基于质量分级法的织物毛细孔径分布测定方法

为有效控制染色、印花、后整理等织物湿加工过程中的产品质量并设计出具有较好热湿舒适性能的服装面料,根据在织物毛细孔中液体前沿弯月面的laplace压力和液柱静压间的平衡关系,提出基于质量分级和差减数据测定沿织物平面毛细孔径分布的理论依据和方法。用自制的实验装置测定棉织物中毛细孔径分布,得到重现性良好的孔径分布直方图和分布曲线图。研究发现,织物的经纬密对孔径分布、平均孔径和最大比例孔径都有显著的影响。     

曲线匹配算法及其在织物自动对花检测中的应用

针对圆网印花过程中的对花检测问题,在提出的基于机器视觉的圆网印花检测系统的基础上,提出了利用曲线匹配策略得到错花套色的位置误差矢量的方法。为了确保目标边缘图像与标准边缘图像拍摄位置相同,利用Fourier-Mellin曲线匹配,对其进行匹配消除拍摄误差;然后用基于傅里叶变换位移定理的曲线匹配算法,对调整好的目标边缘图像和标准边缘图像进行匹配,检测对花误差。该匹配策略消除了拍摄误差,数据运算量小,实验表明其检测精度和运算速度可以满足织物对花在线检测的要求。     

采用纹理平滑的印花织物图案轮廓提取

为丰富印花产品的种类,提出了一种可有效提取出印花织物中图案轮廓的方法,可提取出高精度的印花图案边缘轮廓。首先对图像进行平滑,通过控制平滑程度参数和空间尺度参数,平滑掉织物图像中的纹理结构。再用Canny边缘检测算子检测图案边缘,Canny分割的判别阈值采用默认自动选择阈值就能成功提取织物上图案的轮廓,分割后的图像轮廓清晰,边缘连续,并且能分割出印花织物图像中的细小结构。通过实验证明,在RGB颜色空间对图像平滑和边缘提取的效果要优于其他颜色空间,比较了其他边缘检测算子分割印花织物图案的效果,结果证明Canny算子分割效果最好。     

Content-based lace fabric image retrieval system using texture and shape features

A content-based lace fabric image retrieval system using texture and shape features is presented in this article. The retrieval system consists of two steps: registration and identification. During the registration procedure, a feature vector corresponding to each lace fabric sample image is extracted and then stored in a feature database. During the identification procedure, a feature vector of a lace fabric query image is extracted and then the similarities between the query image and all the sample images are calculated. Finally, the retrieval results can be sorted based on their similarities to the query image. Experimental results demonstrate the effectiveness of retrieval performance of the proposed algorithm and possible practical application of the retrieval system in lace fabric industry to improve management efficiency.     

应用方向梯度直方图和低秩分解的织物疵点检测算法

织物疵点检测是织物表面质量控制的关键环节。基于方向梯度直方图(HOG)和低秩分解,提出一种有效的织物疵点检测算法。首先,将织物图像划分为大小相同的图像块,提取每个图像块的HOG特征,并将图像块特征组成特征矩阵,针对特征矩阵构建有效的低秩分解模型,通过方向交替方法(ADM)优化求解,生成低秩阵和稀疏阵;最后采用改进最优阈值分割算法对由稀疏阵生成的显著图进行分割,从而定位出疵点区域。实验结果表明,低秩分解能有效实现织物疵点的快速分离,与已有方法进行对比,本文方法能显著提高复杂织物纹理图像的疵点检测性能。     

应用最优Gabor滤波器的经编织物疵点检测

针对经编织物疵点自动检测问题,提出了一种新的基于最优Gabor滤波器的经编织物疵点检测方法。具体可分为学习阶段和检测阶段;在学习阶段,对于无疵点的经编织物图像构造可调制的二维Gabor滤波器,采用量子行为粒子群优化(QPSO)算法对Gabor滤波器的参数进行优化,得到与无疵点的织物图像纹理特征最匹配的Gabor滤波器参数;在检测阶段,由学习阶段得到的最佳参数构造Gabor滤波器,用该滤波器对待检测织物图像进行卷积处理,然后再对得到的卷积图像进行二值化处理,最终识别出待检测织物是否有疵点存在。结果表明,该方法的检测率可以达到96.67%,具有很好的稳定性和鲁棒性,适合应用于工业生产。     

Application of Wavelet Transform in Characterization of Fabric Texture

Abstract

The authors have developed an image processing system and a wavelet transform (Symlet Wavelet) based processing technique to find the repeat texture of fabric without any priori information. The method is also capable of characterizing the texture of fabric not having an obvious repeat pattern. The proposed methodology is able to measure the warp and weft diameter, and spacing per unit length per yarn and percentage of their coefficient of variation (CV%).     

