基于图像分析的织物起毛起球自动评级研究
|
摘要
织物起毛起球是指织物在穿着、洗涤过程中由于摩擦在其表面产生绒毛和颗粒小球的现象。织物表面起毛起球不仅破坏织物外观,还会恶化织物触感,影响织物的服用性能,因此织物的抗起毛起球性能是评价织物质量的重要指标之一。现有的基于标准样照比对的织物起毛起球等级评定方法,评定过程依赖于检测人员的视觉判断,具有较强的主观性和随机性等缺陷,为了实现织物起毛起球评级的客观性和可靠性,本文提出基于数字图像处理的方法实现织物起毛起球等级的自动评定,进而构建织物起毛起球自动评级系统。
本论文的主要研究内容包括：起毛起球识别过程中织物结构纹理检测；基于傅里叶变换的织物起毛起球信息提取；基于时域Gabor变换的织物起毛起球分析；基于频域Gabor滤波方法的织物起毛起球分析；织物起毛起球的特征参数分析及评级方法的研究；织物起毛起球自动评级系统的构建。
第一章简要介绍了论文的选题背景及意义。首先介绍了常用模拟织物起毛起球的方法以及现有的起毛起球性能的评价方法,然后概述了国内外有关基于计算机图像处理技术的织物起毛起球评级研究现状,最后分析了现有的基于图像处理的织物起毛起球评级研究中的不足,并阐述了本论文的研究目标、研究内容和主要创新点。
第二章主要介绍了起毛起球识别过程中织物结构纹理的检测方法。文中首先介绍了相关的织物结构参数的含义,在分析这些结构参数与织物起毛起球图像构成关系的基础上,确定织物密度和织物组织循环数作为影响织物纹理的主要结构参数。对于织物密度的自动检测,首先给出了利用灰度投影法检测织物密度的过程,为了消除噪声信号的影响,对织物的傅里叶变换频谱图进行分析,提出在频域内选定代表织物经纱周期和纬纱周期的信号进行滤波,实现经纬纱的分离。然后再对分离后的子图像利用灰度投影法实现经纬纱的定位和密度测量。对于织物组织循环数的检测,首先利用织物密度的检测结果,将织物图像按照纱线的位置进行分离,然后沿着纱线纵向投影获得纱线表征信号,通过检测不同纱线灰度信号的相关性,得到织物组织循环数。
第三章给出了利用傅里叶变换方法在频域内实现毛球信息提取的方法。文中首先介绍了织物图像上的灰度投影规律,根据纱线上亮度信号符合正弦分布的特点,提出利用傅里叶变换对织物图像进行处理,滤除织物周期性纹理信号；然后介绍了基于平板扫描仪采集起毛起球织物和样照图像的方法,并对GB/T4802.3中光面精梳毛机织物起毛起球标准样照图像进行了分析,从傅里叶变换的幅值谱中选择代表织物周期性纹理的信号,结合阈值法滤除织物纹理,再利用低频保留的方法对织物毛球信息进行增强,扩大毛球与背景的反差；最后利用基于局部增强的Otsu阈值方法对织物毛球进行自动分割。五个等级的织物起毛起球标准样照中的毛球分割实例证明基于傅里叶变换的方法可以实现毛球的自动提取。
第四章提出采用时域Gabor滤波的方法对起毛起球织物图像进行了分析。文中首先介绍了Gabor变换的含义,给出了所采用的二维时域Gabor滤波方法；然后结合典型的Gabor滤波器,对Gabor滤波器的参数,包括方向参数、比例参数、频率参数、尺度参数以及滤波器窗口大小的选择方法进行了分析,其中滤波器窗口大小须根据织物组织循环区域确定；接着根据Gabor滤波器的频率参数和方向参数,建立了16个Gabor滤波器组成的Gabor滤波器簇。为了得到理想的Gabor滤波结果,讨论了最优Gabor滤波器的选择标准和Gabor滤波结果的融合方法,考虑到毛球分布具有多方向性的特点,提出将同一频率参数对应的多方向滤波结果进行融合。在Gabor滤波和融合结果的基础上,提出利用图像均值和标准差构建双阈值的方法对滤波结果进行阈值分割,从而定位毛球。最后对不同等级的起毛起球织物图像进行Gabor滤波,实际滤波效果证明运用Gabor滤波方法可以实现织物结构纹理的滤除,同时能对毛球信息增强。对于在滤波处理后的图像中,只要利用简单的阈值方法就可以实现毛球的准确分割。
第五章提出采用频域Gabor滤波的方法对起毛起球织物图像进行了处理。文中首先介绍了频域Gabor变换的含义,并对频域Gabor滤波方法进行了叙述；接着给出了频域Gabor滤波器的实现方法,讨论了频域Gabor滤波器参数,包括尺度参数、比例参数、坐标参数对频域Gabor滤波器性能的影响,并给出了选取各个参数的基本原则；为了得到理想的Gabor滤波结果,首先利用不同的Gabor滤波器对织物图像进行滤波,根据滤波结果提出将同一尺度的各方向滤波器进行融合,以消除滤波结果中毛球信息具有方向性的特征；再分别根据峰点滤波和织物密度检测结果,给出了用于织物起毛起球提取的Gabor滤波器选择方案。在滤波结果的基础上,提出基于局部窗口的阈值方法进行织物毛球分割。最后对不同等级的起毛起球织物图像和实际起毛起球织物图像进行频域Gabor滤波,实际滤波效果证明运用频域Gabor滤波方法可以实现织物结构纹理的滤除,并对毛球信息增强。与时域Gabor滤波的方法相比,基于频域Gabor变换的方法中,可根据织物结构参数便捷的选择滤波器参数,达到更好的滤波效果,实现织物毛球信息的准确提取。
第六章介绍了表征织物起毛起球程度的特征参数及其织物起毛起球评级方法,并构建了起毛起球评级系统。首先在毛球分割的基础上,结合统计方法完成起毛起球特征参数的提取,选择了毛球面积参数、毛球总数、毛球平均密度等作为起毛起球等级评估的标准,然后在分析各参数与起毛起球等级之间关系的基础上,选择毛球总面积参数作为起毛起球等级评定标准。为了消除图像放大倍率的影响,最终采用毛球总面积所占比例作为毛球总面积指标。根据不同等级样照上的毛球总面积比例指标,提出两种方法实现起毛球等级的评定,第一种方法是采用插值法确定不同等级之间的分界值,然后根据实际织物的毛球总面积比例确定起毛起球等级；第二种方法是利用拟合方法得出毛球总面积比例与起毛起球等级之间的函数关系,然后根据实际织物的毛球总面积比例数值直接计算出起毛起球等级。在本章的后一部分构建了起毛起球评级系统,该系统选择德国Basler公司IEEE1394接口SCA 1600型工业相机作为图像采集的硬件,采用Visual Basic 6.0和美国国家仪器公司的IMAQ插件作为软件来构建织物起毛起球自动评级系统,实现了整个起毛起球评级系统的各项功能,并提供了软件的参考界面。
第七章对全文进行了总结和展望。介绍了本课题研究的主要贡献以及所存在的不足问题,对织物起毛起球自动评级的进一步研究提出建议。
During the wearing and washing, pills caused by friction will be appeared on the fabric surface. The pills on the fabric surface not only damage the fabric appearance, but also deteriorate the touching of fabric. This will damage the wearing properties of fabric. During the inspection of fabric or clothing, the resistance of fabric pilling is adopted as one of the important measurement properties for evaluating fabric quality. The existing grade evaluation method for pilling fabric based on standard photos of pilling fabric, is relied on the human vision. The inspection process has the flaws of subjective and the grade evaluation result is partly random. To make the grade evaluation of fabric pilling to be objective and reliable, a novel method for automatic evaluate the grade of fabric pilling is proposed in this paper. The research devotes to development an automatic grade evaluation system for fabric pilling.
