Fabric wrinkle characterization and classification using modified wavelet coefficients and support-vector-machine classifiers
Fabric wrinkle characterization and classification using modified wavelet coefficients and support-vector-machine classifiers
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DOI：
10.1177/0040517510391702
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发表时间：
2011-01
影响因子：
2.3
通讯作者：
Jingjing Sun;Ming Yao;Bugao xu;P. Bel
中科院分区：
文献类型：
--
作者：
Jingjing Sun;Ming Yao;Bugao xu;P. Bel
本文提出了一种新的褶皱评价方法，该方法利用改进的小波系数和优化的支持向量机（SVM）分类来表征和分类织物的褶皱外观。采用小波变换对织物图像进行分解，并根据小波系数的变化定义了5个参数来描述织物的取向、硬度、密度和对比度等纹理特征。这些参数也被用作优化的SVM分类器的输入，以根据美国纺织化学家和着色师协会的标准光滑外观（SA）复制品获得整体皱纹分级。SVM分类器，基于线性核和径向基函数核，在研究中使用。该评价方法的有效性进行了测试，通过300个图像的五个选定的织物，具有不同的纤维含量，组织结构，颜色，和洗涤周期。SA分类的交叉验证测试表明，超过75%的这些多样化的样本的SA等级可以正确识别。所提取的折皱参数为纺织品、家用电器和洗涤剂生产商检测织物的折皱性能提供了有用的信息。
In this paper we present a novel wrinkle evaluation method that uses modified wavelet coefficients and optimized support-vector-machine (SVM) classifications to characterize and classify the wrinkling appearance of fabric. Fabric images were decomposed with the wavelet transform, and five parameters were defined, based on the modified wavelet coefficients, to describe wrinkling features, such as orientation, hardness, density, and contrast. These parameters were also used as the inputs of optimized SVM classifiers to obtain overall wrinkle grading in accordance with the standard American Association of Textile Chemists and Colorists smoothness appearance (SA) replicas. The SVM classifiers, based on a linear kernel and a radial-basis-function kernel, were used in the study. The effectiveness of this evaluation method was tested by 300 images of five selected fabrics that had different fiber contents, weave structures, colors, and laundering cycles. The cross-validation tests on the SA classifications indicated that the SA grades of more than 75% of these diversified samples could be recognized correctly. The extracted wrinkle parameters provided useful information for textile, appliance, and detergent manufactures to inspect wrinkling behaviors of fabrics.