[M].北京：电子工业出版社,2003
[21]刘传才.图像理解与计算机视觉[M].厦门：厦门大学出版社,2002
[22]Krutz G W, Gibson H G, Cassens D L, et al. Colour vision in forest and wood engineering[J]. Landwards,2000,55(1):2-9
[23]Gonzalez R C, Woods R E. Digital image processing[M]. Beijing:House of Electronics Industry, Sencond Edition,2007
[24]岳晓峰.计算机视觉技术及其在工业中应用的研究[D].长春：吉林大学,2006
[25]李筱松.对梳棉棉网品质的探讨[J].天津纺织科技,1991,3：2-6
[26]赵玉,姜明.转杯纺纱工艺中改善梳棉棉网品质的重要性[J].棉纺织技术,1995,23(9)：25-27
[27]邵东锋,袁辉.纺织加工中棉结问题的研究[J].北京纺织,2005,26(3)：30-33
[28]周宇,王兟.清梳过程中棉结与短绒变化分析[J].棉纺织技术,2003,31(8)：5-9
[29]Hebert J J.棉结的剖析[J].国外纺织技术—纺织分册,1989,4：3-4
[30]魏霞.棉花杂质检验中应注意的几个问题[J].中国棉花加工,2006,4：34-35
[31]廖声海.对杂乱成网均匀度及杂乱程度的工艺探讨[J].非织造布,1999,12(1)：23-26
[32]秦贞俊.现代纺纱工程中棉结问题的讨论[J].棉纺织技术,2004,32(3)：29-34
[33]Natusisk M, Frydrych I, Hecquet E. Assessment methods and changes of the number at seed coat neps during processing[J]. Melliand English,2001,83(1-2):7-8
[34]Dipl-lng. High quality card[J]. Textile Bulletin,2000,4:54-55
[35]Subramanian K. Spinning studies on fibre maturity and its importance[J]. Textile Bulletin, 2001,3:44-46
[36]房英杰.控制成纱棉结杂质的措施[J].广西纺织科技,2005,34(4)：31-32
[37]刘荣清.棉纺规律性条干不匀的检测[J].棉纺织技术.2005,33(9)：31-35
[38]王俊英.梳棉棉网清晰度对成纱质量的影响[J].上海纺织科技.2008,36(11)：55-56
[39]晏旭等译.高级棉结数量测定仪[J].中国棉花加工,2001,1：42-44
[40]周松.检验棉网上棉结杂质的新方法[J].棉纺织技术,1992,20(4)：27-30
[41]吕善模.国外几种自动检测棉结、短纤维率的仪器简介[J].中国纤检,2005,5：34-37
[42]吕善模.国外几种自动检测棉结、短纤维率的仪器简介(续一)[J].中国纤检,2005,6：44-46
[43]吕善模.国外几种自动检测棉结、短纤维率的仪器简介(续二)[J].中国纤检,2005,7：38-41
[44]王婉南,王卫权,王文杰.基于小波变换的纱线棉结的识别与分类[J].西安交通大学学报,1996,30(9)：65-70
[45]宋新平.细纱棉结计算机检测系统的研究[J].棉纺织技术,2004,4：28-30
[46]http://search.sipo.gov.cn/sipo/zljs/hyjs-jieguo.jsp
[47]http://www.microview.com.cn/applications/industry/5.html
[48]Taylor R A. Estimating the size of cotton trash with video images[J]. Textile Research Journal, 1990,60(4):185-193
[49]Lieberman M A, Patil R B. Clustering and neural networks to categorize cotton trash[J]. Optical Engineering,1994,33(5):1642-1653
[50]Lieberman M A, Patil R B. Evaluation of learning vector quantization to classify cotton trash[J]. Optical Engineering,1997,36(3):914-921
