基于二层自适应正交小波的疵点检测

本文提出了基于二层自适应正交小波的织物疵点检测方法。首先介绍了织物纹理图像的二维正交小波变换．在此基础上借鉴Daubechies小波构造过程，阐明了二层自适应正交小波的构造方法，然后对图象进行二层小波分解。分别比较无缺陷图像与待检测图像二层分解后的纬向和径向子图像，得到纬向和径向疵点信息，最后将两个方向上的疵点信息融合．得到检测结果。实验证明该方法是有效的。     

一种新的基于纹理分水岭的纺织品缺陷检测方法

纺织品缺陷检测是纺织品自动检测的重要环节,而纺织品缺陷检测的目的是为了准确地对纺织品的缺陷区域进行定位.为了对纺织品缺陷进行准确有效的检测,提出了一种新的基于纹理分水岭的纺织品缺陷检测方法.该方法首先利用小波变换提取了图像的各子带纹理特征;然后对各子带纹理特征求梯度,并通过融合各子带梯度来获得纹理梯度,使其在纹理梯度中能有效地突出纹理区域的边界;最后在此基础上,结合分水岭分割,即能准确地检测出纺织品的缺陷区域.通过对一组6类纺织品缺陷进行检测的实验证明,该新算法是有效的.     

Fabric defect detection based on saliency histogram features

In order to increase the automatic quality control level in the textile industry, depending on the big data collected by the Internet of things of the textile factories, this paper proposes a novel visual saliency–based defect detection algorithm, which has the capability of automatically detecting defect in both nonpatterned and patterned fabrics. The algorithm employs the histogram features extracted from the saliency maps to detect the fabric defects. The algorithm involves three main steps: (1) saliency map generation to highlight the defective regions and suppress the defect‐free regions, (2) saliency histogram features extraction and selection to obtain the feature vectors that can effectively discriminate between the defective and defect‐free fabric images, and (3) fabric defect detection using a two‐class support vector machine classifier that has been trained using sets of feature vectors extracted from defective and defect‐free fabric samples. Experimental results show that our method yields accurate detections, outperforming other state‐of‐the‐art algorithms.     

基于离散小波变换的织物疵点检测方法

提出了一种基于离散小波变换的织物疵点检测新方法。首先通过对采集织物图像进行小波变换,然后把子图分割为相互连接、互不交叠的子窗口,计算每个子窗口小波系数的标准差作为特征值,最后对这些特征值再次计算标准差与极差,以此作为依据与正常织物进行比较,实现对织物疵点的检测。通过对不同疵点进行检测实验,证明了该算法是可行有效的,检测的正确率平均可达90%以上。     

Motif-based defect detection for patterned fabric

This paper proposes a generalized motif-based method for detecting defects in 16 out of 17 wallpaper groups in 2D patterned texture. It assumes that most patterned texture can be decomposed into lattices and their constituents—motifs. It then utilizes the symmetry property of motifs to calculate the energy of moving subtraction and its variance among different motifs. By learning the distribution of these values over a number of defect-free patterns, boundary conditions for discerning defective and defect-free patterns can be determined. This paper presents the theoretical foundation of the method, and defines the relations between motifs and lattice, from which a new concept called energy of moving subtraction is derived using norm metric measurement between a collection of circular shift matrices of motif and itself. It has been shown in this paper that the energy of moving subtraction amplifies the defect information of the defective motif. Together with its variance, an energy-variance space is further defined where decision boundaries are drawn for classifying defective and defect-free motifs. As the 16 wallpaper groups of patterned fabric can be transformed into three major groups, the proposed method is evaluated over these three major groups, from which 160 defect-free lattices samples are used for defining the decision boundaries, with 140 defect-free and 113 defective samples used for testing. An overall detection success rate of 93.32% is achieved for the proposed method. No other generalized approach can achieve this success rate has been reported before, and hence this result outperforms all other previously published approaches.     