The research of this paper includes major contents as follows:automatic recognition of fabric structure parameter for grade evaluation of fabric pilling; the fabric pill information extraction based on Fourier transform; automatic analysis of fabric pills with Gabor transform in time-domain; fabric pills detection with Gabor transform in frequency-domain; characteristic parameters extraction for evaluating fabric pilling grade; the research and discussion of fabric pilling evaluation method; the development of automatic fabric pilling evaluation system.
In Chapter 1 the background and significance of the topic selection of the thesis is introduced briefly. First, The simulation methods of fabric pilling caused by friction during wearing and washing in the lab are introduced and the existing research content and analysis method are discussed. Then, the research of the grade evaluation for fabric pilling based on digital image analysis home and abroad is summarized. The shortcomings of the automatic grade evaluation of fabric pilling based on image processing are analyzed in conclusion. The research objects, content and major innovations of this paper are given at the end of this chapter.
In Chapter 2 the automatic recognition process of fabric structure texture parameters used for evaluating the fabric pilling are discussed. First, the definitions of relative fabric structure parameters are introduced. Based on the discussion of the relation between the parameters and composition of pill.ng fabric image, the fabric density and.he number of fabric weave repeat are chosen as the parameters for determining the structure composition of fabric image. The inspection of fabric density with gray-projection method is discussed and the procedure of the inspection is given. To smooth up the affection of noise information, the fabric image is transformed into frequency-domain with Fourier transform. The amplitude spectrum of fabric image is then obtained and analyzed. The filtering method for separating the warp and weft yarns in the frequency-domain is proposed by selecting the region representing the period information of warp and weft in the fabric image. In the sub-image including separation yarns, gray-projection method is used to locate the warp and weft yarn and the fabric density can be measured. To detect the number of fabric weave repeat, the yarns in the fabric image are segmented based on the fabric density detection result. The projection information along the longitudinal direction is detected to characterize the separating yarn. The number of fabric weave repeat can be obtained by calculating the relation of the gray information of different yarns.
In Chapter 3 the extraction method of fabric pills with Fourier transform method is given. The gray projection character in fabric image is introduced first and the gray intensity in the yarn obeys the sine curve. Based on this, Fourier transform which is based on sine function is proposed to analyze the fabric image. It is then selected to remove the periodic signals in the fabric image. The image acquisition method with a flat scanner of pilling fabric is given and the standard pilling image of wool woven fabric in GB/T4802.3 is chosen as the analysis object. The periodic signals in the fabric image are chosen with the help of amplitude spectrum obtained by Fourier transform and they are eliminated by threshold segmentation in the frequency-domain. The low frequency signals are retained to enhance the fabric pill information. The fabric image with enough difference between the background and the pill are got with the Fourier analysis method. The enhanced local Otsu threshold method is last adopted to segment the fabric pills automatically. The segmentation for standard pilling fabric image of five grades proves that the method pills based Fourier transform can be used for extracting the fabric pills.
In Chapter 4 the method of extracting pills in pilling fabric image based on Gabor transform in time-domain is discussed. The definition of Gabor transform is introduced first and the 2D Gabor filtering method in time-domain is explained. The parameters of Gabor filter, including the direction parameter, proportion parameter, frequency parameter, scale parameter and the window size of Gabor filter are then described with typical Gabor filters and the selection way of these parameters is given then. The size of the Gabor filter is determined by the region of the fabric structure in the fabric image. Based on the frequency parameter and direction parameter, a filter bank including 16 Gabor filters is constructed. To obtain satisfied Gabor filtering result, the selecting rule of optimal Gabor filters is given by calculating the mean and standard deviation of filtering result. The fusion of Gabor filtering result is carried out by fusion of the Gabor filtering results of different directions in the same frequency. The direction of fabric pills in the filtering result can be then eliminated. Based on the Gabor filtering and fusion result image, dual threshold values constructed by the mean and standard deviation of the result image are used to segment the fabric image. The pills can be located with threshold segmentation. The fabric pilling images of different grades are filtered by the proposed Gabor filters. The filtering results proved that the Gabor filters used can eliminate the fabric structure textures and enhance the fabric pill information. In the filtering image, the fabric pills can be segmented with simple threshold method.
In Chapter 5 the method of extracting pills in pilling fabric image based on Gabor transform in frequency-domain is discussed. The definition of Gabor transform in frequency-domain is first introduced in the chapter and the filtering way for image of Gabor transform in frequency-domain is explained. The realization method of Gabor transform in frequency-domain is then given and the parameters, including the scale parameter, the proportion parameter and the coordinate parameter, are discussed with the affection to the character of Gabor filters. The basis selection rules of these parameters are given then. To get satisfied Gabor filtering results, the different Gabor filters are adopted to filter the given pilling fabric image. Based on the filtering results, the coordinate parameter is selected and the results in different directions are fused to remove the direction affection of fabric pills. The selection rule of Gabor filter used for extracting the fabric pills is then given by the peak filtering method and the fabric density detection results. In the filtering result, local threshold method is chosen to locate and segment the fabric pills. The fabric pilling images of different grades are filtered by the proposed Gabor filters in frequency domain. The filtering results proved that the Gabor filters used in this chapter can eliminate the fabric structure textures and enhance the fabric pill information. Compared to the Gabor filtering method in time-domain, the Gabor filters in frequency-domain selects the filtering parameters based on the fabric weave structure parameters. The better filtering result can be obtained and the pill information in the fabric image is extracted accurately.
In Chapter 6 the characteristic parameters of fabric pilling and the method used for evaluating the fabric pilling grade are discussed. The pilling grade evaluation system constructed is also introduced. Based on the pill segmentation results, the characteristic parameters can be calculated with statistical method. Five parameters, including the area of fabric pills, the number of fabric pills and the density of fabric pill, are adopted to evaluate the fabric pilling degree. The relation between the parameters and the fabric pilling grades is analyzed and the total area of fabric pills is selected as the evaluation standard for fabric pilling. To eliminate the magnify affection of fabric image, the scale of the total area of fabric pills to the area of fabric image is chosen as the evaluation index. Based on the scale of total area of fabric pills, two methods are used to assess the fabric pilling grade. In the first method, the cutoff values between different grades are calculated with interpolation. The fabric pilling grade can be then determined based on the scale of total area of fabric pills to the area of fabric image. The second method is to assure the function relation between the scale of total area of fabric pills and the fabric pilling grades. The scale of total area of fabric pills can be used as a parameter to calculate the fabric pilling grade directly. In the latter part of this chapter, the construction of grade evaluation system for fabric pilling is explained. The hardware and software of the system is introduced. The industrial camera SCA 1600 produced by Basler corporation in German with IEEE 1394 port is selected as the hardware of image acquisition. Visual Basic 6.0 and the IMAQ ActiveX of American National Instruments are chosen as the software to construct the fabric pilling automatic evaluation system. The realization for the functions of the fabric pilling evaluation system is then briefly introduced and the reference interface of the system is provided at last.
