首页 | 本学科首页   官方微博 | 高级检索  
     

织物表面折皱的小波分析与自组织神经网络等级评定
引用本文:杨晓波,黄秀宝.织物表面折皱的小波分析与自组织神经网络等级评定[J].中国图象图形学报,2005,10(4):473-478.
作者姓名:杨晓波  黄秀宝
作者单位:[1]浙江财经学院信息学院,杭州310012 [2]东华大学纺织学院,上海200051
基金项目:浙江省教育厅科研项目(20041420)
摘    要:为了提取较为精细的图像信息,引入了多尺度2维小波分析织物的表面折皱。织物图像首先经过高斯滤波,再利用小波变换分解并从中提取高频信息,然后结合4种表征织物折皱的特征参数,计算不同折皱等级模板的特征值,通过分析特征值与折皱等级的相关系数,表明这4种特征参数可以作为模式识别的输入量;最后采用Kohonen自组织神经网络客观评定织物的折皱等级,自组织神经网络将不同等级的织物折皱模板进行分类,并以此为依据,确定26种不同织物类型的折皱等级。为了定量描述评定结果,通过计算客观评定与主观评定结果的相关系数,验证该方法的可行性。

关 键 词:自组织神经网络  小波分析  等级评定  织物表面  Kohonen  折皱等级  特征参数  相关系数  客观评定  图像信息  高斯滤波  织物图像  高频信息  小波变换  模式识别  织物类型  主观评定  特征值  多尺度  再利用  输入量  提取  模板  计算
文章编号:1006-8961(2005)04-0473-06

Wavelet Analysis of Fabric Surface Wrinkle and Self-organized Neural Network Grade Assessment
YANG Xiao-bo and YANG Xiao-bo.Wavelet Analysis of Fabric Surface Wrinkle and Self-organized Neural Network Grade Assessment[J].Journal of Image and Graphics,2005,10(4):473-478.
Authors:YANG Xiao-bo and YANG Xiao-bo
Abstract:In this paper, Multi-Scale two-dimensional wavelet transform is imported to analysze fabric surface wrinkle in order to acquire the finer image information. Firstly, fabric image can be filtered through Gaussian filter, and decomposed by wavelet transform; meanwhile, high frequency information is extracted. Secondly, four kinds of wrinkle feature parameter are applied to calculate the fabric wrinkle degree with different wrinkle replica, which are horizontal variance, vertical variance, horizontal offset and vertical offset separately. Through analyzing the correlation coefficient between feature parameter and wrinkle grade, which indicates the four kinds of wrinkle feature parameter can be taken as the input value for pattern recognition. Finally, Kohonen self-organized neural network is also used to evaluate fabric wrinkle grade objectively. The wrinkle feature parameters are input to the Kohonen self-organized neural network, through training and studying process, the output value can be obtained, different wrinkle grade of fabric replica will be classified by applying self-organized neural network, and wrinkle grade of 26 different type fabrics can be evaluated according to this result. For describing the assessment result with quantify, the correlation coefficient is calculated between objective assessment and subjective assessment in order to validate the feasibility of this method.
Keywords:wavelet analysis  feature extraction  wrinkle grade assessment  
本文献已被 CNKI 维普 万方数据 等数据库收录!
点击此处可从《中国图象图形学报》浏览原始摘要信息
点击此处可从《中国图象图形学报》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号