Evaluation of defect detection in textile images using Gabor wavelet based independent component analysis and vector quantized principal component analysis

Abstract

Textile defect detection plays an important role in the manufacturing industry to maintain the quality of the end product. Wavelet transform is more suitable for quality inspection due to its multi-resolution representation. The Gabor Wavelet Network provides an effective way to analyze the input images and to extract the texture features. The paper addresses the functionality of Gabor wavelet network with independent component analysis and vector quantized principal component analysis. The two methods are used to extract the features from the template image. Then the difference between the template image and the input image features are compared, and threshold value is calculated using Otsu method to obtain the binary image. The performances of the methods are evaluated to verify the efficiency in identifying the defect in the pattern fabric image. © 2013 Springer.