In Chapter 7 a summary is made to describe the main contributions and the problems of the present work. The advice of the future work of the automatic evaluation of fabric pilling based on image processing is given at last.
本论文的主要研究内容包括：起毛起球识别过程中织物结构纹理检测；基于傅里叶变换的织物起毛起球信息提取；基于时域Gabor变换的织物起毛起球分析；基于频域Gabor滤波方法的织物起毛起球分析；织物起毛起球的特征参数分析及评级方法的研究；织物起毛起球自动评级系统的构建。
第一章简要介绍了论文的选题背景及意义。首先介绍了常用模拟织物起毛起球的方法以及现有的起毛起球性能的评价方法,然后概述了国内外有关基于计算机图像处理技术的织物起毛起球评级研究现状,最后分析了现有的基于图像处理的织物起毛起球评级研究中的不足,并阐述了本论文的研究目标、研究内容和主要创新点。
第二章主要介绍了起毛起球识别过程中织物结构纹理的检测方法。文中首先介绍了相关的织物结构参数的含义,在分析这些结构参数与织物起毛起球图像构成关系的基础上,确定织物密度和织物组织循环数作为影响织物纹理的主要结构参数。对于织物密度的自动检测,首先给出了利用灰度投影法检测织物密度的过程,为了消除噪声信号的影响,对织物的傅里叶变换频谱图进行分析,提出在频域内选定代表织物经纱周期和纬纱周期的信号进行滤波,实现经纬纱的分离。然后再对分离后的子图像利用灰度投影法实现经纬纱的定位和密度测量。对于织物组织循环数的检测,首先利用织物密度的检测结果,将织物图像按照纱线的位置进行分离,然后沿着纱线纵向投影获得纱线表征信号,通过检测不同纱线灰度信号的相关性,得到织物组织循环数。
第三章给出了利用傅里叶变换方法在频域内实现毛球信息提取的方法。文中首先介绍了织物图像上的灰度投影规律,根据纱线上亮度信号符合正弦分布的特点,提出利用傅里叶变换对织物图像进行处理,滤除织物周期性纹理信号；然后介绍了基于平板扫描仪采集起毛起球织物和样照图像的方法,并对GB/T4802.3中光面精梳毛机织物起毛起球标准样照图像进行了分析,从傅里叶变换的幅值谱中选择代表织物周期性纹理的信号,结合阈值法滤除织物纹理,再利用低频保留的方法对织物毛球信息进行增强,扩大毛球与背景的反差；最后利用基于局部增强的Otsu阈值方法对织物毛球进行自动分割。五个等级的织物起毛起球标准样照中的毛球分割实例证明基于傅里叶变换的方法可以实现毛球的自动提取。
第四章提出采用时域Gabor滤波的方法对起毛起球织物图像进行了分析。文中首先介绍了Gabor变换的含义,给出了所采用的二维时域Gabor滤波方法；然后结合典型的Gabor滤波器,对Gabor滤波器的参数,包括方向参数、比例参数、频率参数、尺度参数以及滤波器窗口大小的选择方法进行了分析,其中滤波器窗口大小须根据织物组织循环区域确定；接着根据Gabor滤波器的频率参数和方向参数,建立了16个Gabor滤波器组成的Gabor滤波器簇。为了得到理想的Gabor滤波结果,讨论了最优Gabor滤波器的选择标准和Gabor滤波结果的融合方法,考虑到毛球分布具有多方向性的特点,提出将同一频率参数对应的多方向滤波结果进行融合。在Gabor滤波和融合结果的基础上,提出利用图像均值和标准差构建双阈值的方法对滤波结果进行阈值分割,从而定位毛球。最后对不同等级的起毛起球织物图像进行Gabor滤波,实际滤波效果证明运用Gabor滤波方法可以实现织物结构纹理的滤除,同时能对毛球信息增强。对于在滤波处理后的图像中,只要利用简单的阈值方法就可以实现毛球的准确分割。
第五章提出采用频域Gabor滤波的方法对起毛起球织物图像进行了处理。文中首先介绍了频域Gabor变换的含义,并对频域Gabor滤波方法进行了叙述；接着给出了频域Gabor滤波器的实现方法,讨论了频域Gabor滤波器参数,包括尺度参数、比例参数、坐标参数对频域Gabor滤波器性能的影响,并给出了选取各个参数的基本原则；为了得到理想的Gabor滤波结果,首先利用不同的Gabor滤波器对织物图像进行滤波,根据滤波结果提出将同一尺度的各方向滤波器进行融合,以消除滤波结果中毛球信息具有方向性的特征；再分别根据峰点滤波和织物密度检测结果,给出了用于织物起毛起球提取的Gabor滤波器选择方案。在滤波结果的基础上,提出基于局部窗口的阈值方法进行织物毛球分割。最后对不同等级的起毛起球织物图像和实际起毛起球织物图像进行频域Gabor滤波,实际滤波效果证明运用频域Gabor滤波方法可以实现织物结构纹理的滤除,并对毛球信息增强。与时域Gabor滤波的方法相比,基于频域Gabor变换的方法中,可根据织物结构参数便捷的选择滤波器参数,达到更好的滤波效果,实现织物毛球信息的准确提取。
第六章介绍了表征织物起毛起球程度的特征参数及其织物起毛起球评级方法,并构建了起毛起球评级系统。首先在毛球分割的基础上,结合统计方法完成起毛起球特征参数的提取,选择了毛球面积参数、毛球总数、毛球平均密度等作为起毛起球等级评估的标准,然后在分析各参数与起毛起球等级之间关系的基础上,选择毛球总面积参数作为起毛起球等级评定标准。为了消除图像放大倍率的影响,最终采用毛球总面积所占比例作为毛球总面积指标。根据不同等级样照上的毛球总面积比例指标,提出两种方法实现起毛球等级的评定,第一种方法是采用插值法确定不同等级之间的分界值,然后根据实际织物的毛球总面积比例确定起毛起球等级；第二种方法是利用拟合方法得出毛球总面积比例与起毛起球等级之间的函数关系,然后根据实际织物的毛球总面积比例数值直接计算出起毛起球等级。在本章的后一部分构建了起毛起球评级系统,该系统选择德国Basler公司IEEE1394接口SCA 1600型工业相机作为图像采集的硬件,采用Visual Basic 6.0和美国国家仪器公司的IMAQ插件作为软件来构建织物起毛起球自动评级系统,实现了整个起毛起球评级系统的各项功能,并提供了软件的参考界面。
第七章对全文进行了总结和展望。介绍了本课题研究的主要贡献以及所存在的不足问题,对织物起毛起球自动评级的进一步研究提出建议。
During the wearing and washing, pills caused by friction will be appeared on the fabric surface. The pills on the fabric surface not only damage the fabric appearance, but also deteriorate the touching of fabric. This will damage the wearing properties of fabric. During the inspection of fabric or clothing, the resistance of fabric pilling is adopted as one of the important measurement properties for evaluating fabric quality. The existing grade evaluation method for pilling fabric based on standard photos of pilling fabric, is relied on the human vision. The inspection process has the flaws of subjective and the grade evaluation result is partly random. To make the grade evaluation of fabric pilling to be objective and reliable, a novel method for automatic evaluate the grade of fabric pilling is proposed in this paper. The research devotes to development an automatic grade evaluation system for fabric pilling.
