Applications of Fractal in Textile Engineering

2012 ◽  
Vol 627 ◽  
pp. 567-571
Author(s):  
Chang Qu ◽  
Meng Xu ◽  
Jun Ze Wang ◽  
Jie Deng
Keyword(s):  
Texture Analysis ◽  
Defect Detection ◽  
Fractal Theory ◽  
Woven Fabric ◽  
Pattern Design ◽  
Fabric Defect Detection ◽  
Complex Problems ◽  
Fabric Defect ◽  
Fractal Images ◽  
Fabric Surface

In view of the generation of fractal images, the applications of fractal in textile engineering are summarized into two parts. Firstly, fractal images are used in textile image design, textile pattern design and so on. Secondly, fabric properties, such as woven fabric permeability analysis, fabric defect detection, texture analysis of the fabric surface and so on, are analyzed based on fractal theory. The applications of fractal images provide some new creative ideas for textile pattern design. The fractal theory is a powerful tool to solve the complex problems of textile engineering.

Semi-supervised fabric defect detection based on image reconstruction and density estimation

2020 ◽  
pp. 004051752096673
Author(s):  
Qihong Zhou ◽  
Jun Mei ◽  
Qian Zhang ◽  
Shaozong Wang ◽  
Ge Chen
Keyword(s):  
Image Reconstruction ◽  
Density Estimation ◽  
Defect Detection ◽  
Gaussian Mixture ◽  
Inspection System ◽  
Fabric Defect Detection ◽  
Latent Space ◽  
Variational Autoencoder ◽  
Fabric Defect ◽  
Fabric Surface

Defective products are a major contributor toward a decline in profits in textile industries. Hence, there are compelling needs for an automated inspection system to identify and locate defects on the fabric surface. Although much effort has been made by researchers worldwide, there are still challenges with computation and accuracy in the location of defects. In this paper, we propose a hybrid semi-supervised method for fabric defect detection based on variational autoencoder (VAE) and Gaussian mixture model (GMM). The VAE model is trained for feature extraction and image reconstruction while the GMM is used to perform density estimation. By synthesizing the detection results from both image content and latent space, the method can construct defect region boundaries more accurately, which are useful in fabric quality evaluation. The proposed method is validated on AITEX and DAGM 2007 public database. Results demonstrate that the method is qualified for automated detection and outperforms other selected methods in terms of overall performance.

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