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%.     

Investigation of image registration method for the multi-directional image fusion of woven fabrics

Abstract

Traditional single-side scanning or single-vision image acquisition methods have the limitation of incomplete information caused by the existence of blind spots. To collect the complete texture information of fabric images, a new multi-vision image acquisition and the related fusion method is developed to solve this problem. However, linear addition of image sequences acquired from multiple directions cannot achieve a good result of image fusion, it is necessary to conduct the image fusion based on the image registration between images at pixel level. Therefore, a new multi-directional digital image acquisition system for woven fabrics is established in this article; one set of image fusion algorithm based on image registration is proposed for the image enhancement of fabric. Fabric texture images are digitized by means of multi-directional vision imaging instead of unidirectional imaging, the structural information of fabric texture could be enhanced using image registration and fusion technology and the indexing and localization of texture corresponding points could be controlled using matching points or control points. Our experimental results show that the proposed method could be used to merge the effective information from the multi-directional vision images completely, it has the potential application for the rendering of woven fabrics using image driven virtual reality enhancement.     

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.     

机织物密度对字典学习纹理表征的影响

为探讨纺织品表观质量的客观、智能评定方法,使用不同密度的机织物图像,采用子窗口样本获取方式作为学习样本,以离散余弦字典作为初始学习字典,选择基于最小二乘的字典学习算法求解用于表征织物纹理图像的字典,再通过字典元素的线性组合对织物图像进行重构。以均方误差为指标,首先讨论织物图像灰度值分布对字典学习算法重构误差的影响,然后对图像灰度值进行标准化处理,在此基础上探讨织物经纬密度对重构图像误差的影响。实验结果发现,当字典个数等于9时,织物密度在150 ~ 360根/10 cm之间,随着织物密度的增加,平纹重构图像的均方误差先变大,以后不再增加,而斜纹重构图像的均方误差增大。     

喷水织机织物设计与可织性研究

根据喷水织机的生产特点，织物设计时不仅要考虑风格特征，还要注意其可织性。文章从小提花或条型织物的织缩性差异，织物紧度，纬线捻线型的捻系数这三个方面讨论了织物的可织性，并给出了正常生产的量值范围，可供喷水织机品种设计的参考。     

纹理织物疵点窗口跳步形态学法检测

针对纹理织物疵点自动检测时因生产速度快造成的织物抖动以及检测速度难以匹配问题,提出窗口跳步形态学法纹理织物疵点检测算法。使用该算法对图像进行窗口分割及预处理后,首先对纹理织物图像的纹理特征进行分析,然后设计形态学算子进行腐蚀操作,最后使用连通域分析来确定疵点大小及位置。仿真实验及工厂实际应用表明,该算法可有效克服工业生产中纹理织物抖动造成的图像明暗不均,可检测出纹理织物中存在的破洞、经纬疵点、污渍、断线、折痕和结头等各种疵点,而且检测速度明显优于快速傅里叶变换特征点算法以及传统形态学检测算法。实时检测速度超过80 m/min,疵点检测精度为0.1 mm,满足实际生产需求。     

单层曲面机织布的织造技术研究

在现有织机上，借鉴部分成熟机织技术并通过对织物成形机构进行改造，获得了单层仿形缠绕织物。相对于传统不等纬密仿形织物，该织物织造起来较复杂，除了要添加短纬纱线，还要做到组织循环中长、短纬纱分配合理。但该织物结构均匀，力学性能好，同时具有机械化生产能力，应用前景广阔。     

Development of automatic weaving technique of tapered circular tubular woven graft: a preliminary validation

The tapered circular tubular (TCT) woven fabric has been applied successfully as the components in vascular prostheses. The application of tapered tubular woven fabric based on the existing weaving technology requires the densification of a large end to ensure porosity uniformity through the tapered tubule. Although the previously proposed equal-cover-factor (ECF) design technique overcame some disadvantages of the conventional densification methods, such as the protruding portions of the dropped yarns as well as the change of both fabric porosity and tensile strength, the off-loom weft density significantly deviates from the on-loom value. On the basis of ECF design technique and the basic shed geometry, this study rebuilt the relative motion relationship among the front rest, the cloth fell, the back rest, and the take-up length, and modified the weft density on loom to achieve continuous weaving process of TCT fabric as well as the uniform porosity. And then, a computerized weaving procedure based on the rebuilt relationship was developed to automatically weave TCT fabric with different weaves on the customized shuttle loom. As a result, the uniform porosity of those samples validated the proposed weaving techniques.     

