Fabric defect detection and classification using gabor filters and gaussian mixture model

Authors:
Yu Zhang;Zhaoyang Lu;Jing Li
Affiliations:
State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China;State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China;State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an, China
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Year:
2009

Citing 6
Cited 2

Learning Texture Discrimination Masks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Support Vector Machines for Texture Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Palmprint Identification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification and segmentation using wavelet packet frame and Gaussian mixture model

Pattern Recognition
Defect detection in textured materials using optimized filters

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Review article: Automated fabric defect detection-A review

Image and Vision Computing
Automated defect detection in uniform and structured fabrics using Gabor filters and PCA

Journal of Visual Communication and Image Representation

Quantified Score

Hi-index	0.00

Visualization

Abstract

This work investigates the problem of automatic and robust fabric defect detection and classification which are more essential and important in assuring the fabric quality. Two characteristics of this work are: first, a new scheme combining Gabor filters and Gaussian mixture model (GMM) is proposed for fabric defect detection and classification. In detection, the foreground mask and texture features are extracted using Gabor filters. In classification, a GMM based classifier is trained and assigns each foreground pixel to known classes. The second characteristic of this work is the test data is actually collected from Qinfeng textile factory, China, including nine different fabric defects with more than 1000 samples. All the evaluation of our method is based on these actual fabric images and the experimental results show the proposed algorithm achieved satisfied performance.