Fabric Wrinkle Characterization and Classification Using Modified Wavelet Coefficients and Support-Vector-Machine Classifiers
This paper presents a novel wrinkle evaluation method that uses modified wavelet coefficients and optimized support-vector-machine (SVM) classifications to characterize and classify 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 directionality,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 AATCC smoothness appearance (SA) replicas. The SVM classifiers based on a linear kernel and a radial-basis-function (RBF) 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.
Fabric wrinkling Objective evaluation Wavelet transform SVM classification
Jingjing Sun Ming Yao Patricia Bel Bugao Xu
School of Human Ecology,University of Texas at Austin,Austin,TX 78712,USA USDA Southern Regional Research Center,New Orleans,LA70124,USA
国际会议
杭州
英文
564-577
2010-10-20（万方平台首次上网日期，不代表论文的发表时间）