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Abstract

A new objective fabric pilling evaluation method based on wavelet transform and the local binary pattern (LBP) is developed. The surface pills are identified from the high-frequency noise, fabric texture, and illuminative variation of a pilled fabric image by the two-dimensional discrete wavelet transform. The energies of each detailed sub-image at scales 4–6 in three orientations (horizontal, vertical, and diagonal) and the LBP features of the reconstructed detail image from scales 3 to 6 are calculated as elements of the pilling feature vector to characterize the pilling intensity. These feature values are normalized and the vector dimensions are reduced by principal component analysis. Then the support vector machine, a kind of data mining tool, is used as a classifier to classify the pilling grades. The result suggests that the proposed method can successfully evaluate the pilling intensity of knitted fabrics and could be applicable to practical objective pilling evaluation.

Keywords

Fabric pilling wavelet transform local binary pattern principal component analysis support vector machine

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Objective evaluation of fabric pilling based on wavelet transform and the local binary pattern

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