[51]Xu B G, Fang C, Huang R, et al. Chromatic image analysis for cotton trash and color measurements [J]. Textile Research Journal,1997,67(12):881-890
[52]Xu B, Fang C, Watson M D. Clustering analysis for cotton trash classification[J]. Textile Research Journal,1999,69(9):656-662
[53]Lieberman M A, Michael A L, Charles K. B, et al. Determining gravimetric bark content in cotton with machine vision[J]. Textile Research Journal,1998,68(2):94-104
[54]Tae J K, Kim S C. Objective evaluation of the trash and color of raw cotton by image procesing and neural network[J]. Textile Research Journal,2002,72(9):776-782
[55]金守峰,袁建畅,张慧.基于Matlab的棉花异性纤维检测及定位算法[J].中国棉花加工,2004(4)：29-30
[56]符宝鼎,袁建畅,郭彩霞.基于RGB颜色模型棉花杂质的识别算法[J].北京纺织,2005,26(5)：48-51
[57]李碧丹,丁天怀,郏东耀.皮棉异性纤维剔除系统设计[J].农业机械学报,2006,37(1)：107-110
[58]赵燕,袁建畅.原棉异物在线检测系统剔除机构的分析研究[J].上海纺织科技,2006,34(1)：21-22,27
[59]郭彩霞,袁建畅,金守峰.基于BP网络的棉花中异性纤维识别算法[J].西安工程科技学院学报,2006,20(5)：542-544
[60]郏东耀,丁天怀.纤维红外吸收特性及其在皮棉杂质检测中的应用[J].农业工程学报,2004,20(3)：104-108
[61]郏东耀,丁天怀.利用纤维红外吸收特性的皮棉杂质检测新方法[J].红外与毫米波学报,2005,24(2)：147-150
[62]郏东耀,丁天怀.棉花中异性纤维的多光谱检测[J].清华大学学报：自然科学版,2005,45(2)：193-196
[63]郏东耀,丁天怀.检测皮棉内部杂质的透射成像影响因素分析[J].农业机械学报,2005,36(2)：65-69
[64]郏东耀,丁天怀.皮棉杂质透射检测及成像目标增强方法[J].物理学报,2005,54(9)：4058-4064
[65]罗德坡,朱邦太.紫外线荧光效应及其在棉花异性纤维分拣系统中的应用[J].河南科技大学学报：自然科学版,2007,28(2)：63-67
[66]丁天怀,郏东耀.利用多颜色空间特征融合方法检测近似目标[J].清华大学学报：自然科学版,2006,46(2)：176-179
[67]许鹤群等.CPPS-85图像处理系统及其在纺织上的应用.中国纺织大学学报,1989,15(5)：1-6
[68]刘明,李政.梳棉棉网中棉结检测图象识别方法探讨[J].上海工程技术大学学报,1991,5(4)：29-33
[69]刘明.棉网均匀度检测中的图像识别方法[J].中国纺织大学学报,1990,16(4)：97-101
[70]林毅,沈天飞.棉网均匀度的多点动态检测与单板机数据处理[J].纺织学报,1990,11(9)：22-23
[71]黄健,许鹤群.棉网质量检测的计算机辅助分析系统[J].中国纺织大学学报,1996,22(3)：58-62
[72]俞晓磊,赵志敏,陈远,郭林峰,汪东华.基于机器视觉的梳棉机棉结在线检测[J].传感器与微系统,2008,27(7)：96-98
[73] http://patft.uspto.gov/netacgi/nph-Parser?Sectl=PT02&Sect2=HITOFF&p=1&u=%2Fnetahtml% 2FPTO%2Fsearch-bool.html&r=4&f=G&I=50&co 1=AND&d=PTXT&s 1=6621915&OS=662191 5&RS=6621915
[74]于爱春.运用NCT在线检测仪优化梳棉工艺[J].棉纺织技术,2006,34(1)：9-12
[75]穆征,张冶.TC03型梳棉机的在线监控技术[J].棉纺织技术,2005,33(7)：62-64.
