基于自适应小波三层分解的织物疵点检测

提出了运用自适应正交小波对织物图像进行三层分解的织物疵点检测方法。通过借鉴Daubechies的构造条件构造织物自适应小波,由自适应小波对织物图像进行三层分解,将疵点信息融合后设定固定的闻值进行二值化。同采用DB族小波分析结果比较与实验检测,表明此方法有效。     

用小波变换法自动测量机织物经纬密度

研究用小波变换方法对机织物图像处理,实现织物经纬密度的自动测量,提出了确定小波分解层次的方法,对分解图像进行二值化和平滑处理的方法,实验证明用coiflets小波处理织物图像是有效的。     

基于小波分解和奇异值分解的织物疵点检测

探讨基于小波分解和奇异值分解的织物疵点检测效果。运用三种算法进行了织物疵点检测。采用基本奇异值分解法进行机织物疵点检测,检测结果受噪声影响较大;采用Haar小波对待测图像进行除噪,边缘检测性较弱。利用Gabor小波良好的图像边缘敏感度、方向和尺度选择性,进行图像滤波,再进行SVD分解。结果表明:Gabor小波和SVD的融合算法可以对图像多种方向多种尺度进行调节,检测效果较好。认为:Gabor小波和奇异值分解相融合算法可应用于机织物疵点检测。     

基于小波熵的织物疵点检测方法

探讨利用小波熵检测织物疵点的新方法。在采用二维离散小波变换对织物图像进行分解的基础上,引入了熵的概念,将小波熵作为织物图像的特征值,把熵值看作系统紊乱程度的度量,由此得到织物图像的小波熵特征值,通过与正常织物经过二维小波变换后提取的小波熵值相比较,熵值大者即认为有疵点存在。通过对不同组织织物和几种典型的织物疵点进行检测,试验结果表明该方法有效可行。     

小波分析在织物缺陷检测中的应用

对于织物缺陷的检测，可以使用多种不同的图像处理技术．而具有多分辨特性的小波变换是一种分析图像的新方法，它的变尺度特性与人类视觉中的空间频率多通道相吻合．使用小波分析的方法对3种织物缺陷进行检测分类．首先将织物图像进行3层小波分解，然后把小波分解后的图像灰度值作为特征参数输入到BP神经网络进行检测识别，实验结果表明，用这种方法识别织物缺陷识别率可达到98％。     

基于Radon变换和能量曲线的机织物密度检测

探讨基于Radon变换和能量曲线的机织物经密纬密自动检测方法。针对在经密纬密测量中织物倾斜和小波最优分解级数确定的问题,通过图像预处理,采用Randon变换对图像进行校正处理,根据二维小波变换输出的细节分量绘制能量曲线确定小波分解级数的方法,确定最优分解级数,并对织物重构图的垂直分量和水平分量进行优化,从而精确计算织物经密纬密。与人工测量相比,计算机测试织物密度的相对误差在1%左右。认为:本研究方法能够满足自动准确测量织物密度的要求。     

基于小波变换的机织物高密度检测

针对目前高密度机织物密度自动检测算法测量精度较低的现象,提出在三原组织织物密度自动检测过程中,利用小波变换的分解与重构特性分析预处理后的织物图像,对分解的子图像进行二值化、平滑处理来提取纱线周期性特征参数,统计二值图像中黑白循环更替次数来确定织物经纬密度。为提高密度检测精度,采用分区域平滑减小了斜纹纹路的影响;通过缎纹织物图像反面获取纬纱信息消除缎纹组织织物浮长的影响;运用形态学滤波去除因高密度织物上纱线毛羽导致平滑过程中出现的细条块。实验结果表明:该方法检测误约为1.00%,测量精度较高,具有一定的实用参考价值。     

基于MATLAB的机织物密度自动检测

对机织物用MATLAB语言进行图像处理，实现机织物经纬密度自动检测进行了研究。提出用小波变换对机织物图像进行分解和重构，将机织物经向和纬向信息分离出来，再对变换后的图像进行二值化和平滑处理。得到与背景完全分离的纱线分布信息，自动计算出机织物的经纬密度。经实验验证，所得结果相对误差为3‰，准确度较高。     

基于小波变换的机织物经纬密度自动检测技术

探讨了小波变换在机织物图像处理方面的应用,通过使用多层小波分解与单层细节重构,实现了对机织物经纬密度的自动测量;提出了一种利用最大相关系数算法自动确定小波重构层数的新方法,可使结果更客观、准确。     

基于改进的小波分解织物疵点检测

采用小波分解的改进方法，运用二维离散小波变换进行分解，有效地从图像中提取信息，分析织物的纹理特征并进行相应处理，实现目标图像的特征提取和输入LMBP神经网络进行学习训练。实验结果表明，对油污、破洞、断经、断纬能比较准确地识别和定位，可快速有效地进行织物疵点检测。     

机织物组织自动识别技术

为实现机织物组织的自动识别,对织物表面反射图像进行纠偏和直方图均衡预处理,并按图像的经纬向灰度投影曲线实现织物组织点的定位。根据经纬向组织点灰度的相关性,求出织物组织的经纬纱循环数。通过比较组织循环范围内组织点的灰度均值来确定组织点的属性,并以组织点的灰度方差对组织点的属性进行验证。实现单循环组织识别后,再对多个组织循环内的对应组织点进行验证,以进一步提高识别的准确性。织物的测试结果表明,所提出的算法能够实现简单的机织物组织的准确识别,并能输出对应的织物组织图。     

