Fabric Defect Detection and Classification Using Gabor Filters and Gaussian Mixture Model

2010 ◽  
pp. 635-644 ◽  
Author(s):  
Yu Zhang ◽  
Zhaoyang Lu ◽  
Jing Li
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Defect Detection ◽  
Gaussian Mixture ◽  
Gabor Filters ◽  
Fabric Defect Detection ◽  
Fabric Defect

Fabric defect detection using Gabor filters and defect classification based on LBP and Tamura method

2013 ◽  
Vol 104 (1) ◽  
pp. 18-27 ◽  
Author(s):  
Junfeng Jing ◽  
Huanhuan Zhang ◽  
Jing Wang ◽  
Pengfei Li ◽  
Jianyuan Jia
Keyword(s):  
Defect Detection ◽  
Gabor Filters ◽  
Defect Classification ◽  
Fabric Defect Detection ◽  
Fabric Defect

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.

scholarly journals Fabric defect detection using multi-level tuned-matched Gabor filters

2012 ◽  
Vol 8 (2) ◽  
pp. 325-341 ◽  
Author(s):  
Kai-Ling Mak ◽  
◽  
Pai Peng ◽  
Ka-Fai Cedric Yiu ◽  
Keyword(s):  
Defect Detection ◽  
Gabor Filters ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Multi Level

Texture Defect Detection Using Gabor Filters

2012 ◽  
Vol 229-231 ◽  
pp. 1176-1179
Author(s):  
Peng Fei Li ◽  
Huan Huan Zhang ◽  
Xue Juan Kang ◽  
Jun Feng Jing
Keyword(s):  
Defect Detection ◽  
Texture Features ◽  
Gabor Filters ◽  
Experimental Results ◽  
Detection Scheme ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Texture Defect Detection ◽  
Regular Texture

This paper aims at investigating a method for tackling the problem of automated fabric defect detection. A new scheme for defect detection using Gabor filters was proposed. In the experiment, texture features is extracted using Gabor filters. The proposed method would automatically segment defects from the regular texture. The experimental results showed the effectiveness of the proposed detection scheme.

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