Wavelet based methods on patterned fabric defect detection

The wavelet transform (WT) has been developed over 20 years and successfully applied in defect detection on plain (unpatterned) fabric. This paper is on the use of the wavelet transform to develop an automated visual inspection method for defect detection on patterned fabric. A method called direct thresholding (DT) based on WT detailed subimages has been developed. The golden image subtraction method (GIS) is also introduced. GIS is an efficient and fast method, which can segment out the defective regions on patterned fabric effectively. In this paper, the method of wavelet preprocessed golden image subtraction (WGIS) has been developed for defect detection on patterned fabric or repetitive patterned texture. This paper also presents a comparison of the three methods. It can be concluded that the WGIS method provides the best detection result. The overall detection success rate is 96.7% with 30 defect-free images and 30 defective patterned images for one common kind of patterned Jacquard fabric.     

Unsupervised detection and localization of structural textures using projection profiles

The main goal of existing approaches for structural texture analysis has been the identification of repeating texture primitives and their placement patterns in images containing a single type of texture. We describe a novel unsupervised method for simultaneous detection and localization of multiple structural texture areas along with estimates of their orientations and scales in real images. First, multi-scale isotropic filters are used to enhance the potential texton locations. Then, regularity of the textons is quantified in terms of the periodicity of projection profiles of filter responses within sliding windows at multiple orientations. Next, a regularity index is computed for each pixel as the maximum regularity score together with its orientation and scale. Finally, thresholding of this regularity index produces accurate localization of structural textures in images containing different kinds of textures as well as non-textured areas. Experiments using three different data sets show the effectiveness of the proposed method in complex scenes.     

Periodic pattern of texture analysis and synthesis based on texels distribution

Recently, sample-based texture synthesis techniques have drawn significant attention from researchers. These existing approaches mainly use the Markov Random Field (MRF) or texture features as texture model to analyze the local properties of sample textures. Indeed, human perception is sensitive to structure and periodicity. In this paper, we perform texture synthesis by taking into account the distribution of texels. Given a sample texture, the analysis procedure consists in segmenting texture into individual texels, and detecting each texel in order to analyze their neighborhood relationships by constructing connectivity. Then the synthesis process consists in reproducing a new large texture directly on a user-specified canvas by recomposing segmented texels, which synthesizes two-dimensional texel arrangements based on the previously constructed neighborhood relationships of texels. Results show that the proposed method is successful in generating textures visually indistinguishable to the sample textures. Moreover, the method especially deals with the near-regular textures, which well preserves underlying structural regularity.     

基于统计法的纹理矢量周期描述

本文提出了纹理矢量周期的描述方法:T{θ,Tθ,Vθ},分别从八个方向上(即0°,45°,90°,135°,180°,225°,270°,315°)讨论纹理的周期性、方向性以及周期成分所占的比例等方面。提出了分析纹理周期的d-θ分析方法,比较全面地实现了对纹理周期描述和分析。针对纺织布的纹理特征,经过大量实验,筛选出能反映周期方向的5个特征参量和周期大小的4个特征参量。最后,在本文提出的纹理周期矢量描述方法和分析方法基础上,采用二值共生矩阵及其纹理特征实现对纺织布的纹理周期描述。结果表明,对于含有一定周期的纹理图像,采用本文纹理周期描述和分析方法,实现了比较全面的描述。和直接采用灰度共生矩阵进行分析相比,本文分析方法极大降低了计算量。     

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。     

基于纹理周期性分析的织物疵点检测方法

根据织物图像纹理自身特点,从图像纹理的周期性这个重要的视觉特征入手,提出了基于纹理周期性分析的织物疵点检测方法。通过对大量不同疵点图像检测实验,证明提出方法对织物疵点检测具有较好的有效性和可靠性,而且具有检测的疵点种类多、实用性好的特点。     

基于模板匹配单色布匹瑕疵检测算法

提出一种基于模板匹配的单色布匹瑕疵检测算法,该检测算法首先存储待测布匹的无瑕疵模板图,并对模板图进行分块,然后对待测样本图进行相同的分块操作,进一步利用模板匹配方法对相同分块区域的样本图与模板图进行匹配查找,得到最优匹配图像.在匹配过程中,对模板图按照一定比例进行扩充,以提高匹配的准确性.最后将样本图与最优匹配图像进行差值对比实现布匹瑕疵检测.实验结果表明,算法弥补了传统Gabor算法高度依赖纹理的缺陷,提高了对于纹理模糊的单色布匹瑕疵检测准确率,检测效率与精度满足验布现场需求.     