[76]周衡书.现代清梳联技术与生条质量控制[D].苏州大学,2004
[77]陈俊琰,郁崇文,孙见原等.实验用小型清梳联合机：中国,200820058693.X[P],2009.03.04
[78]王庆有.图像传感器应用技术[M].北京：电子工业出版社,2006
[79]林竹君,杨飒.CCD非接触检测技术在工业生产中的实际应用[J].河北工业科技,2009,26(5)：335-338,344
[80]申晓彦,王鉴.用于视觉检测的光源照明系统分析[J].灯与照明,2009,3：22-23
[81]俞晓磊,赵志敏,汪东华.棉结在线检测系统中的光源选择[J].中国激光,2008,35(5)：788-791
[82]蔡文贵,李永远,徐振华.CCD技术及应用[M].北京：电子工业出版社,1992
[83]Faraji H, James W, Lean M. CCD Noise removal in digital images [J]. IEEE Transactions on Image Processing,2006,15(9):2676-2685
[84]Janesick J R. Scientific charge-coupled devices[M]. Bellingham, Washington, USA:SPIE Press,2001
[85]Martin G, Pattichis M S. The characterization of scanning noise and quantization on texture feature analysis[C]. Proceedings of the 6th IEEE Southwest Symposium on Image Analysis and Interpretation,2004:152-156
[86]何明一,卫保国.数字图像处理[M].科学出版社.2008,8：102-146
[87]杨杰,付忠良,阮波.照度不均匀图像的快速自适应灰度修正[J].计算机应用,2005,25(3)：598-600,602
[88]王娜,曾平,孔月萍.CCD扫描噪声建模及自适应滤波算法[J].计算机工程,2008,30(24)：244-246
[89]方莉,张萍.经典图像去噪算法研究综述[J].工业控制计算机,2010,23(11)：73-74
[90]李伟.基于匹配跟踪和自适应字典的图像去噪算法研究[D].华北电力大学,2010
[91]薛存金,温晓阳,董庆.SAR图像的自适应边缘保持去噪滤波[J].应用科学学报,2011.1,29(1)：44-50
[92]王文中.梳棉工艺与成纱棉结的关系[J].纺织科技进展,2004,5：50-51
[93]陈爱军,董光辉,夏宇.数字图像处理及其MATLAB实现[M].东北林业大学出版社,
[94]Otsu N. A threshold selection method from gray-level histogram[J]. IEEE Transactions on Systems, Man and Cybernetics,1979,9(1):62-66
[95]李美玲.基于计算机视觉的棉网质量检测系统的研究[D].东华大学,2010
[96]Haralick R M. Statistical and structural approaches to texture[J]. Proceedings of the IEEE,1975,67(5):786-804
[97]Bharati M H, Liu J J, Mac G. Image texture analysis:Methods and comparisons[J]. Chemo-metrics and Intelligent Laboratory Systems,2004,72(1):57-71.
[98]Baraldi A, Parmiggian F. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters[J]. IEEE Transactions on Geoseience and Remote Sensing,1995,33(2):293-304
[99]Haralick R M, Shanmugam K, Dinstein I. Textual features for image classification. IEEE Transactions on Systems, Man, and Cybernetics,1973,3(6):610-621
[100]Huber R, Dutra L V. Feature selection for ERS-1/2 InSAR classification:high dimensionality case[C].1998 IEEE International Geoscience and Remote Sensing Symposium Proceedings (IGARSS'98),1998,3:1605-1607
[101]Leen-Kiat Soh, Tsatsoulis C. Segmentation of satellite imagery of natural scenes using data mining[J]. IEEE Transactions on Geoscience and Remote Sensing,1999,37(2):1086-1099
[2]Mital A, Govindaraju M, Subramani B. A comparison between manual and hybrid methods in parts inspection[J]. Integrated Manufacturing Systems,1998,9(6):344-349