The research of this paper includes major contents as follows:automatic recognition of fabric structure parameter for grade evaluation of fabric pilling; the fabric pill information extraction based on Fourier transform; automatic analysis of fabric pills with Gabor transform in time-domain; fabric pills detection with Gabor transform in frequency-domain; characteristic parameters extraction for evaluating fabric pilling grade; the research and discussion of fabric pilling evaluation method; the development of automatic fabric pilling evaluation system.
In Chapter 1 the background and significance of the topic selection of the thesis is introduced briefly. First, The simulation methods of fabric pilling caused by friction during wearing and washing in the lab are introduced and the existing research content and analysis method are discussed. Then, the research of the grade evaluation for fabric pilling based on digital image analysis home and abroad is summarized. The shortcomings of the automatic grade evaluation of fabric pilling based on image processing are analyzed in conclusion. The research objects, content and major innovations of this paper are given at the end of this chapter.
In Chapter 2 the automatic recognition process of fabric structure texture parameters used for evaluating the fabric pilling are discussed. First, the definitions of relative fabric structure parameters are introduced. Based on the discussion of the relation between the parameters and composition of pill.ng fabric image, the fabric density and.he number of fabric weave repeat are chosen as the parameters for determining the structure composition of fabric image. The inspection of fabric density with gray-projection method is discussed and the procedure of the inspection is given. To smooth up the affection of noise information, the fabric image is transformed into frequency-domain with Fourier transform. The amplitude spectrum of fabric image is then obtained and analyzed. The filtering method for separating the warp and weft yarns in the frequency-domain is proposed by selecting the region representing the period information of warp and weft in the fabric image. In the sub-image including separation yarns, gray-projection method is used to locate the warp and weft yarn and the fabric density can be measured. To detect the number of fabric weave repeat, the yarns in the fabric image are segmented based on the fabric density detection result. The projection information along the longitudinal direction is detected to characterize the separating yarn. The number of fabric weave repeat can be obtained by calculating the relation of the gray information of different yarns.
In Chapter 3 the extraction method of fabric pills with Fourier transform method is given. The gray projection character in fabric image is introduced first and the gray intensity in the yarn obeys the sine curve. Based on this, Fourier transform which is based on sine function is proposed to analyze the fabric image. It is then selected to remove the periodic signals in the fabric image. The image acquisition method with a flat scanner of pilling fabric is given and the standard pilling image of wool woven fabric in GB/T4802.3 is chosen as the analysis object. The periodic signals in the fabric image are chosen with the help of amplitude spectrum obtained by Fourier transform and they are eliminated by threshold segmentation in the frequency-domain. The low frequency signals are retained to enhance the fabric pill information. The fabric image with enough difference between the background and the pill are got with the Fourier analysis method. The enhanced local Otsu threshold method is last adopted to segment the fabric pills automatically. The segmentation for standard pilling fabric image of five grades proves that the method pills based Fourier transform can be used for extracting the fabric pills.
In Chapter 4 the method of extracting pills in pilling fabric image based on Gabor transform in time-domain is discussed. The definition of Gabor transform is introduced first and the 2D Gabor filtering method in time-domain is explained. The parameters of Gabor filter, including the direction parameter, proportion parameter, frequency parameter, scale parameter and the window size of Gabor filter are then described with typical Gabor filters and the selection way of these parameters is given then. The size of the Gabor filter is determined by the region of the fabric structure in the fabric image. Based on the frequency parameter and direction parameter, a filter bank including 16 Gabor filters is constructed. To obtain satisfied Gabor filtering result, the selecting rule of optimal Gabor filters is given by calculating the mean and standard deviation of filtering result. The fusion of Gabor filtering result is carried out by fusion of the Gabor filtering results of different directions in the same frequency. The direction of fabric pills in the filtering result can be then eliminated. Based on the Gabor filtering and fusion result image, dual threshold values constructed by the mean and standard deviation of the result image are used to segment the fabric image. The pills can be located with threshold segmentation. The fabric pilling images of different grades are filtered by the proposed Gabor filters. The filtering results proved that the Gabor filters used can eliminate the fabric structure textures and enhance the fabric pill information. In the filtering image, the fabric pills can be segmented with simple threshold method.
In Chapter 5 the method of extracting pills in pilling fabric image based on Gabor transform in frequency-domain is discussed. The definition of Gabor transform in frequency-domain is first introduced in the chapter and the filtering way for image of Gabor transform in frequency-domain is explained. The realization method of Gabor transform in frequency-domain is then given and the parameters, including the scale parameter, the proportion parameter and the coordinate parameter, are discussed with the affection to the character of Gabor filters. The basis selection rules of these parameters are given then. To get satisfied Gabor filtering results, the different Gabor filters are adopted to filter the given pilling fabric image. Based on the filtering results, the coordinate parameter is selected and the results in different directions are fused to remove the direction affection of fabric pills. The selection rule of Gabor filter used for extracting the fabric pills is then given by the peak filtering method and the fabric density detection results. In the filtering result, local threshold method is chosen to locate and segment the fabric pills. The fabric pilling images of different grades are filtered by the proposed Gabor filters in frequency domain. The filtering results proved that the Gabor filters used in this chapter can eliminate the fabric structure textures and enhance the fabric pill information. Compared to the Gabor filtering method in time-domain, the Gabor filters in frequency-domain selects the filtering parameters based on the fabric weave structure parameters. The better filtering result can be obtained and the pill information in the fabric image is extracted accurately.
In Chapter 6 the characteristic parameters of fabric pilling and the method used for evaluating the fabric pilling grade are discussed. The pilling grade evaluation system constructed is also introduced. Based on the pill segmentation results, the characteristic parameters can be calculated with statistical method. Five parameters, including the area of fabric pills, the number of fabric pills and the density of fabric pill, are adopted to evaluate the fabric pilling degree. The relation between the parameters and the fabric pilling grades is analyzed and the total area of fabric pills is selected as the evaluation standard for fabric pilling. To eliminate the magnify affection of fabric image, the scale of the total area of fabric pills to the area of fabric image is chosen as the evaluation index. Based on the scale of total area of fabric pills, two methods are used to assess the fabric pilling grade. In the first method, the cutoff values between different grades are calculated with interpolation. The fabric pilling grade can be then determined based on the scale of total area of fabric pills to the area of fabric image. The second method is to assure the function relation between the scale of total area of fabric pills and the fabric pilling grades. The scale of total area of fabric pills can be used as a parameter to calculate the fabric pilling grade directly. In the latter part of this chapter, the construction of grade evaluation system for fabric pilling is explained. The hardware and software of the system is introduced. The industrial camera SCA 1600 produced by Basler corporation in German with IEEE 1394 port is selected as the hardware of image acquisition. Visual Basic 6.0 and the IMAQ ActiveX of American National Instruments are chosen as the software to construct the fabric pilling automatic evaluation system. The realization for the functions of the fabric pilling evaluation system is then briefly introduced and the reference interface of the system is provided at last.