基于数字图像相关法的纱线和织物微观变形测量

为连续、精确地测量纱线和织物组织单元的拉伸变形量,结合数字图像相关法(DIC),提出一种微观尺度下的单轴拉伸变形测量方法。首先选取4种织物,从织物中抽取纱线、裁剪织物试样,并制作合理的散斑。然后对纱线和织物试样分别进行单轴拉伸实验并用相机记录整个过程。最后在DIC测试软件中添加引伸计,计算拉伸过程中单纱分段线应变以及织物单元结构在经、纬方向上的线应变。结果表明：单纱拉伸过程中,各处线应变增长速率不同;织物微小变形下内部结构呈非均匀变形,其中纬编针织物线圈变形不均匀性更为明显。基于DIC的微观变形测量方法揭示了纱线和织物生产、应用中的微量变形规律,为智能可穿戴电子纺织品的设计与应用提供指导。     

采用距离匹配函数的印花织物图案周期测定

为实现自动检测织物图案周期和印花织物质量的有效评估,保证在纺织工业中图案织物生产的高速性和织物的高质量性。本文运用距离匹配函数计算印花织物周期,基于印花织物图案的周期性,本文首先计算印花织物图案的各行和各列的一维距离匹配函数,然后对织物所有行和所有列的一维距离匹配函数求和作为行和列的二维距离匹配函数。当距离匹配函数中的周期变量为织物图案周期时,印花织物距离匹配函数二次前差分得到最大值。实验结果表明,距离匹配函数测量各类织物周期具有高效性和准确性。与传统的自相关函数检测织物周期相比,该算法检测织物周期准确且可检测织物各种图案的周期。     

应用上下文视觉显著性的色织物疵点检测

为实现色织物疵点的有效检测,提出一种应用上下文视觉显著性的疵点检测方法。根据上下文视觉显著性的原则,将织物图像分为大小相同的图像块;然后针对每个图像块,选取K个与其最相似的图像块计算与该图像块的差异值之和,用该差异值之和表示该图像块中心像素的显著性;从而生成一幅视觉显著性图;最后对显著性图进行阈值分割,得到色织物疵点的检测结果。为验证该算法的有效性,将带有纬缩、破洞和跳花等区域性疵点的素色、条纹和格子色织物图像作为样本进行检测。结果表明:该方法可较好地抑制不同种类织物的纹理背景,突出疵点区域,实现疵点的有效检测,该方法在色织物疵点检测上具有一定的可行性。     

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.     

应用机器视觉的织物表面绒毛率测试系统

目前织物表面绒毛含量大都采用人工方式检测,存在效率低、准确度不高等问题。为此,应用机器视觉和图像处理技术,研制了一套织物表面绒毛率测试系统。介绍了织物表面绒毛率测试原理,包括织物表面绒毛率检测数学模型、检测算法和阈值的确定方法,并介绍了织物表面绒毛率测试系统的软硬件组成。采用该测试系统检测了5种织物的表面绒毛率,并与人工检测结果进行了对比分析。结果表明：该测试系统能够高效地测定织物表面绒毛率,且与人工检测结果呈现高度正相关;系统重复检测偏差范围为1.18%～7.25%,可满足织物表面绒毛率的检测需求。     

结合图像频域和空间域的纬编针织物密度检测方法

为解决当前基于图像处理测量纬编针织物密度准确度不高、稳定性较差等问题,提出一种结合频域和空间域来测定针织物密度的方法。首先通过离散小波变换重构针织物线圈图像获得线圈结构清晰的图像,再分别提取线圈灰度及二值图的灰度曲线。利用提出的基于概率密度统计的波谷坐标校验算法,结合线圈坐标校验算法获得实际线圈所在列坐标;再利用八邻域广度优先搜索算法求出纬编针织物横、纵方向的线圈个数,得到针织物横密和纵密。结果表明,密度检测方法与人工测量数据相比误差小于1.7%,该方法适用性好,运算量小,准确率高,可实现纬编针织物密度的自动化测量。     

A computer vision-based system for automatic detection of misarranged warp yarns in yarn-dyed fabric. Part I: continuous segmentation of warp yarns

In textile and garment industries, misarranged warp yarns of yarn-dyed fabrics disorganize the layout of fabrics and lead to poor product quality. This series of studies aims to develop a computer vision-based system for automatic detection of misarranged color warp yarns in terms of high efficiency and good accuracy. Four main parts are included in this series of studies: warp yarn segmentation, fabric image stitching, warp regional segmentation, and yarn layout proofing. This paper proposes a continuous segmentation method of warp yarns to detect the misarranged color warp yarns for yarn-dyed fabrics automatically, which is the foundation of the developed computer vision-based system. The proposed framework consists of two main components: warp yarn segmentation and fabric image stitching. Firstly, the sequence images of a fabric stripe are captured using a designed offline image acquisition platform. Secondly, the warp yarns in the sequence images are segmented by a sub-image projection-based method successively. Thirdly, the sequence images are stitched by a yarn-template matching method based on their warp segmentation results. Finally, the continuous segmentation result of warp yarns is saved for the further processing of warp regional segmentation and color warp layout proofing. The proposed method has been evaluated on 720 fabric images of five fabric examples with plain and 2/2 twill, and experimental results show that the proposed method can realize the continuous segmentation of warp yarns in yarn-dyed fabrics with the yarn segmentation accuracy of 97.43% and image stitching accuracy of 99.53%.     