基于离散余弦变换过完备字典的机织物纹理稀疏表征

为进一步研究基于字典学习的机织物纹理表征算法的稳定性与可比性,提出用离散余弦变换（DCT）过完备字典稀疏表征算法来重构织物纹理图像。重点探讨了稀疏度、子窗口大小、字典个数对纹理表征效果的影响,利用均方根误差和峰值信噪比指标对机织物原图与重构图像之间的近似程度进行量化,并确定最终优选的稀疏度为10,子窗口大小为8像素×8像素,字典个数为256。实验结果表明,所提方法不仅方便快捷,还可得到较好的表征效果。此外,其DCT过完备字典峰值信噪比值仅次于基于训练的自适应学习字典,且优于主成分分析和非稀疏表征算法约4 dB。     

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.     

织物纹样特征提取与匹配方法比较

针对织物纹样自动识别过程中因尺度、旋转和褶皱等因素引起图像差异的问题,探索了复杂纹样特征的准确提取与匹配方法。以江崖海水纹样为例,采集尺度、旋转、模糊、光照、褶皱5种变化下的织物纹样图像,分别运用尺度不变特征变换(SIFT)、快速鲁棒性尺度不变特征(SURF)、二进制鲁棒不变可扩展关键点(BRISK)3种方法提取纹样局部特征,然后采用欧氏距离进行特征匹配计算,最后通过随机抽样一致算法剔除误匹配对。结果表明:采用BRISK算法的准确配对率最高,平均准确匹配率达87.10%;褶皱对织物特征匹配的影响最大,该变化下BRISK算法的鲁棒性优于SIFT和SURF算法;BRISK算法速度最快,图像平均匹配时间0.551 s;在织物纹样特征匹配中,BRISK算法比SIFT和SURF算法具有更好的适用性。     

机织物图像自动纠偏方法的研究

图像纠偏在织物组织结构参数自动识别过程中直接影响识别结果.本文通过对织物图像去噪、直方图均衡化等预处理操作后,对图像进行水平和垂直边缘检测;同时为提高倾斜角度识别的准确度和算法运行效率加入细化处理,然后对细化后的图像进行Hough变换,检测倾斜角度,最后对图像进行旋转实现旋转纠偏.通过对不同机织物图像进行纠偏处理实验,证明了该方法的有效性和可靠性.     

采用Gabor滤波簇和等距映射算法的织物疵点检测方法

为提高织物疵点检测率,将Gabbor滤波法与等距映射方法进行融合,克服疵点检测过程中存在的问题。首先用由3个尺度和5个方向组成的15个Gabor滤波器簇对织物疵点图像进行滤波,减少疵点图像光照不均和对比度低的影响;然后将滤波图像划分成面积相等且互不重合的邻域,并从邻域中提取高维特征向量。采用等距映射方法对高维特征向量进行降维,剔除高维特征中冗余信息,强化分类器拟合能力;再用低维嵌入模型提取新增样本低维特征向量,用于概率神经网络分类器分类,检测是否存在疵点;最后用2种不同纹理的织物进行检测实验。结果表明,本文方法能有效提高疵点的检测精度。     

Neural network trained morphological processing for the detection of defects in woven fabric

Basic morphological operations such as the erosion, dilation, opening, and closing often fail to detect various types of defects that may be present in woven fabric, mainly because of the heuristic selection of structuring element needed for these operations. In this paper, an artificial neural network (ANN) is utilized for the selection of structuring element, where ANN is trained by two pre‐assigned normalized numbers related to the warp and weft counts of the test fabric. The test gray fabric image is pre‐processed to remove noise and the interlaced grating structure of weft and warp and then converted to a binary image by thresholding. An intensity threshold value of the processed fabric image and the dimension of a sliding window needed for correlation operation are obtained from the trained ANN. Defects are detected after morphological reconstruction of the processed binary fabric image, where an ANN trained structuring element is used. The technique is tested on 317 samples for eight different types of defects in three types of plain woven fabrics from TILDA database and 92.8% success of detection is achieved.     