In Chapter 7 a summary is made to describe the main contributions and the problems of the present work. The advice of the future work of the automatic evaluation of fabric pilling based on image processing is given at last.
引文
[1]姚穆.纺织材料学,1990,中国纺织出版社,北京.
[2]于伟东.纺织材料学,2006,中国纺织出版社,北京.
[3]Konda A, Xin L C. Evaluation of pilling by computer image analysis. Journal of the Textile Machinery Society of Japan,1990,36(3):96-107.
[4]Ramgulan R B, Amirbayat J. The objective assessment of fabric pilling. Part I: Methodology. Journal of the Textile Institute,1993,84(2):221-226.
[5]His H C, Bresee R R, Annis P A. Characterizing fabric pilling using image-analysis techniques. Part Ⅰ:Pill detection and description. Journal of the Textile Institute,1998,89(1):80-95.
[6]His H C, Bresee R R, Annis P A. Characterizing fabric pilling using image-analysis techniques. Part Ⅱ:Comparison with visual pilling rating. Journal of the Textile Institute,1998,89(1):96-100.
[7]His H C, Bresee R R. Pilling evaluation of laboratory, abraded, laundered and worn fabrics using image analysis. AATCC,1996:465-479.
[8]Xu B. Instrumental evaluation of fabric pilling. Journal of Textile Institute,1997, 88:488-500.
[9]Abril H C, Millan M S, Torres Y. Automatic method based on image analysis for pilling in fabric. Optical Engineer,1998,37(6):1477-1488.
[10]Abril H C, Millan M S, Torres Y. Automatic method based on image analysis for pilling in fabric. Optical Engineering,1998,37(11):2939-2947.
[11]Abril H C, Millan M S, Torres Y. Image synthesis of pilling textiles by Karhumen-Loeve transform. SPIE,1999(3572):254-258.
[12]Palmer S, Wang X. Objective classification of fabric pilling based on the two-dimensional discrete wavelet transform. Textile Research Journal,2003,73(8): 713-720.
[13]Hu J, Xin B. Image based modeling and analysis of textile materials. The Kluwer International Series in Engineering and Computer Science,2004:283-307.
[14]Kim S C, Kang T J. Image analysis of standard pilling photographs using wavelet reconstruction. Textile Research Journal,2005,75(12):801-811.
[15]Kim S, Park C K. Evaluation of fabric pilling using hybrid imaging methods. Fibers and Polymers,2006,7(1):57-61.
[16]Zhang J, Wang X, Palmer S. Objective grading of fabric pilling with wavelet texture analysis. Textile Research Journal,2007,77(11):871-879.
[17]Zhang J, Wang X, Palmer S. Objective pilling evaluation of wool fabrics. Textile Research Journal,2007,77(12):929-936.
[18]Zhang J, Wang X, Palmer S. The robustness of objective fabric pilling evaluation method. Fibers and Polymers,2009,10(1):108-115.
[19]Deng Z, Wang L, Wang X. An integrated method of feature extraction and objective evaluation of fabric pilling. Journal of the Textile Institute,2011, 102(1):1-13.
[20]王晓红,姚穆.图象分析技术评级织物起球.纺织学报,1998,19(6)：8-11.
[21]徐增波,陆凯,黄秀宝.应用图像分析技术评估织物起球等级.中国纺织大学学报,1999,25(3)：28-33.
[22]杨旭红,张长胜,唐人成.图像分析法评价Lyocell织物的起球性能.印染,2001(12)：50-53.
[23]陈霞,黄秀宝.基于光照投影的起球织物图像的采集、预处理和毛球分割.东华大学学报,2003,29(5)：36-41.
[24]Chen X, Huang X. Evaluating fabric pilling with light-projected image analysis. Textile Research Journal,2004,74(11):977-981.
[25]陈霞.基于切面投影图像的织物起球等级的计算机视觉评定.2004,东华大学,上海.
[26]Chen X, Huang X. Image analysis of fabric pilling based on light projection. Journal of Donghua University,2003,20(4):1-4.
[27]Chen X, Xu Zengbo, Chen X, et al. Detecting pills in fabric images based on multi-scale matched filtering. Textile Research Journal,2009,79(15):1389-1395.
[28]钟智丽.基于小波分析的织物起球客观评级研究.2006,天津工业大学,天津.
[29]卢海空,钟智丽.小波分析理论在织物起毛起球图像消噪中的应用.江苏纺织,2006(7)：39-41.
[30]卢海空.小波分析理论在织物起毛起球客观评定中的应用.2007,天津工业大学,天津.
[31]曹飞,汪军,陈霞.织物起球标准样照的图像分析.东华大学学报,2007,33(6)：751-755.
[32]曹飞.基于图像分析技术的织物起球等级评定方法.2007,东华大学,上海.
[33]Liu X. Segmentation for fabric pilling images based on edge flow. Information and Computing Science,2009,369-372.
[34]Ryuichi Akiyama, Toshihiro Iguro, Sei Uchiyama, et al. Detection of weave types in woven fabrics by observing optical diffraction patterns. Sen-I Gakkaishi,1986, 42(10):T574-T579.
[35]Mizuho Kinnoshita, Yositada Hashimoto, Ryuichi Akiyama, et al. Determination of weave type in woven fabric by digital image processing. Journal of the Textile Machinery Society of Japan,1989,35(2):1-4.
[36]Wood E J. Appling Fourier and associated transforms to pattern characterization in Textiles. Textile Research Journal,1995,65(11):212-220.
[37]Wood E J. Carpet texture measurement using image analysis. Textile Research Journal,1989,59(1):1-12.
[38]Ravandi S A H, Torumi K. Fourier transform analysis of plain weave fabric appearance. Textile Research Journal,1995,65(11):676-683.
[39]孙亚峰,陈霞,王新厚.机织物密度的计算机自动识别.东华大学学报(自然科学版),2006,32(2)：83-88.
[40]辛斌杰,余序芬,吴兆平.机织物经纬密测量的图像处理技术.中国纺织大学学报,1999,25(3)：34-37.
[41]Xu B. Identifying fabric structure with fast Fourier transform techniques. Textile Research Journal,1996,66(8):496-506.
[42]Lachkar A, Gadi T, Benslimane, et al. Textile woven-fabric recognition by using Fourier image-analysis techniques. Part I:A fully automatic approach for crossed-points detection. Journal of the Textile Institute,2003,94(3):194-201.
[43]Maros Tunak, Ales Linka, Petr Volf. Automatic assessing and monitoring of weaving density. Fibers and Polymers,2009,10(6),830-836.
[44]李立轻,陈霞,黄秀宝.基于自适应正交小波的机织物密度自动检测的研究.东华大学学报,2005,31(1)：63-66.
[45]何峰,李立轻,徐建明.基于自适应小波变换的织物密度测量.纺织学报,2007,28(2)：32-35.