[3]Malamas E N, Petrakis E G M, Zervakis M, et al. A survey on industrial vision systems, applications and tools[J]. Image and Vision Computing,2003,21(2):171-188
[4]李学佳.数字图像处理技术在纺织检测中的研究和应用[J].测试与分析,2004,9：33-36
[5]丁志坚.棉纤维纺纱棉结杂质的测试分析[J].棉纺织技术,2001,29(3)：159-162
[6]周宇,王兟.清梳过程中棉结与短绒变化分析[J].棉纺织技术,2003,31(8)：453-457
[7]徐曼.浅析现代纺纱生产质量控制技术[J].棉纺织技术,2004,34(4)：225-228
[8]贾云得.机器视觉[M].北京：科学出版社,2000
[9]章毓晋.计算机视觉教程[M].北京：人民邮电出版社,2011
[10]Roberts L G. Machine perception of three-dimensional solids[J]. Optical and Electron-Optical Information Processing, MIT Press,1965:159-197
[11]刘曙光,刘明远.机器视觉及其应用[J].机械制造,2000,38(7)：20-22
[12]孙瑾,顾宏斌.计算机视觉系统框架结构研究[J].计算机工程与应用,2004,40(12)：44-47
[13]林学言,王宏等译.计算机视觉：一种现代的方法[M].北京：电子工业出版社,2004
[14]张铮,王艳平,薛桂香.数字图像处理与机器视觉：Visual C++与Matlab实现[M].北京：人民邮电出版社,2010
[15]Sonka M, Hlavac V, Boyle R. Image processing, analysis and machine vision[M]. USA: Thomson Asia Pet Led, Second Edition,2002
[16]祝世平,申功勋.工件特征点三维坐标非视觉测量方法综述[J].光学精密工程,1997,7(5)：22-29
[17]王红军.机器视觉：现代工业的眼睛[J].机电一体化,1999,3：26-27
[18]赵志强,熊元姣.计算机视觉检测系统的设计方案[J].工业控制计算机,2005,18(10)：1-2
[19]高文,陈熙霖.计算机视觉一算法与系统原理[M].北京：清华大学出版社,1999
[20]Rafael C. Gonzalez, Richard E. Woods著,阮秋琦,阮宇智等译.数字图像处理[M].北京：电子工业出版社,2003
[21]刘传才.图像理解与计算机视觉[M].厦门：厦门大学出版社,2002
[22]Krutz G W, Gibson H G, Cassens D L, et al. Colour vision in forest and wood engineering[J]. Landwards,2000,55(1):2-9
[23]Gonzalez R C, Woods R E. Digital image processing[M]. Beijing:House of Electronics Industry, Sencond Edition,2007
[24]岳晓峰.计算机视觉技术及其在工业中应用的研究[D].长春：吉林大学,2006
[25]李筱松.对梳棉棉网品质的探讨[J].天津纺织科技,1991,3：2-6
[26]赵玉,姜明.转杯纺纱工艺中改善梳棉棉网品质的重要性[J].棉纺织技术,1995,23(9)：25-27
[27]邵东锋,袁辉.纺织加工中棉结问题的研究[J].北京纺织,2005,26(3)：30-33
[28]周宇,王兟.清梳过程中棉结与短绒变化分析[J].棉纺织技术,2003,31(8)：5-9
[29]Hebert J J.棉结的剖析[J].国外纺织技术—纺织分册,1989,4：3-4
[30]魏霞.棉花杂质检验中应注意的几个问题[J].中国棉花加工,2006,4：34-35
[31]廖声海.对杂乱成网均匀度及杂乱程度的工艺探讨[J].非织造布,1999,12(1)：23-26
[32]秦贞俊.现代纺纱工程中棉结问题的讨论[J].棉纺织技术,2004,32(3)：29-34
[33]Natusisk M, Frydrych I, Hecquet E. Assessment methods and changes of the number at seed coat neps during processing[J]. Melliand English,2001,83(1-2):7-8
[34]Dipl-lng. High quality card[J]. Textile Bulletin,2000,4:54-55
[35]Subramanian K. Spinning studies on fibre maturity and its importance[J]. Textile Bulletin, 2001,3:44-46
[36]房英杰.控制成纱棉结杂质的措施[J].广西纺织科技,2005,34(4)：31-32
[37]刘荣清.棉纺规律性条干不匀的检测[J].棉纺织技术.2005,33(9)：31-35
[38]王俊英.梳棉棉网清晰度对成纱质量的影响[J].上海纺织科技.2008,36(11)：55-56
[39]晏旭等译.高级棉结数量测定仪[J].中国棉花加工,2001,1：42-44
[40]周松.检验棉网上棉结杂质的新方法[J].棉纺织技术,1992,20(4)：27-30
[41]吕善模.国外几种自动检测棉结、短纤维率的仪器简介[J].中国纤检,2005,5：34-37
[42]吕善模.国外几种自动检测棉结、短纤维率的仪器简介(续一)[J].中国纤检,2005,6：44-46
[43]吕善模.国外几种自动检测棉结、短纤维率的仪器简介(续二)[J].中国纤检,2005,7：38-41
