Fabric defect classification with geometric features using Bayesian classifier

2015 ◽  
Author(s):  
Md. Mozaharul Mottalib ◽  
M. Rokonuzzaman ◽  
Md. Tarek Habib ◽  
Farruk Ahmed
Keyword(s):  
Bayesian Classifier ◽  
Defect Classification ◽  
Geometric Features ◽  
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

scholarly journals Texture classification of fabric defects using machine learning

2020 ◽  
Vol 10 (4) ◽  
pp. 4390
Author(s):  
Yassine Ben Salem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Texture Classification ◽  
Support Vector ◽  
Defect Classification ◽  
Analysis Method ◽  
Fabric Defect ◽  
Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

Yarn-dyed fabric defect classification based on convolutional neural network

2017 ◽  
Vol 56 (09) ◽  
pp. 1 ◽  
Author(s):  
Junfeng Jing ◽  
Amei Dong ◽  
Pengfei Li ◽  
Kaibing Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Classification ◽  
Fabric Defect ◽  
Dyed Fabric

scholarly journals Generic Wrapper Based Model using Haralick Features for Silk Fabric Defect Classification

2021 ◽  
Vol 23 (11) ◽  
pp. 867-878
Author(s):  
Ms. Shweta Loonkar ◽  
◽  
Dhirendra S. Mishra ◽  
Surya S. Durbha ◽  
◽  
...  
Keyword(s):  
Defect Detection ◽  
Control Unit ◽  
Gabor Transform ◽  
Defect Classification ◽  
Silk Fabric ◽  
Fabric Defect ◽  
Segmented Images ◽  
Wrapper Method ◽  
Haralick Features

Quality control unit of fabric industry looks for the effective defect detection methodology. The research is required to be done in this area to develop such solution. Various models based on combination of suitable feature extraction, selection and classification approaches need to be experimented out for the same. This paper attempts to experiment and provide such models mainly based on generic wrapper based selection approaches. Widely used broader range of Haralick features are prominently used for detection and classification of defects in this research. It also attempts to identify the suitability of these features based on segmented images provided as an input. The research has been carried on TILDA Dataset consisting of 800 Silk Fabric Images with eight different defects present on it and each carrying 100 images per defect. Models generated using generic wrapper based approach has also been compared with the Gabor Transforms. Then identification of suitable Haralick Features for particular type of defects has been carried out. In this 68% classification accuracy has been achieved using generic wrapper method and 40 % accuracy has been achieved using Gabor Transform with respect to fourteen Haralick Features and seven types of defects.

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