[46]冯毅力,李汝勤.用小波变换法自动测量机织物经纬密度.纺织学报,2001,22(2)：94-95.
[47]黄战华,黄孟怀,蔡怀宇,等.用数码图像频谱检测纺织品线密度.光电子激光,2000,11(6)：626-627.
[48]高卫东,刘基宏,徐伯俊,等.织物中纬纱排列参数的自动识别.棉纺织技术,2002,30(1)：28-31.
[49]高卫东,刘基宏,徐伯俊,等.织物中经纱排列参数的自动识别.棉纺织技术,2002,30(3)：31-34.
[50]谢莉青,于伟东.织物经纬密度自动测量实用技术：1.测量方法.纺织学报,29(5),26-30,2008.
[51]Huang C C, Liu S C, Yu W H. Woven fabric analysis by image processing. Part I: Identification of weave patterns. Textile Research Journal,2000,70(6):481-485.
[52]Jeong Y J, Jang J. Applying image analysis to automatic inspection of fabric density for woven fabrics. Fibers and Polymers,2005,26(2):156-161.
[53]Kang T J, Chang H K, Kung W O. Automatic recognition of fabric weave patterns by digital image analysis. Textile Research Journal,1999,69(2):77-83.
[54]Lin J J. Applying a co-occurrence matrix to automatic inspection of weaving density for woven fabrics. Textile Research Journal,2002,72(6):486-490.
[55]Liu J, Yamaura I, Gao W. Discussing reflecting model of yarn. International Journal of Clothing Science and Technology,2006,18(2):129-141.
[56]Grafakos Loukas. Classical and modern Fourier analysis.2006,机械工业出版社,北京.
[57]Boggess A, Narcowich F J小波与傅里叶分析基础(第二版).2010,电子工业出版社,北京.
[58]朱虹.数字图像处理基础.2005,科学出版社,北京.
[59]邢树永,余序芬,吴兆平.应用图像处理技术评定织物起球性能探讨.山东纺织科技,2004(5)：31-33.
[60]Tsai W. Moment-preserving threshold:A new approach. CVGIP,1985(29):377-393.
[61]Gonzales R C, Woods R E数字图像处理(第二版).2007,电子工业出版社,北京.
[62]Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on System, Man, and Cybernetics,1979,9(1):62-66.
[63]吴冰,秦志远.自动确定图像二值化最佳阈值的新方法.测绘学院学报,2001,18(4)：283-286.
[64]张德丰Matlab小波分析.2010,机械工业出版社,北京.
[65]Gabor D. Theory of communication. Journal of the Institution of Electrical Engineers,1946(93):429-457.
[66]Daugman J G. Uncertainty relation for resolution in space, spatial-frequency, band orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A.1985,2(7):1160-1169.
[67]Tsai D M, Wu S K. Automated surface inspection using Gabor filters. The International Journal of Advanced Manufacturing Technology,2000,16(7):474-482.
[68]Bodnarova A, Bennamoun M, Latham S. Optimal Gabor filters for textile flaw detection. Pattern Recognition,2002,35(12):2973-2991.
[69]Dunn D F, Higgins W E. Optimal Gabor filters for texture segmentation. IEEE Transactions on Image Processing,1995,4 (7):947-964.
[70]Jain A K, Farrokhnia F. Unsupervised texture segmentation using Gabor filters, Pattern Recognition,1991,24 (12):1167-1186.
[71]Liu X, Wen Z, Su Z, Choi K F. Slub extraction in woven fabric images using Gabor filters. Textile Research Journal,2008,78(4):320-325.
[72]Weldon T P, Higgins W E, Dunn D F. Efficient Gabor filter design for texture segmentation. Pattern Recognition,1996,29 (12):2005-2015.
[73]Chan C H, Pang K H. Fabric defect detection by Fourier analysis. IEEE transactions on Industry Applications,2000,36(5):1267-1276.
[74]Maro Tunak, Ale Linka. Analysis of planar anisotropy of fibre systems by using 2D Fourier transform. Fibres & Textiles in Eastern Europe,2007,64(5):86-90.
[75]Escofet J, Navarro R, MillVan M S, Pladellorens J. Detection of local defects in textile webs using Gabor filters. Optical Engineering,1998,37 (8):2297-2307.
[76]Rallo M, Millan M S, Escofet J. Unsupervised novelty detection using Gabor filters for defect segmentation in textures. Journal of the Optical Society of America A,2009,26(9):1967-1976.
[77]徐增波,陆凯,黄秀宝Evaluation of fabric pilling using light projection and image analysis techniques. Journal of China Textile University (English Edition), 2000,17(4):80-86.
[78]屈名,王德麾.一种新的带有优化参数的曲线插值算法.机械与电子,2010,9：19-22.
[79]Gopal M, Jepson W P. Development of digital image analysis techniques for the study of velocity and void profiles in slug flow. International Journal of Multiphase Flow,1997,23(5):945-965.
[80]王荣武.基于图像处理技术的苎麻和棉纤维纵向全自动识别系统.2007,东华大学,上海.
[81]王树刚,余新.浅谈光电耦合器CCD和CMOS的差别.科技信息,2009,14：311.
[82]韩振雷.CCD和CMOS图像传感器的异同剖析.影像技术,2009,4：39-42.
[83]http://en.wikipedia.org/wiki/CCD_camera.
[84]http://en.wikipedia.org/wiki/CMOS_camera.
[85]米本和也CCD/CMOS图像传感器基础与应用.2006,科学出版社,北京.
[86]王景中,张朝杰.1394总线在实时图像系统中的应用.计算机测量与控制,2011：19(1)：222-224.
[87]杨庆勇,刘方,张覃平.基于IEEE1394接口的图像传输控制器设计.仪器仪表,2008,3：20-25.
[88]廖伟平,谌德荣.基于1394的高速图像传输接口的设计与实现.计算机测量与控制.2006,(6)：28-35.
[89]http://en.wikipedia.org/wiki/IEEE_1394.
[90]毕美华,刘文文.基于VC++6.0的IEEE1394 CCD应用程序开发.现代显示,2009,98：38-42.
[91]赵义先,宋申民,陈兴林,等.基于IEEE1394总线的图像采集处理系统实现.2006,13：110-113.
[92]韩天宝,王军政,沈伟.基于WINDOWS CE.NET的1394摄像机驱动程序开发.微计算机信息,2006,22(10)：204-207.
[93]刘义先,宋申民,陈兴林,强文义.基于IEEE1394总线的图像采集处理系统实现.控制工程,2006,13(S1)：110-113.
[94]http://en.wikipedia.org/wiki/Visual_Basic.
[95]NI-IMAQ User Manual. www.ni.com/pdf/manuals/321386a.pdf.
[96]张峻.简化视觉应用开发的NI IMAQ Vision Builder机电一体化,1999,5：39.
[97]夏庆观,路红,邹簧.基于IMAQ的模式识别在零件检测中的应用.现代制造工程,2005,9：73-75.
[98]NI-IMAQ for IEEE 1394 Camera Release Notes.www.ni.com/pdf/manuals/ 373321c.pdf.
[99]NI-IMAQ for IEEE 1394 Cameras User Manual. www.ni.com/pdf/manuals/ 370362b.pdf.
