Textile Texture Modeling Using Random Field Models

Described in the paper is the method to fit autoregressive(AR)random field models and Gausslan Markov ran-dom field(GMRF)models for textile textures.Maxi-mum likelihood(ML)estimation schemes and texturesynthesis for these models are presented.Three types oftypical textile textures are tested using the method andsatisfactory results are achieved.     

纺织品表面纹理的图像分析方法

回顾了用于纺织品表面纹理的图像分析方法,其中着重介绍了纺织品表面纹理特征的描述方法、分析方法和模式分类方法,并以一些常用纺织品为对象,对这些方法的有效性作了比较。     

基于二维连续小波变换的织物疵点检测

同正常织物纹理比较,疵点区域由于其纹理不规则及变形而导致不同的局部纹理特征。利用二维连续小波特征,能在时域和频域上对织物图像同时实现任意尺度和旋转角度的变换。通过纹理模型和频谱分析,确定出最优的变换尺度和旋转角度,并由预先确定的全局阈值从小波变换系数的模中进行疵点的分割。实际疵点的检测结果表明该方法是可行的。     

三维整体织物结构的复合材料设计及弯曲性能探讨

复合材料的性能与其增强结构有关.探讨了正交结构的机织三维整体织物的设计和成型工艺.对两种正交组织的机织多层整体结构的复合材料弯曲性能试验结果表明,纤维含量高,纱线屈曲程度小,材料对疲劳载荷越不敏感.     

Fabric Defect Detection Using GMRF Model

It has been testified that the Gauss Markov random field model is most suitable for the characterization of fabric texture among a variety of available models because of its approximately constant character and the normality of the gray-level distribution found with typical fabric images. However, the general Gauss-Markov random field(GMRF) method for fabric defect detection is not always ideal in practice since in some cases, the estimated model parameters make the Markov error covariance not positively definite, which may render the method to fail thoroughly. In this paper, the use of the GMRF model for defect detection of fabric is discussed and an approach to this problem is proposed. Some detailed texture may be overlooked in this way, but good detection results can still be expected as far as fabric defect detection is concerned.     

基于Wold纹理模型和分形理论的织物疵点检测

应用基于Wold纹理模型,从织物纹理中提取非确定性随机结构.通过正常纹理和疵点纹理中随机结构的表面维分形特征曲线比较,以距离为判别标准定位疵点.几种典型织物疵点的分析表明,基于Wold纹理模型和分形理论的分析方法能准确地检测出织物表面的多种疵点.     

基于高斯-马尔可夫随机场模型的织物纹理特征参数提取方法

将高斯-马尔可夫随机场用于织物纹理的建模.在估计模型参数时,采用不完全 Cholesky共轭梯度法.获得特征参数后,通过纹理合成方法,仿真原始纹理图像.试验表明:仿真织物纹理图像与真实织物纹理图像具有较为一致的视觉效果,从而论证了高斯-马尔可夫随机场模型对织物纹理建模的有效性.