[44]王婉南,王卫权,王文杰.基于小波变换的纱线棉结的识别与分类[J].西安交通大学学报,1996,30(9)：65-70
[45]宋新平.细纱棉结计算机检测系统的研究[J].棉纺织技术,2004,4：28-30
[46]http://search.sipo.gov.cn/sipo/zljs/hyjs-jieguo.jsp
[47]http://www.microview.com.cn/applications/industry/5.html
[48]Taylor R A. Estimating the size of cotton trash with video images[J]. Textile Research Journal, 1990,60(4):185-193
[49]Lieberman M A, Patil R B. Clustering and neural networks to categorize cotton trash[J]. Optical Engineering,1994,33(5):1642-1653
[50]Lieberman M A, Patil R B. Evaluation of learning vector quantization to classify cotton trash[J]. Optical Engineering,1997,36(3):914-921
[51]Xu B G, Fang C, Huang R, et al. Chromatic image analysis for cotton trash and color measurements [J]. Textile Research Journal,1997,67(12):881-890
[52]Xu B, Fang C, Watson M D. Clustering analysis for cotton trash classification[J]. Textile Research Journal,1999,69(9):656-662
[53]Lieberman M A, Michael A L, Charles K. B, et al. Determining gravimetric bark content in cotton with machine vision[J]. Textile Research Journal,1998,68(2):94-104
[54]Tae J K, Kim S C. Objective evaluation of the trash and color of raw cotton by image procesing and neural network[J]. Textile Research Journal,2002,72(9):776-782
[55]金守峰,袁建畅,张慧.基于Matlab的棉花异性纤维检测及定位算法[J].中国棉花加工,2004(4)：29-30
[56]符宝鼎,袁建畅,郭彩霞.基于RGB颜色模型棉花杂质的识别算法[J].北京纺织,2005,26(5)：48-51
[57]李碧丹,丁天怀,郏东耀.皮棉异性纤维剔除系统设计[J].农业机械学报,2006,37(1)：107-110
[58]赵燕,袁建畅.原棉异物在线检测系统剔除机构的分析研究[J].上海纺织科技,2006,34(1)：21-22,27
[59]郭彩霞,袁建畅,金守峰.基于BP网络的棉花中异性纤维识别算法[J].西安工程科技学院学报,2006,20(5)：542-544
[60]郏东耀,丁天怀.纤维红外吸收特性及其在皮棉杂质检测中的应用[J].农业工程学报,2004,20(3)：104-108
[61]郏东耀,丁天怀.利用纤维红外吸收特性的皮棉杂质检测新方法[J].红外与毫米波学报,2005,24(2)：147-150
[62]郏东耀,丁天怀.棉花中异性纤维的多光谱检测[J].清华大学学报：自然科学版,2005,45(2)：193-196
[63]郏东耀,丁天怀.检测皮棉内部杂质的透射成像影响因素分析[J].农业机械学报,2005,36(2)：65-69
[64]郏东耀,丁天怀.皮棉杂质透射检测及成像目标增强方法[J].物理学报,2005,54(9)：4058-4064
[65]罗德坡,朱邦太.紫外线荧光效应及其在棉花异性纤维分拣系统中的应用[J].河南科技大学学报：自然科学版,2007,28(2)：63-67
[66]丁天怀,郏东耀.利用多颜色空间特征融合方法检测近似目标[J].清华大学学报：自然科学版,2006,46(2)：176-179
[67]许鹤群等.CPPS-85图像处理系统及其在纺织上的应用.中国纺织大学学报,1989,15(5)：1-6
[68]刘明,李政.梳棉棉网中棉结检测图象识别方法探讨[J].上海工程技术大学学报,1991,5(4)：29-33
[69]刘明.棉网均匀度检测中的图像识别方法[J].中国纺织大学学报,1990,16(4)：97-101
[70]林毅,沈天飞.棉网均匀度的多点动态检测与单板机数据处理[J].纺织学报,1990,11(9)：22-23
[71]黄健,许鹤群.棉网质量检测的计算机辅助分析系统[J].中国纺织大学学报,1996,22(3)：58-62
[72]俞晓磊,赵志敏,陈远,郭林峰,汪东华.基于机器视觉的梳棉机棉结在线检测[J].传感器与微系统,2008,27(7)：96-98
[73] http://patft.uspto.gov/netacgi/nph-Parser?Sectl=PT02&Sect2=HITOFF&p=1&u=%2Fnetahtml% 2FPTO%2Fsearch-bool.html&r=4&f=G&I=50&co 1=AND&d=PTXT&s 1=6621915&OS=662191 5&RS=6621915
[74]于爱春.运用NCT在线检测仪优化梳棉工艺[J].棉纺织技术,2006,34(1)：9-12
[75]穆征,张冶.TC03型梳棉机的在线监控技术[J].棉纺织技术,2005,33(7)：62-64.