[100]NI-IMAQ for IEEE 1394 Cameras. www.cadfamily.com/download/EDA/IMAQ /323321b.pdf
[101]NI Vision for Visual Basic User Manual. www.ni.com/pdf/manuals/371257b. pdf
[2]于伟东.纺织材料学,2006,中国纺织出版社,北京.
[3]Konda A, Xin L C. Evaluation of pilling by computer image analysis. Journal of the Textile Machinery Society of Japan,1990,36(3):96-107.
[4]Ramgulan R B, Amirbayat J. The objective assessment of fabric pilling. Part I: Methodology. Journal of the Textile Institute,1993,84(2):221-226.
[5]His H C, Bresee R R, Annis P A. Characterizing fabric pilling using image-analysis techniques. Part Ⅰ:Pill detection and description. Journal of the Textile Institute,1998,89(1):80-95.
[6]His H C, Bresee R R, Annis P A. Characterizing fabric pilling using image-analysis techniques. Part Ⅱ:Comparison with visual pilling rating. Journal of the Textile Institute,1998,89(1):96-100.
[7]His H C, Bresee R R. Pilling evaluation of laboratory, abraded, laundered and worn fabrics using image analysis. AATCC,1996:465-479.
[8]Xu B. Instrumental evaluation of fabric pilling. Journal of Textile Institute,1997, 88:488-500.
[9]Abril H C, Millan M S, Torres Y. Automatic method based on image analysis for pilling in fabric. Optical Engineer,1998,37(6):1477-1488.
[10]Abril H C, Millan M S, Torres Y. Automatic method based on image analysis for pilling in fabric. Optical Engineering,1998,37(11):2939-2947.
[11]Abril H C, Millan M S, Torres Y. Image synthesis of pilling textiles by Karhumen-Loeve transform. SPIE,1999(3572):254-258.
[12]Palmer S, Wang X. Objective classification of fabric pilling based on the two-dimensional discrete wavelet transform. Textile Research Journal,2003,73(8): 713-720.
[13]Hu J, Xin B. Image based modeling and analysis of textile materials. The Kluwer International Series in Engineering and Computer Science,2004:283-307.
[14]Kim S C, Kang T J. Image analysis of standard pilling photographs using wavelet reconstruction. Textile Research Journal,2005,75(12):801-811.
[15]Kim S, Park C K. Evaluation of fabric pilling using hybrid imaging methods. Fibers and Polymers,2006,7(1):57-61.
[16]Zhang J, Wang X, Palmer S. Objective grading of fabric pilling with wavelet texture analysis. Textile Research Journal,2007,77(11):871-879.
[17]Zhang J, Wang X, Palmer S. Objective pilling evaluation of wool fabrics. Textile Research Journal,2007,77(12):929-936.
[18]Zhang J, Wang X, Palmer S. The robustness of objective fabric pilling evaluation method. Fibers and Polymers,2009,10(1):108-115.
[19]Deng Z, Wang L, Wang X. An integrated method of feature extraction and objective evaluation of fabric pilling. Journal of the Textile Institute,2011, 102(1):1-13.
[20]王晓红,姚穆.图象分析技术评级织物起球.纺织学报,1998,19(6)：8-11.
[21]徐增波,陆凯,黄秀宝.应用图像分析技术评估织物起球等级.中国纺织大学学报,1999,25(3)：28-33.
[22]杨旭红,张长胜,唐人成.图像分析法评价Lyocell织物的起球性能.印染,2001(12)：50-53.
[23]陈霞,黄秀宝.基于光照投影的起球织物图像的采集、预处理和毛球分割.东华大学学报,2003,29(5)：36-41.
[24]Chen X, Huang X. Evaluating fabric pilling with light-projected image analysis. Textile Research Journal,2004,74(11):977-981.
[25]陈霞.基于切面投影图像的织物起球等级的计算机视觉评定.2004,东华大学,上海.
[26]Chen X, Huang X. Image analysis of fabric pilling based on light projection. Journal of Donghua University,2003,20(4):1-4.
[27]Chen X, Xu Zengbo, Chen X, et al. Detecting pills in fabric images based on multi-scale matched filtering. Textile Research Journal,2009,79(15):1389-1395.
[28]钟智丽.基于小波分析的织物起球客观评级研究.2006,天津工业大学,天津.
[29]卢海空,钟智丽.小波分析理论在织物起毛起球图像消噪中的应用.江苏纺织,2006(7)：39-41.
[30]卢海空.小波分析理论在织物起毛起球客观评定中的应用.2007,天津工业大学,天津.
[31]曹飞,汪军,陈霞.织物起球标准样照的图像分析.东华大学学报,2007,33(6)：751-755.
[32]曹飞.基于图像分析技术的织物起球等级评定方法.2007,东华大学,上海.
[33]Liu X. Segmentation for fabric pilling images based on edge flow. Information and Computing Science,2009,369-372.
[34]Ryuichi Akiyama, Toshihiro Iguro, Sei Uchiyama, et al. Detection of weave types in woven fabrics by observing optical diffraction patterns. Sen-I Gakkaishi,1986, 42(10):T574-T579.
[35]Mizuho Kinnoshita, Yositada Hashimoto, Ryuichi Akiyama, et al. Determination of weave type in woven fabric by digital image processing. Journal of the Textile Machinery Society of Japan,1989,35(2):1-4.
[36]Wood E J. Appling Fourier and associated transforms to pattern characterization in Textiles. Textile Research Journal,1995,65(11):212-220.
[37]Wood E J. Carpet texture measurement using image analysis. Textile Research Journal,1989,59(1):1-12.
[38]Ravandi S A H, Torumi K. Fourier transform analysis of plain weave fabric appearance. Textile Research Journal,1995,65(11):676-683.
[39]孙亚峰,陈霞,王新厚.机织物密度的计算机自动识别.东华大学学报(自然科学版),2006,32(2)：83-88.
[40]辛斌杰,余序芬,吴兆平.机织物经纬密测量的图像处理技术.中国纺织大学学报,1999,25(3)：34-37.
[41]Xu B. Identifying fabric structure with fast Fourier transform techniques. Textile Research Journal,1996,66(8):496-506.
[42]Lachkar A, Gadi T, Benslimane, et al. Textile woven-fabric recognition by using Fourier image-analysis techniques. Part I:A fully automatic approach for crossed-points detection. Journal of the Textile Institute,2003,94(3):194-201.
[43]Maros Tunak, Ales Linka, Petr Volf. Automatic assessing and monitoring of weaving density. Fibers and Polymers,2009,10(6),830-836.
[44]李立轻,陈霞,黄秀宝.基于自适应正交小波的机织物密度自动检测的研究.东华大学学报,2005,31(1)：63-66.
[45]何峰,李立轻,徐建明.基于自适应小波变换的织物密度测量.纺织学报,2007,28(2)：32-35.
[46]冯毅力,李汝勤.用小波变换法自动测量机织物经纬密度.纺织学报,2001,22(2)：94-95.
[47]黄战华,黄孟怀,蔡怀宇,等.用数码图像频谱检测纺织品线密度.光电子激光,2000,11(6)：626-627.
[48]高卫东,刘基宏,徐伯俊,等.织物中纬纱排列参数的自动识别.棉纺织技术,2002,30(1)：28-31.
