Automatic Recognition of Woven Fabric Pattern Based on TILT

Zhitao Xiao,Wen Wang,Jun Wu,Yongmin Guo,Fang Zhang,Lei Geng,Yanbei Liu

doi:10.1155/2018/9707104

Zhitao Xiao, Wen Wang + Show 5 more

Open Access

https://doi.org/10.1155/2018/9707104

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Abstract

In this paper, an effective method based on Transform Invariant Low-rank Textures (TILT) and HOG is proposed to identify woven fabric pattern. Firstly, the method based on TILT is used to solve the deflection phenomenon in the process of woven fabric image acquisition. Secondly, the yarn floats in the fabric image is localized by the yarns segmentation method based on the 2D spatial-domain gray projection, which is used to segment the weft and warp yarns. Thirdly, HOG is applied to extract distinctive invariant features in the process of feature extraction. According to the HOG feature, the texture features of the woven fabric are acquired. Finally, the yarn floats are classified by Fuzzy C-Means (FCM) clustering to recognize the weft and warp cross. Experimental results demonstrate that the proposed method can achieve the recognition of the three woven fabrics, plain, twill, and satin, and obtain accurate classification results.

Highlights

In the traditional textile industry, the recognition of the woven fabric is mostly done manually, which is very tedious and time-consuming
In order to correct the captured woven fabric images, we propose a novel algorithm, based on low-rank texture transformation
The skew angles of the original woven fabric images are corrected based on the algorithm of Transform Invariant Low-rank Textures (TILT)

Summary

Introduction

In the traditional textile industry, the recognition of the woven fabric is mostly done manually, which is very tedious and time-consuming. With the rapid development of technology, the application of image processing and machine vision technology is becoming more dominant. Image processing and machine vision technology have been introduced into the area of woven fabric [1]. There are limits to its adaptability for solid fabrics, since these methods recognize patterns by light reflection from the warp and weft, there are limits to their adaptability for solid fabrics. Some relevant researches have been developed for automatic analysis of fabric weave structures. R.Pan [9] analyzed weft and warp floats to determine the fabric weave patterns. Y. Ben Salem [10] developed a supervised recognition method using support vector machine to classify fabric weave patterns and used the SVM to classify woven fabric structure.

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