[76]周衡书.现代清梳联技术与生条质量控制[D].苏州大学,2004
[77]陈俊琰,郁崇文,孙见原等.实验用小型清梳联合机：中国,200820058693.X[P],2009.03.04
[78]王庆有.图像传感器应用技术[M].北京：电子工业出版社,2006
[79]林竹君,杨飒.CCD非接触检测技术在工业生产中的实际应用[J].河北工业科技,2009,26(5)：335-338,344
[80]申晓彦,王鉴.用于视觉检测的光源照明系统分析[J].灯与照明,2009,3：22-23
[81]俞晓磊,赵志敏,汪东华.棉结在线检测系统中的光源选择[J].中国激光,2008,35(5)：788-791
[82]蔡文贵,李永远,徐振华.CCD技术及应用[M].北京：电子工业出版社,1992
[83]Faraji H, James W, Lean M. CCD Noise removal in digital images [J]. IEEE Transactions on Image Processing,2006,15(9):2676-2685
[84]Janesick J R. Scientific charge-coupled devices[M]. Bellingham, Washington, USA:SPIE Press,2001
[85]Martin G, Pattichis M S. The characterization of scanning noise and quantization on texture feature analysis[C]. Proceedings of the 6th IEEE Southwest Symposium on Image Analysis and Interpretation,2004:152-156
[86]何明一,卫保国.数字图像处理[M].科学出版社.2008,8：102-146
[87]杨杰,付忠良,阮波.照度不均匀图像的快速自适应灰度修正[J].计算机应用,2005,25(3)：598-600,602
[88]王娜,曾平,孔月萍.CCD扫描噪声建模及自适应滤波算法[J].计算机工程,2008,30(24)：244-246
[89]方莉,张萍.经典图像去噪算法研究综述[J].工业控制计算机,2010,23(11)：73-74
[90]李伟.基于匹配跟踪和自适应字典的图像去噪算法研究[D].华北电力大学,2010
[91]薛存金,温晓阳,董庆.SAR图像的自适应边缘保持去噪滤波[J].应用科学学报,2011.1,29(1)：44-50
[92]王文中.梳棉工艺与成纱棉结的关系[J].纺织科技进展,2004,5：50-51
[93]陈爱军,董光辉,夏宇.数字图像处理及其MATLAB实现[M].东北林业大学出版社,
[94]Otsu N. A threshold selection method from gray-level histogram[J]. IEEE Transactions on Systems, Man and Cybernetics,1979,9(1):62-66
[95]李美玲.基于计算机视觉的棉网质量检测系统的研究[D].东华大学,2010
[96]Haralick R M. Statistical and structural approaches to texture[J]. Proceedings of the IEEE,1975,67(5):786-804
[97]Bharati M H, Liu J J, Mac G. Image texture analysis:Methods and comparisons[J]. Chemo-metrics and Intelligent Laboratory Systems,2004,72(1):57-71.
[98]Baraldi A, Parmiggian F. An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters[J]. IEEE Transactions on Geoseience and Remote Sensing,1995,33(2):293-304
[99]Haralick R M, Shanmugam K, Dinstein I. Textual features for image classification. IEEE Transactions on Systems, Man, and Cybernetics,1973,3(6):610-621
[100]Huber R, Dutra L V. Feature selection for ERS-1/2 InSAR classification:high dimensionality case[C].1998 IEEE International Geoscience and Remote Sensing Symposium Proceedings (IGARSS'98),1998,3:1605-1607
[101]Leen-Kiat Soh, Tsatsoulis C. Segmentation of satellite imagery of natural scenes using data mining[J]. IEEE Transactions on Geoscience and Remote Sensing,1999,37(2):1086-1099