基于边缘检测的斜纹布匹瑕疵检测

为了对布匹瑕疵进行快速准确的检测,提出了一种基于边缘检测的新算法。利用布匹图像中瑕疵与正常纹理产生的纹理边缘,将布匹瑕疵作为正常纹理的边缘检测出来。利用Sobel算子的方向性,分别对织物疵点在水平和垂直方向进行增强,计算出RGB图像中水平与垂直方向的梯度后进行边缘检测,通过图像融合和二值化完成最终检测。实验证明,该方法准确性高并且检测速率大大提高。     

Automatic defect detection of texture surface with an efficient texture removal network

Automated defect inspection of texture surface is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. We present a simple but powerful image transformation network to remove textures and highlight defects at full resolution. The simple full convolution network consists only of 3 × 3 regular convolution and several dilated convolution blocks, which makes it compact and able to capture multi-scale features effectively. To further improve the ability of the network to suppress texture and highlight defects, a polynomial loss function combining perceptual loss, structural similarity loss and image gradient loss is proposed. In addition, a semi-automatic annotation method mainly composed of wavelet transform and relative total variation is designed to generate a data set of image pairs containing the original texture image and the desired texture removal image. We conducted experiments on a milled metal surface defect dataset and an open data set containing various textured backgrounds to evaluate the performance of our method. Compared with other convolutional neural network approaches, the results demonstrate the superiority of the proposed method. The method has been applied to the surface defect online detection system of an aluminum ingot milling production line, which effectively improves the surface inspection efficiency and product quality.     

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).     

Invariant and adaptive geometrical texture features for defect detection and classification

Automatic texture defect detection is highly important for many fields of visual inspection. We introduce novel, geometrical texture features for this task, which are Euclidean motion invariant and texture adaptive: An algebraic function (rational, Padé, or polynomial) is integrated over intensities in local, circular neighborhoods on the image including an anisotropical texture analysis. Adaptiveness is achieved through the optimization of this feature kernel and further coefficients based on a simplex energy minimization, constrained by a measure of texture discrimination (Fisher criterion). A backpropagation trained, multilayer perceptron network classifies the textures locally. Our approach contains new properties, usually not common in feature theories: Theoretically implicit, multiple invariances and an automatic and specific adaptation of the features to the texture images. Experiments with a fabric data set and Brodatz textures reveal up to 98.6% recognition accuracy.     

一种方向Gabor滤波纹理分割算法

结合人眼视觉特性，设计了一种方向Gabor滤波器，该滤波器顾及了纹理图像的方向特性；利用Gabor滤波器的带通技术，抑制次要纹理图像的主频率分量，增强目标纹理图像主频率分量，使滤波输出图像具有较大的类间离散度和较小的类内离散度，将纹理图像的分割转化为传统的图像分割，使图像的分割质量和算法效率都得到了提高。     

基于下采样的改进的织物图像预处理方法

预处理对织物疵点检测来说是必不可少的,通过对传统的下采样预处理方法进行分析,指出其中的不足之处：图像下采样窗口大小是试凑得到的,针对此不足,根据织物纹理的周期性提出了一种确定下采样窗口的简单方法,加快了图像预处理的速度,并通过实验证明了此方法的优越之处。     

A Simplified pulse-coupled neural network for adaptive segmentation of fabric defects

This paper describes an adaptive image-segmentation method based on a simplified pulse-coupled neural network (PCNN) for detecting fabric defects. Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors (e.g., illumination, noise). A new parameter called the deviation of the contrast (DOC) was introduced to describe the contrast difference in row and column between the analyzed image and a defect-free image of the same fabric. The DOC essentially weakens the influence of the weave texture and the illumination. The simplification of PCNN reduces the number of the network’s parameters by utilizing the local and global DOC information for the parameter selections. The validation tests on the developed algorithms were performed with fabric images captured by a line-scan camera on an inspection machine, and with images from TILDA’s Textile Texture Database () as well.     