基于小波和奇异值分解的图像边缘检测

针对传统图像边缘检测抑制噪声能力弱的问题,给出了一种小波变换和局部梯度 场内奇异值分解相结合的边缘检测方法。首先在图像预处理阶段,为了提取准确的边缘特征, 文中利用小波变换的时频局部化特性,对图像进行小波变换。该文对用小波求取的梯度场使用 局部梯度奇异值分解的方法;利用奇异值的特性和良好的稳定性,使提取的边缘特征更加突出 并且能够达到抑制噪声的目的。实验证明该文方法既能在无噪声影响的图像中提取出清晰完整 的单边缘,又能在有噪声干扰的情况下提取出理想的边缘。     

基于轮廓波变换的织物疵点图像消噪新方法

为了实现织物疵点图像的有效消噪,使其更有利于特征提取和疵点检测,提出了基于轮廓波变换的织物疵点图像消噪新方法。综合考虑轮廓波方向子带能量的大小与织物疵点图像轮廓细节之间的关系,对Donoho多尺度分解阈值进行修正,改进了Donoho多尺度分解阈值对图像细节"过扼杀"的缺点。实验结果表明,对织物疵点图像进行基于轮廓波变换改进阈值消噪时,该方法更好地保留了织物疵点图像的轮廓细节,峰值信噪比显著提高。采用改进的轮廓波Donoho多尺度分解阈值消噪后的图像,可以更好地应用于织物疵点图像的特征提取和疵点识别。     

基于小波分析的织物疵点自动检测方法研究

本文提出了一种织物疵点自动检测方法,用具有高分辨率的CCD摄像头在可移动云台及可调标准光源的照射下捕获织物图像并存储,然后使用数字图像处理方法和小波变换技术将织物图像中的疵点部分分离出来进行识别。该方法其具有检测速度快、定位准确等优点。     

基于Gabor小波和神经网络的布匹瑕疵检测

为了实现布匹表面瑕疵的在线视觉检测,利用Gabor小波函数与神经网络的结合,提出了一种有效提取Gabor滤波最优参数的方法。该方法通过离线构建Gabor小波神经网络,结合Levenberg-Marquardt算法优化得到最优解,重构无瑕疵的布匹图像,以削弱在线检测时布匹纹理对瑕疵检测的影响,从而能够于在线实时监测过程中凸显布匹瑕疵,最终从融合图像中得到瑕疵区域。通过对霉点、断经、油污、破洞四种常见的布匹瑕疵图像进行检测,表明该方法能够满足对瑕疵的实时分割要求。     

基于小波变换的图像边缘检测匹配算法

小波变换能够获取信号的时频局部化特征,可作为一种有效的图像边缘检测工具。本文提出一种基于二维小波变换的图像边缘检测方法。首先对图像分别做两方向的小波变换,进而得到二维小波变换的幅值和梯度,然后利用非极大值抑制方法检测二维小波变换的模极值点作为图像的边缘点,最后利用Hausdorff算法实现模板边缘与实时图边缘之间的匹配。仿真试验表明本文所提出算法的有效性。     

An automated inspection system for textile fabrics based on Gabor filters

This paper studies the application of advanced computer image processing techniques for solving the problem of automated defect detection for textile fabrics. A new defect detection scheme is proposed, which consists of an odd symmetric real-valued Gabor filter, an even symmetric real-valued Gabor filter and one smoothing filter. In developing the scheme, the Gabor filters are designed on the basis of the texture features extracted optimally from a non-defective fabric image by using a Gabor wavelet network (GWN). The performance of the proposed defect detection scheme is evaluated off-line by using a set of fabric images taken from a database consisting of a wide variety of homogeneous fabric images. The results exhibit accurate defect detection with low false alarms, thus showing the effectiveness and robustness of the proposed scheme. To evaluate the performance of the proposed defect detection scheme further, real-time tests are conducted by using a prototyped automated defect detection system. The experimental results obtained further confirm the efficiency, effectiveness and robustness of the proposed detection scheme.     

基于Radon小波低分辨率的织物疵点检测算法

针对纺织过程中可能出现的瑕疵问题,提出了一种新的织物疵点分割方法--四分法和织物疵点特征提取方法--Radon小波低分辨率特征(RWLRC)。该算法先将织物图像经过Gabor滤波器预处理,再将预处理之后的织物图像等分成四部分,通过4部分的最大值与最小值确定阈值并分割。将疵点形状的二值图像进行Radon变换并得到特征曲线,应用Mallat塔式分解算法进行特征降维,最后由神经网络进行状态识别和特征分类。实验结果表明,四分法无需与正常织物对照分割,具有自适应性,Radon小波低分辨率特征的特征值只有3维,具有特征维数低、疵点形状描述准确等特点,所提方法可以有效检测与识别缺纬、缺经、油污、漏洞等常见疵点。     