[49]高卫东,刘基宏,徐伯俊,等.织物中经纱排列参数的自动识别.棉纺织技术,2002,30(3)：31-34.
[50]谢莉青,于伟东.织物经纬密度自动测量实用技术：1.测量方法.纺织学报,29(5),26-30,2008.
[51]Huang C C, Liu S C, Yu W H. Woven fabric analysis by image processing. Part I: Identification of weave patterns. Textile Research Journal,2000,70(6):481-485.
[52]Jeong Y J, Jang J. Applying image analysis to automatic inspection of fabric density for woven fabrics. Fibers and Polymers,2005,26(2):156-161.
[53]Kang T J, Chang H K, Kung W O. Automatic recognition of fabric weave patterns by digital image analysis. Textile Research Journal,1999,69(2):77-83.
[54]Lin J J. Applying a co-occurrence matrix to automatic inspection of weaving density for woven fabrics. Textile Research Journal,2002,72(6):486-490.
[55]Liu J, Yamaura I, Gao W. Discussing reflecting model of yarn. International Journal of Clothing Science and Technology,2006,18(2):129-141.
[56]Grafakos Loukas. Classical and modern Fourier analysis.2006,机械工业出版社,北京.
[57]Boggess A, Narcowich F J小波与傅里叶分析基础(第二版).2010,电子工业出版社,北京.
[58]朱虹.数字图像处理基础.2005,科学出版社,北京.
[59]邢树永,余序芬,吴兆平.应用图像处理技术评定织物起球性能探讨.山东纺织科技,2004(5)：31-33.
[60]Tsai W. Moment-preserving threshold:A new approach. CVGIP,1985(29):377-393.
[61]Gonzales R C, Woods R E数字图像处理(第二版).2007,电子工业出版社,北京.
[62]Otsu N. A threshold selection method from gray-level histogram. IEEE Transactions on System, Man, and Cybernetics,1979,9(1):62-66.
[63]吴冰,秦志远.自动确定图像二值化最佳阈值的新方法.测绘学院学报,2001,18(4)：283-286.
[64]张德丰Matlab小波分析.2010,机械工业出版社,北京.
[65]Gabor D. Theory of communication. Journal of the Institution of Electrical Engineers,1946(93):429-457.
[66]Daugman J G. Uncertainty relation for resolution in space, spatial-frequency, band orientation optimized by two-dimensional visual cortical filters. Journal of the Optical Society of America A.1985,2(7):1160-1169.
[67]Tsai D M, Wu S K. Automated surface inspection using Gabor filters. The International Journal of Advanced Manufacturing Technology,2000,16(7):474-482.
[68]Bodnarova A, Bennamoun M, Latham S. Optimal Gabor filters for textile flaw detection. Pattern Recognition,2002,35(12):2973-2991.
[69]Dunn D F, Higgins W E. Optimal Gabor filters for texture segmentation. IEEE Transactions on Image Processing,1995,4 (7):947-964.
[70]Jain A K, Farrokhnia F. Unsupervised texture segmentation using Gabor filters, Pattern Recognition,1991,24 (12):1167-1186.
[71]Liu X, Wen Z, Su Z, Choi K F. Slub extraction in woven fabric images using Gabor filters. Textile Research Journal,2008,78(4):320-325.
[72]Weldon T P, Higgins W E, Dunn D F. Efficient Gabor filter design for texture segmentation. Pattern Recognition,1996,29 (12):2005-2015.
[73]Chan C H, Pang K H. Fabric defect detection by Fourier analysis. IEEE transactions on Industry Applications,2000,36(5):1267-1276.
[74]Maro Tunak, Ale Linka. Analysis of planar anisotropy of fibre systems by using 2D Fourier transform. Fibres & Textiles in Eastern Europe,2007,64(5):86-90.
[75]Escofet J, Navarro R, MillVan M S, Pladellorens J. Detection of local defects in textile webs using Gabor filters. Optical Engineering,1998,37 (8):2297-2307.
[76]Rallo M, Millan M S, Escofet J. Unsupervised novelty detection using Gabor filters for defect segmentation in textures. Journal of the Optical Society of America A,2009,26(9):1967-1976.
[77]徐增波,陆凯,黄秀宝Evaluation of fabric pilling using light projection and image analysis techniques. Journal of China Textile University (English Edition), 2000,17(4):80-86.
[78]屈名,王德麾.一种新的带有优化参数的曲线插值算法.机械与电子,2010,9：19-22.
[79]Gopal M, Jepson W P. Development of digital image analysis techniques for the study of velocity and void profiles in slug flow. International Journal of Multiphase Flow,1997,23(5):945-965.
[80]王荣武.基于图像处理技术的苎麻和棉纤维纵向全自动识别系统.2007,东华大学,上海.
[81]王树刚,余新.浅谈光电耦合器CCD和CMOS的差别.科技信息,2009,14：311.
[82]韩振雷.CCD和CMOS图像传感器的异同剖析.影像技术,2009,4：39-42.
[83]http://en.wikipedia.org/wiki/CCD_camera.
[84]http://en.wikipedia.org/wiki/CMOS_camera.
[85]米本和也CCD/CMOS图像传感器基础与应用.2006,科学出版社,北京.
[86]王景中,张朝杰.1394总线在实时图像系统中的应用.计算机测量与控制,2011：19(1)：222-224.
[87]杨庆勇,刘方,张覃平.基于IEEE1394接口的图像传输控制器设计.仪器仪表,2008,3：20-25.
[88]廖伟平,谌德荣.基于1394的高速图像传输接口的设计与实现.计算机测量与控制.2006,(6)：28-35.
[89]http://en.wikipedia.org/wiki/IEEE_1394.
[90]毕美华,刘文文.基于VC++6.0的IEEE1394 CCD应用程序开发.现代显示,2009,98：38-42.
[91]赵义先,宋申民,陈兴林,等.基于IEEE1394总线的图像采集处理系统实现.2006,13：110-113.
[92]韩天宝,王军政,沈伟.基于WINDOWS CE.NET的1394摄像机驱动程序开发.微计算机信息,2006,22(10)：204-207.
[93]刘义先,宋申民,陈兴林,强文义.基于IEEE1394总线的图像采集处理系统实现.控制工程,2006,13(S1)：110-113.
[94]http://en.wikipedia.org/wiki/Visual_Basic.
[95]NI-IMAQ User Manual. www.ni.com/pdf/manuals/321386a.pdf.
[96]张峻.简化视觉应用开发的NI IMAQ Vision Builder机电一体化,1999,5：39.
[97]夏庆观,路红,邹簧.基于IMAQ的模式识别在零件检测中的应用.现代制造工程,2005,9：73-75.
[98]NI-IMAQ for IEEE 1394 Camera Release Notes.www.ni.com/pdf/manuals/ 373321c.pdf.
[99]NI-IMAQ for IEEE 1394 Cameras User Manual. www.ni.com/pdf/manuals/ 370362b.pdf.
[100]NI-IMAQ for IEEE 1394 Cameras. www.cadfamily.com/download/EDA/IMAQ /323321b.pdf
[101]NI Vision for Visual Basic User Manual. www.ni.com/pdf/manuals/371257b. pdf