基于纹理规则性分析的图像填补方法

针对目前图像填补中存在的很难自动分析和提取前景、背景,然后合理地恢复图像等问题,提出一种利用纹理规则性分析和纹理合成技术进行有效图像填补的方法.为了实现自动化的提取,将前景视为近似规则纹理的场景区域,首先引入纹理规则性分析方法,利用近似规则纹理具有强烈的信号规则性的特点自动有效地提取前景信息;然后通过合成优先级的思想将纹理合成技术和图像修补技术结合起来,提出一个合理的背景填补方法以恢复图像.实验结果证明,该方法可以自动有效地提取具有近似规则纹理特征的前景或背景内容,并合理地恢复图像.     

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.     

基于Gabor滤波器和HOG特征的织物疵点检测

针对织物疵点检测问题,提出了一种基于Gabor滤波器和方向梯度直方图（HOG）特征的织物疵点检测算法。首先使用3个尺度、4个方向的Gabor滤波器组对织物图像进行滤波,并做融合处理,增强织物图像疵点区域和背景纹理之间的对比度;然后使用双边滤波减弱图像背景纹理和噪声的影响;最后将图像划分成均匀子块,提取每个子图像块的HOG特征,利用图像疵点区域和背景纹理的HOG特征差异进行阈值分割实现织物疵点的检测。实验选取5种常见织物疵点进行验证,并与传统的Gabor滤波算法进行了实验对比,结果表明该算法可以较好的抑制织物背景纹理的干扰,更加准确的检测出织物疵点。     

Automatic extraction of texture-periodicity using superposition of distance matching functions and their forward differences

In this paper, we propose a method of automatic detection of texture-periodicity using superposition of distance matching functions (DMFs) followed by computation of their forward differences. The method has been specifically devised for automatically identifying row and column periodicities and thereby the size of periodic units from textile fabrics belonging to any of the 17 wallpaper groups and is a part of automatic fabric defect detection scheme being developed by us that needs periodicities along row and column directions. Overall row-DMF (or overall column-DMF) is obtained based on superposition of DMF of all rows (or columns) from the input image and its second forward difference is computed to get the overall maximum which is a direct measure of periodicity along row (or column) direction. Results from experiments on various near-regular textures demonstrate the capability of the proposed method for automatic periodicity extraction without the need of human intervention.     

基于AR谱分析的织物瑕疵自动检测

为实现快速和有效的织物瑕疵自动检测,提出了一种基于时间序列而不是图像的功率谱纹理分析方法.依据Burg auto regressive(AR)算法估计得到谱数据,从中提取能够反映纹理周期和取向等特点的特征,并首次采用支持向量数据描述模型来检测织物瑕疵纹理.对包含多种疵点的若干织物样本的检测结果表明,依照本文所述方案能够在保持较低的误譬率前提下达到较高的疵点栓出率,证明了所述方案的可行性.     

织物纹理的计算机生成技术

纹理是真实感图象最重要的特征之一，而织物纹理是决定服装风格特征的一个重要因素。由于织物纹理的多样性和复杂性，在计算机图形学和服装ＣＡＤ技术的研究中，如何用计算机生成漂亮、逼真的服装面料以及织物纹理就成为一个非常有趣并富有挑战性的问题。本基于Ａｒｎｏｌｄ变换和生命游戏算法提出了一种在计算机上生成多种复杂织物纹理的新方法。实验表明，利用该方法生成的织物纹理既十分漂亮又比较逼真。