Title :
Fabric fault classification using neural trees
Author :
Abdulhady, M. ; Abba, H. ; Nassar, S.
Author_Institution :
Electron. Res. Inst., Cairo, Egypt
Abstract :
In this paper, the problem of textile quality control is addressed. The basic objective is to classify the most important defects in woven fabrics. The algorithm adopted here is composed of three stages. The first stage is a preprocessing phase where defects are detected and localized. Since every detected defect has its different shape and size, all defects are normalized to a predetermined size. In the second stage, a set of features are calculated for each defect using the Haralick et al (1973) spatial features. During the third and last stage, those features are then used to train a competitive neural tree (CNeT) (Behnke and Karayiannis, 1998) designed to learn in a supervised manner the class associated with each set of features. The network can be then used to test and classify new defects. The approach is experimented with a set of images of fault free and defected textiles and output results are analyzed.
Keywords :
feature extraction; image classification; learning (artificial intelligence); neural nets; quality control; textile industry; competitive neural tree; defect detection; experiment; fabric fault classification; neural trees; spatial features; supervised learning; textile industry; textile quality control; woven fabrics; Classification tree analysis; Fabrics; Fault detection; Image analysis; Inspection; Neural networks; Phase detection; Shape; Testing; Textiles;
Conference_Titel :
Systems, Man and Cybernetics, 2002 IEEE International Conference on
Print_ISBN :
0-7803-7437-1
DOI :
10.1109/ICSMC.2002.1175570