基于显著纹理特征的织物疵点检测方法

针对显著纹理背景下织物图像灰度级有限、对比度不明显致使目标疵点自动检测难度较大的问题,提出了一种用于显著纹理背景的织物疵点检测方法。首先,鉴于Tamura纹理模型具有分辨能力强、旋转不变性以及算法鲁棒性强的特点,提出了多尺度度量局部纹理粗糙度的改进算法,以增强纹理分辨能力;然后,结合织物疵点图像视觉显著性分析,基于局部纹理最佳窗口,通过提取与融合粗糙度、对比度和方向生成视觉显著性特征图,以显著突出织物疵点区。经TILDA织物纹理图库数据的实验测试,结果表明,与其他相关方法相比,此方法在有效抑制显著纹理背景的同时,检测的目标疵点具有较好的一致性和完整性。     

Detection of defects in fabrics using subimage-based singular value decomposition

Matrix singular value decomposition technique is employed for the detection of defects in fabrics. Firstly, a region of interest (ROI) containing the defect is identified by a proposed adaptive partitioning technique – thus reducing the computational duty of operating over the whole image. The ROI portion of fabric image is then divided into small nonoverlapping subimages to further reduce the computational complexity and the average singular values of the subimages are calculated. To remove the interlaced warp–weft grating structure from ROI, which is the global information in the fabric image, selected singular values associated with positive average singular numbers are rejected and the fabric image is reconstructed to yield the image of the defect. Since the resulting image is saturated with noise and some unconnected parts mainly due to dissimilarity of the subimage of the fabric structure, postprocessing is carried out by binarization and edge detection to yield the edge map of the defect. Validity and feasibility of the proposed approach is established for detection of defects form images of TILDA database. The detection rate of 95% and detection success rate of 94.1% are achieved when tested over 460 samples.     

织物表面疵点检测方法的设计与实现

针对传统织物生产企业中,人工检测织物存在瑕疵检出效率低、误检率高的问题,提出了一种织物表面疵点检测方法。该方法首先采用高斯滤波、线性归一化以及限制对比度自适应直方图均衡化对织物表面图像进行预处理,从而有效增强图像中的疵点表现细节,然后通过改进的Gabor优化选择,再对选择后的图像进行初分解,从中挑选出最优滤波图像进行二值化处理,最后运用统计学方法进行疵点判断并获得最终结果。该方法实现简便、硬件要求低、适应性广,可用于判断织物表面是否含有疵点,并定位疵点。实验证明,织物表面疵点检测准确率高达95.38%。     

基于分形理论中Mandelbrot集变换的印花图案设计

针对传统纺织印花图案构思慢、设计时间长的缺陷,基于分形理论系统,对Mandelbrot集分形图的生成原理和变换方式进行了讨论研究。利用计算机绘图技术,尝试提出一种新的纺织印花设计方法。该方法运用分形理论中的Mandelbrot集变换构造出大量多姿多彩的图案,生成得到了丰富、精美的Julia集图形。在此基础上,借助数码印花实验,对其中的一些图形资源在丝绸印花图案设计开发中的应用进行了初步探讨。结果表明,研究成果非常适用于纺织印花图案的设计,能够快速实现分形图案向数码印花产品的应用转移。     

织物疵点自动检测方法及应用进展

织物疵点是影响织物价格的重要因素,一直以来都备受关注.随着科学技术的发展,对智能化需求的提高,织物疵点的自动检测成为纺织行业的热门话题.织物疵点自动检测系统在市场上已经有了较为成熟的产品,国内的主流方式仍为机下检测,对平整布面检测效果较好.根据图像处理的方法可将检测算法分为结构法、统计法、频谱分析法、基于模型的方法、基...     

轧光工序中织物接缝线的图像检测

针对轧光工艺中织物接缝线的自动检测,本文提出了一种基于数字图像处理的判别方法。以防羽布的轧光工艺为例,首先对获取的图像进行灰度化与中值滤波预处理;然后对预处理的图像提取均值、标准差与变异系数(CV值)等特征参数;最后通过对这三项特征参数的分析与对比,发现特征参数CV值可以作为防羽布有无接缝线的判别依据。实验结果表明,特征参数CV值能够精确地判别织物有无接缝线,并且图像处理的计算量小,检测速度快,可实现接缝线的及时避让。