共查询到18条相似文献,搜索用时 531 毫秒
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采用图像处理的织物缝纫平整度自动评估 总被引:1,自引:0,他引:1
为解决织物缝纫平整度客观自动评估时分类正确率低的问题,提出了一种基于灰度共生矩阵、小波分析和反向传播(BP)神经网络相结合的织物缝纫平整度的自动评估方法。首先采集标准缝纫图像,将图像的灰度级降至16 级,计算图像在0°和90°方向上的灰度共生矩阵并将其归一化,提取灰度共生矩阵的能量、熵、对比度和相关性4 个特征参数,并分别对特征参数在0°和90°方向上取均值;同时,运用Haar 小波在第6个分析尺度上提取并计算图像的水平细节系数的标准差。然后将提取的这5 个特征参数输入到BP 神经网络中训练和识别,并对标准缝纫图像进行了评估。评估结果显示:提出的算法与单独采用灰度共生矩阵特征、小波特征相比,具有较高的分类正确率,分类效果稳定。 相似文献
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本文提出一种基于灰度共生矩阵和小波变换的方法相结合进行纹理相似度判别的评估模型。图像的纹理特性综合反映了斑点构成的分布和特征,因此伪装与背景的纹理特性差异,可以反映出他们之间的综合特性差异。灰度共生矩阵可定量描述纹理特征,而小波变换将图像分解为不同层次,可以模拟在不同观察距离上伪装效果的评估。本文在进行模型分析将伪装图像进行多层小波分解,只在变换后的低频部分,对不同层次上伪装和背景图像的低频图像纹理特征进行比较,能够较好的反映伪装和背景在不同观察距离上的综合纹理相似度。 相似文献
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对于织物缺陷的检测,可以使用多种不同的图像处理技术.而具有多分辨特性的小波变换是一种分析图像的新方法,它的变尺度特性与人类视觉中的空间频率多通道相吻合.使用小波分析的方法对3种织物缺陷进行检测分类.首先将织物图像进行3层小波分解,然后把小波分解后的图像灰度值作为特征参数输入到BP神经网络进行检测识别,实验结果表明,用这种方法识别织物缺陷识别率可达到98%。 相似文献
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针对目前缺乏有效显现织物特征的成熟模型,使织物疵点识别效果不佳的现状,提出一种新的织物图像特征模型,即增强矩阵特征模型.该特征模型以图像的灰度值为基础,引入一种新的增强矩阵.该矩阵由根据织物图像梯度变化生成的矩阵算子组成,可对像素灰度值进行变换计算以放大或缩小图像局部特征,使图像的特征显现更加层次分明.通过采用MatL... 相似文献
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织物纹样特征提取与匹配方法比较 总被引:1,自引:0,他引:1
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。 相似文献
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This paper proposed an approach, which is based on multi-scale wavelet transform and Gaussian mixture model, to solve the problem about automated fabric defect detection and improve the quality of fabric in the production. Firstly, the sample image was tackled by the “Pyramid” wavelet decomposition algorithm, and the new images were obtained by reconstructing with the produced wavelet coefficients using wavelet thresholding denoising method. Secondly, the obtained new images were segmented by applying the Gaussian mixture model that was based on the Expectation–Maximization (EM) algorithm. Various fabric samples were used in the evaluation, and the experimental results showed that the designed algorithm could precisely locate the position of defect and segment the defect. 相似文献
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This paper describes a machine vision system for the detection of weft‐knitted fabric defects based on an adaptive pulse‐coupled neural network (PCNN) and Ridgelet transform. In order to classify defects according to their different texture features, two methods are implemented: an improved PCNN method to segment the defects such as hole and dropped stitch from background image and a Ridgelet transform method based on wavelet analysis to identify the defect such as course mark. In implementing the PCNN model, necessary parameters of PCNN model such as linking coefficient, connection weight, and iteration number are automatically calculated in accordance with the spatial distance of neurons, mean, and variance value of whole image, and the cross‐entropy criterion. The function of Ridgelet transform is to identify the straight line marks and fit the regression equation for simulating the course mark in the image. The Ridgelet transform model can be simplified as the combination of Radon and wavelet transforms. The parameters of detected line are acquired by wavelet analysis in Fourier semicircle region. The experiment materials were several plain and interlocked weft‐knitted fabrics with hole, dropped stitch, and course mark defects. The fabric images were captured by an area‐scan camera with a resolution of 600 × 800 pixels, and signal processing was controlled by a digital signal processing multiprocessor on the inspection machine. The validation tests proved that the system performed well. 相似文献
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《纺织学会志》2013,104(6):423-430
Abstract Previously, the authors proposed a new, simple method of frequency domain analysis based on the two-dimensional discrete wavelet transform to objectively measure the pilling intensity in sample fabric images. The method was further characterized, and the results obtained indicate that standard deviation and variance are the most appropriate measures of the dispersion of wavelet details coefficients for analysis, that the relationship between wavelet analysis scale and fabric inter-yarn pitch was empirically confirmed, and, that fabrics with random patterns do not appear to impact on the effectiveness of the analysis method. 相似文献
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以农田害虫识别系统中图像的预处理为研究对象,利用小波变换对图像进行不同尺度的小波分解,对得到的小波系数进行不同的处理,包括小尺度下的高频系数置零、阈值处理、模极大值处理以及增加大尺度下高频系数的相对值等方法,从而达到去噪、增强等图像预处理的目的.结果表明:利用小波变换对图像进行处理,可以收到良好的效果. 相似文献