A novel hybrid genetic and imperialist competitive algorithm for structure extraction of woven fabric images

The woven fabric is a flexible object and to specify its parameters, applying inflexible and ordinary methods of image processing ever have considerable errors. In this regards, proposing an adaptable method to fabric image properties is concentrated to detect the yarns position. In this research, a flexible algorithm is proposed containing two stages: first, the inexact ranges of fabric parameters are determined by preprocessing colored fabric images using wavelet transform and clustering methods. Then, the hybrid genetic and imperialist competitive algorithm is applied to optimize the obtained ranges and detect the yarns position. To achieve better results, the parameters of the hybrid ICA–GA are calibrated using the Taguchi method. Results indicate that in this new method, the error value of detecting structural fabric parameters has considerably decreased to 5% as compared with common gray-scale projection method. The proposed method is capable of detecting the exact yarns position in colored fabric images with uneven color intensity and low-density weave with mean precision value of 96.2%. In the fabric images with high density weaves, the mean precision value is more than 94.72%.     

基于图像处理技术的织物组织自动识别

文章给出了一种利用图像处理技术自动识别机织物组织的方案，实验证明测量结果对简单机织物而言具有良好的准确性，测量方法可代替人工操作，对织物的自动检测具有一定的参考价值。     

应用频谱图的机织物纹理分析

利用傅里叶变换技术将空间域的纹理图像变换到频率域中,采用频谱图对机织物纹理信息进行分析。通过傅里叶变换技术得到机织物图像的频谱图,利用阈值分割曲线选取合适的阈值对频谱图进行分割,然后用邻域最大值的方法找到织物纹理的特征坐标点。根据坐标点与实际频率的关系计算机织物纹理的频率范围。实验结果表明,随着密度的增大织物纹理的频率增加,斜纹和缎纹的频率范围比平纹织物的大,常见机织物的纹理为中低频。     

Automatic recognition for striped woven fabric pattern

Three basic weaves and fancy woven fabrics can be recognized usually. But the recognition of the striped woven fabric pattern is a challenging work, because it contains two or more types of woven fabrics. A robust striped woven fabric pattern recognition method is presented in this paper, through which the striped woven fabric pattern could be segmented into three basic weaves and fancy woven fabrics based on Gray-Level Co-occurrence Matrix (GLCM). Firstly, scanning window is selected automatically by analyzing the characteristics of the striped woven fabric, and features are extracted in this window based on GLCM. Then we compute the correlation coefficient between the adjacent windows and complete the segmentation of striped woven fabric. At last, the segmented woven fabric patterns are recognized based on the approach of gradient histogram. According to the tests, we concluded that this method can segment and recognize the striped woven fabric patterns successfully, which can overcome the effects of thickness and color of yarns changing, and uneven illumination.     

Air permeability of multilayer woven fabric systems

A model to predict the air permeability of a multilayer woven fabric system from the air permeability and construction parameters of a single fabric has been developed. A new factor to account for the distortion of the threads in the fabric due to the flow of air through the layers of the fabric is proposed. This model incorporates the fabric cover factor (determined using an image‐processing method) and the proposed distortion factor (determined based on yarn linear density and fabric structure). The performance of the proposed model has been successfully validated by using it to predict the air permeability of other multilayer woven fabric systems and comparing it to experimentally determined values. The model can thus be used as a valuable tool in the engineering design of multilayer woven structures with desired air permeability performance requirements.     

Measurement of cloth fell position using image analysis

In this paper, a computer‐aided image‐analyzing method for measuring cloth fell position directly in a non‐contact manner is introduced. The method measures the cloth fell position from an image taken by means of a camera which is mounted over the woven fabric and warp region including cloth fell on a weaving loom. Laser light lines are used as reference lines. The image containing the reference lines, the fabric and warp system is decomposed using wavelet transforms. Horizontal detail coefficient matrix is filtered with local standard deviation of a kernel for textural feature extraction and grouping achieved by means of k‐means clustering in order to determine the cloth fell position. Due to the fact that reference line positions remain the same on the fabric, cloth fell position alterations can be calculated by measuring the distance between the reference line and the cloth fell. The conversion of the distance between the cloth fell position and the reference line, measured by the number of pixels, can be accomplished by using the known distance between reference lines as a means of calibration.     

基于遗传规划的织物自适应正交小波基的构造和优化

 为了实现机织物疵点的自动检测,文章在构造织物自适应正交小波库的基础上,运用遗传规划算法,将构造的小波库作为群体规模,对遗传规划算法四种不同的适应度函数进行优选后,从群体规模中优化出与织物纹理相匹配的小波基。研究结果表明,以织物纹理波动为适应度函数得到的小波基与织物的匹配性较好。试验验证了该方法对相关疵点检测的有效性,并采用窗口分割法对织物疵点进行定位,表明采用遗传规划算法结合适应度函数优选的方法,能够找到与织物纹理相适应的最优小波基,实现织物疵点的自动检测。