基于特征残差的色织物瑕疵检测

为解决自动织物瑕疵检测算法中,未知花色织物瑕疵检测困难的问题,提出了一种基于特征残差的色织物瑕疵检测方法.首先使用瑕疵织物图像与模板织物图像的瑕疵残差和正常无标注织物图像进行融合,生成新花色瑕疵织物样本;然后改进特征提取网络采用共享权值方法,对瑕疵织物和模板织物提取特征后计算得到特征残差;最后使用ROIAlign方法将全局上下文信息缩放到和感兴趣区域统一大小后进行特征融合,对融合特征进行瑕疵分类和位置回归.实验针对不包含未知花色和包含未知花色的不同测试集分别进行算法测试实验,结果表明改进后的算法能够较好地消除织物花色对检测结果的影响,在不包含未知花色的测试集中精度得到了较大的提升,在包含未知花色的测试集中,瑕疵检测效果依旧保持不错的精度,相较于改进前的通用算法,最终score分别提升了15.4％和16.2％.     

基于最小分类误差小波特征的纺织品缺陷分类方法研究

纺织品缺陷分类是利用计算机视觉技术检测纺织品品质的一个关键环节。提出了一种基于小波框架的纺织品缺陷分类新方法。该方法使用纺织品图像的小波框架来描述缺陷的纹理特征。在最小分类误差训练框架下,通过联合设计一个基于线性变换矩阵的特征提取器和一个分类器,来获取面向缺陷分类的小波框架特征,并最小化分类器的错误概率。该方法对包含9类纺织品缺陷的329个样本,以及328个无缺陷样本进行了分类实验评估,获得了931%的分类准确率,相比传统的基于小波变换的分类方法提高了272%。     

基于傅里叶特征谱和相关系数的织物疵点检测

为提高织物疵点自动检测的准确度,提出一种基于傅里叶特征谱和相关系数的织物疵点检测算法。以平纹、斜纹织物为研究对象,对织物图像进行傅里叶变换,得到织物图像的频谱图;定位频谱中的特征峰点,提取表征图像灰度、纹理的五个特征值;以正常织物为模板,计算待检图像特征值与模板图像特征值之间的相关系数,确定用于识别织物疵点的阈值,来实现织物疵点检测。实验结果表明:当阈值设定为0.80时,该算法能够实现稀密路、断经、吊经、纬缩、破洞等常见疵点的准确检测。     

人脸识别中应用小波变换的三个关键问题

由于小渡分解在空域和频域上都能提供良好的局部信息,尤其是在小渡分解后可以减少图像的分辨率,进而相应地减少计算复杂度,因此小波变换经常用于图像处理和图像分析中.本文讨论小波变换用于人脸识别的三个关键问题:小波基的选择、分解层数和小波子图的确定,通过实验分析和比较研究得出相应的答案和指导原则,并提出了一种新的人脸识别方法.     

Fabric defect detection using local contrast deviations

Defect inspection is a vital step for quality assurance in fabric production. The development of a fully automated fabric defect detection system requires robust and efficient fabric defect detection algorithms. The inspection of real fabric defects is particularly challenging due to delicate features of defects complicated by variations in weave textures and changes in environmental factors (e.g., illumination, noise, etc.). Based on characteristics of fabric structure, an approach of using local contrast deviation (LCD) is proposed for fabric defect detection in this paper. LCD is a parameter used to describe features of the contrast difference in four directions between the analyzed image and a defect-free image of the same fabric, and is used with a bilevel threshold function for defect segmentation. The validation tests on the developed algorithms were performed with fabric images from TILDA’s Textile Texture Database and captured by a line-scan camera on an inspection machine. The experimental results show that the proposed method has robustness and simplicity as opposed to the approach of using modified local binary patterns (LBP).     

方向性纹理织物疵点检测方法研究

通过分析方向性织物纹理的特点,提出了一种织物疵点检测新的方法。首先根据正常纹理Hough变换确定织物纹理的纹路方向;然后采用方向性小波对织物纹理图像进行方向性的分解,并在此基础上从分解后的各细节子图中提取子窗口的特征;最后通过BP神经网络进行织物疵点识别。实验结果表明了该方法的有效性。