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Feature extraction and classification of woven fabric using optimized Haralick parameters: A rough set based approach

机译:使用优化的Haralick参数对机织物进行特征提取和分类:一种基于粗糙集的方法

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Classification of fabric samples into classes is highly required for automatic fabric inspection systems, as many of the fabric defects are defined relative to the fabric classes. The texture of the fabric surface is the best way to represent a fabric class, corresponding to which the statistical measures are the Haralick parameters. As all of the Haralick parameters are not responsible for fabric classification and there are no universal Haralick parameters for classifying all types of fabric samples, so it is necessary to determine a subset of Haralick parameters that gives best classification result for the fabric classes under consideration. This subset of Haralick parameters is termed as optimized Haralick parameters of the fabric classes under consideration, which has been determined by using the rough set theory. The developed system has been tested on TILDA database and its superiority with respect to the non-optimized Haralick parameters is established in terms of classification result and separability index.
机译:自动织物检查系统非常需要将织物样本分类,因为许多织物缺陷是相对于织物类别定义的。织物表面的纹理是表示织物类别的最佳方法,与之对应的统计度量是Haralick参数。由于所有Haralick参数都不负责织物分类,并且没有通用的Haralick参数可以对所有类型的织物样本进行分类,因此有必要确定Haralick参数的子集,该子集可以为所考虑的织物类别提供最佳的分类结果。 Haralick参数的此子集称为考虑中的织物类别的优化Haralick参数,这是通过使用粗糙集理论确定的。所开发的系统已经在TILDA数据库上进行了测试,并且在分类结果和可分离性指标方面确立了其相对于非优化Haralick参数的优越性。

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