基于图像技术的织物导湿性能测试方法

为提高织物导湿性能测试的准确性,在传统滴液法的基础上借助摄像机获得织物润湿图像后,利用同态滤波对图像进行增强预处理,再经阈值分割和形态学处理后,提取到织物的导湿面积对其导湿性能进行表征。重点分析了测试用液体、滴液高度、滴液用量及图像采集样本数量等参数对导湿性能测试结果稳定性的影响。结果表明:图像技术方法较传统方法更快速准确,稳定性和再现性好;以机织物作为测试对象时,选择测试液体为去离子水,单次滴液量为40 μL,滴液高度为2 cm时,重复实验5次,此时织物导湿面积测试结果变异系数小,测试方法稳定性较高。     

机织滤布鼓泡孔径测试与分析

为探索机织滤布的孔隙及其分布情况,使滤布在设计和选择的过程中有所依据,对滤布的孔径问题进行了探讨。采用鼓泡孔径表示滤布的孔隙及其分布,测试分析滤布鼓泡孔径与织物结构的关系。结果表明:滤布的鼓泡孔径随着织物密度的增大而减小;纱线捻度增大则使滤布的鼓泡孔径加大;织物组织对滤布鼓泡孔径有影响,平纹组织的孔径最小,斜纹组织的孔径最大;滤布的孔径越小,其孔径的均匀性越好。     

基于图像处理的网格圈织物孔隙率检测

针对目前网格圈织物的孔隙测试相对困难的问题,本文采用图像处理方法对其孔隙及孔隙率的检测进行研究。首先对扫描所得的网格圈织物图像采用阈值分割方法将其转换成二值图像,对该二值图像可利用投影法判断经纬纱直径,计算得到理论孔隙率;然后对二值图像中纱线主体部分的干扰区域进行剔除,图像取反;最后通过面积滤波后提取孔隙数量、孔隙面积大小、孔隙分布以及计算实际孔隙率等指标,并且对面积过小的孔隙进行位置标定。实验结果表明,用图像处理方法对网格圈织物进行检测,能够客观反映网格圈织物的孔隙率和孔隙分布的均匀性,且实际孔隙率远低于理论孔隙率。     

应用纱线序列图像的电子织物构建

为检测纱线条干均匀性对织物外观的影响,在纱线条干图像测量的基础上,提出了一种基于纱线序列图像的电子织物的构建方法。通过建立织物组织变化模型和光照模型,将纱线直径值与基元组织点外观灰度纹理分布相结合,构建电子织物外观数学模型。实验中通过将采集的纱线序列图像进行图像分割和形态学运算等处理,获取纱线直径数据,代入到构建的织物外观数学模型中,实现基于纱线序列图像的电子织物的模拟并且相关参数可调。通过选择合理的织物结构参数,提出的电子织物模型能够真实的反映纱线条干均匀性对织物外观的影响,准确预测布面效果。     

针织圆纬机牵拉张力闭环控制技术

针织圆纬机编织过程中,牵拉卷取机构的结构和性能对织物质量有很大影响,为此,针对提花编织时针织圆纬机现有的机械式牵拉卷取机构和开环电子式牵拉卷取机构牵拉力偏差大,易导致织物密度不均匀,表面不平整,甚至出现织物组织结构被破坏等问题,提出了一种针织圆纬机牵拉张力闭环控制方法,在开环电子式牵拉卷取机构的基础上进行优化设计,结合嵌入式技术、智能控制技术和现代传感器技术,开发了针织圆纬机牵拉张力闭环控制系统。详细阐述了牵拉卷取机构的结构优化以及控制系统的硬件设计和软件开发。经过理论分析和实际测试证实,张力闭环控制系统实时性好,牵拉张力动态稳定,可满足编织过程中织物平整度要求,设计方案合理可行。     

基于织物透光图像分析评价防钻绒性的方法

为快速、准确、经济地测试织物的防钻绒性能,提出一种采用数字图像处理技术,基于织物透光灰度图像的防钻绒性测试评价方法。利用Photoshop软件放大扫描所得织物的透光数字图像,获取3~5个较大孔隙最亮处的灰度值,通过计算平均透光率,判断织物属于物理防钻绒织物还是化学防钻绒织物;对于物理防绒织物运用MatLab软件按流程图像导入→阈值确定→二值化处理→形态学处理→连通域标记→连通域面积计算对图像进行处理,得到织物的大孔隙频率分布,再与防钻绒织物的极限孔隙分布相比较,以确定织物的防钻绒性能。结果表明:基于织物透光图像的防钻绒性测试评价方法准确可行、简单快捷,而且能节约试验材料。