织物起球等级客观评估的图像采集方法

研究了织物起球等级计算机视觉评估中单侧向照明、圆周向照明及多角度投影这3种方式下起球图像采集原理,并进行了包括规则、不规则及深色等多种织物起球图像采集的可靠性列表比较及分析.结果表明:圆周向照明采集方式为最佳方式;基于特征向量分析对多角度投影起球图像进行合成,是一种可行的采集方式;最适合起球图像评估的织物类型为非深色规则纹理织物.     

复杂纹理织物的起球图像采集及预处理方法

针对织物起球等级客观评估中复杂织物纹理干扰起球图像分割的问题,提出一种新的起球图像采集及预处理方法.首先采集同一织物的多角度光照投影图像,该图像混合有不变场景的复杂底纹和光照投影变化的起球信息;然后应用独立成分分析方法,对多角度光照起球图像进行独立成分分解;再利用频谱特征分析方法分离出属于织物底纹的独立分量,以重构出滤除织物复杂底纹的起球图像.试验结果表明,该方法能有效地滤除各种起球织物图像中的复杂底纹,为复杂纹理织物的起球等级客观评估提供可能.     

用图像分析技术评价织物起球

首先采用图像分析技术对起球织物的图像进行预处理;其次以织物起球总面积M和起球形态S等指标定量地综合评价织物的起球状况;最后以针织物为例,通过实验验证此方法的可行性.     

织物起球标准样照的图像分析

探讨了运用图像分析技术对织物起球程度做客观和定量的描述．首先对精梳毛织物的起球标准样照进行处理分析，然后提取织物起球的特征参数，分析特征值与起球等级间的相关性，再选取相关性较好的3个特征参数，归一化处理后建立特征向量模板，最后对实际织物图像进行分析处理，提取表征毛球特征的参数值描述实际织物起球信息，运用最小距离法时实际织物起球等级进行客观评定，结果与主观评定的结果一致．     

基于Wold模型的织物起球纹理非确定性成分提取

通过对Wold纹理模型理论及其在织物起球纹理分解应用可行性的分析,提出包括谐波峰状点结构、线结构及其支撑区域扩展的二维Wold分解算法流程,并设计了二维多尺度B-样条小波匹配滤波器进行峰结构奇异目标检测,提高了Wold分解的鲁棒性.几种典型起球织物纹理的Wold纹理模型分解结果表明,该分解算法能将含有毛球信息的非确定性纹理成分从织物底纹中分解出来,较好地解决了复杂织物底纹对毛球目标识别与分割的影响.     

神经网络技术在精纺粗花呢起毛起球等级评定中的应用

以精纺粗花呢纯毛织物为例,探讨纤维、纱线、织物结构因素及后整理工艺条件对精纺粗花呢织物起毛起球的影响.采用BP神经网络方法,建立纱线和织物结构参数与织物起毛起球性之间的关系.根据各种影响因素的变化预测精纺粗花呢起毛起球等级.     

织物图像中起球特征值的提取与分析

研究了织物图像中的起球特征值与起球等级之间的关系.对13个起球特征如毛球个数、毛球面积、毛球体积、毛球分行维等进行描述和提取,对15幅织物图像中的特征值与起球等级的相关性、可分离性和近似函数关系进行了分析.根据分析结果,选取10个特征值作为起球等级客观评定的依据.     

织物抗起毛起球新技术现状分析

介绍了目前一些纺织品的起毛起球成因,分析了纤维、纱线、织物、加工及服用等因素对起毛起球的影响。根据不同种类的纤维和织物(棉麻、涤纶、腈纶、毛),提出了相关的抗起毛起球新工艺。     

小波分析在织物起毛起球客观评级中的应用

本文将小波分析方法应用于织物起毛起球图像分析,建立了基于二维离散小波变换的织物起球图像分析模型,提出了优化分析小波及相应的分析尺度的步骤,并利用Haar小波的方波形状与机织物的织纹结构具有相似性的原理,采用Haar小波对机织物起毛起球进行了分析,给出了织物起毛起球客观评定方法.     

基于三维扫描技术检测针织物的起毛起球特征值

利用三维扫描仪获取了针织物表面坐标数据,在等间距网络规整预处理和小波分析降噪处理后,研究了毛球与背景分离的方法,并通过相关算法和计算机处理获取了毛球的个数、面积和体积等特征值.试验结果表明,该方法可以自动检测针织物起毛起球特征值,具有较高精度和较好可视性,并且不受针织物色彩及纹理的影响.     

Evaluation of Fabric Pilling Using Light Projection and Image Analysis Techniques

A new instrument based on light projection and image analysis techniques for assessing fabric pilling is presented. This system can automatically detect the pills from the successive sections of the fabric surface, which can make up for the limitation of both the gray level image -analysis techniques and laser triangulab'on techniques. The test of a large number of worsted wool fabric samples shows that it can objectively characterize the degree of pilling by using fuzzy sets and has good accordance with human visual inspection.     

Image Analysis of Fabric Pilling Based on Light Projection

The objective assessment of fabric pilling based on light projection and image analysis has been exploited recently. The device for capturing the cross-sectional images of the pilled fabrics with light projection is elaborated. The detection of the profile line and integration of the sequential cross-sectional pilled image are discussed. The threshold based on Gaussian model is recommended for pill segmentation. The results show that the installed system is capable of eliminating the interference with pill information from the fabric color and pattern.     

Assessing the Pilling of Fabric by Image Analysis

In this paper, firstly, the image of pilling was pre - pro-cessed by image analysis technique, which based on the image's gray- scale statistical characters or mathemati-cal morphology. Then, the pilling of fabric was assessed synthetically by the size of pilling A,number of pilling N and morphology of pitting S. At the end, we tested the practicability of this method by testing knitgoods as a sample, and the result was satisfiable.     

Pilling Segmentation for Objective Pilling Evaluation

IntroductionPillingonfabricsisaquiteunpleasantfeaturewhichaffectsthevisualquality.Pillingisgenerallyformedinfourstages:fuzz,entanglement,growthandwearoff[1].Dailywashingandwearingarethecommonsourcefortheformation.Thedurabilityofagarmentisthereforedeterminedbythetimeusedtoformsuchsmallfuzzballsonsurface.Inthelaboratory,fabricsarecutintosizeandplacedintopillingtesterstosimulatethesituation.ASTMprovidedseveralguidelines[2—5]fortheinspectionwhileAATCCgaveageneraltestingprocedure[6]onpillingre…     

Assessing Texture of Slub-Yarn Fabric Using Image Analysis

The application of digital image processing to the classification of the slub-yarn texture is discussed. Texture of the slub-yarn fabric is analyzed by using the texture analysis techniques. The influence of the slub-yarn parameters on the fabric texture is discussed. Results indicate that texture of the slub-yarn fabric can be reliably measured using gray level co-occurrence matrix （GLCM） analysis. The four indices of GLCM, the angular second moment, the contrast, the inverse difference moment and the correlation, are sensitive to the change of the slub-yarn parameters, and can be regarded as the major indices for the texture.