TITLE:
Identification of Textile Defects Based on GLCM and Neural Networks
AUTHORS:
Gamil Abdel Azim
KEYWORDS:
Image Processing, Neural Network, Gray-Level Co-Occurrence Matrices (GLCM)
JOURNAL NAME:
Journal of Computer and Communications,
Vol.3 No.12,
December
2,
2015
ABSTRACT: In modern textile industry, Tissue online
Automatic Inspection (TAI) is becoming an attractive alternative to Human
Vision Inspection (HVI). HVI needs a high level of attention nevertheless
leading to low performance in terms of tissue inspection. Based on the
co-occurrence matrix and its statistical features, as an approach for defects
textile identification in the digital image, TAI can potentially provide an
objective and reliable evaluation on the fabric production quality. The goal of
most TAI systems is to detect the presence of faults in textiles and accurately
locate the position of the defects. The motivation behind the fabric defects
identification is to enable an on-line quality control of the weaving process.
In this paper, we proposed a method based on texture analysis and neural
networks to identify the textile defects. A feature extractor is designed based
on Gray Level Co-occurrence Matrix (GLCM). A neural network is used as a
classifier to identify the textile defects. The numerical simulation showed
that the error recognition rates were 100% for the training and 100%, 91% for
the best and worst testing respectively.