An adaptive level-selecting wavelet transform for texture defect detection

2007 ◽  
Vol 25 (8) ◽  
pp. 1239-1248 ◽  
Author(s):  
Yanfang Han ◽  
Pengfei Shi
Keyword(s):  
Wavelet Transform ◽  
Defect Detection ◽  
Texture Defect Detection

scholarly journals Defect Detection in Fabric using Wavelet Transform and Genetic Algorithm

2015 ◽  
Vol 3 (6) ◽  
Author(s):  
Depavath Harinath ◽  
K. Ramesh Babu ◽  
P. Satyanarayana ◽  
M.V. Ramana Murthy
Keyword(s):  
Genetic Algorithm ◽  
Wavelet Transform ◽  
Defect Detection

APPLICATION OF WAVELET TRANSFORM TO DEFECT DETECTION OF BUILDING’S STRUCTURE

Mechanika ◽  
2013 ◽  
Vol 18 (6) ◽  
Author(s):  
V. Volkovas ◽  
M. Eidukevičiūte ◽  
H. S. Nogay ◽  
T. C. Akinci
Keyword(s):  
Wavelet Transform ◽  
Defect Detection

scholarly journals Texture defect detection by using polynomial interpolation and multilayer perceptron

2019 ◽  
Vol 14 ◽  
pp. 155892501882527
Author(s):  
Sabeur Abid
Keyword(s):  
Defect Detection ◽  
Manufacturing Industry ◽  
Polynomial Interpolation ◽  
Detection System ◽  
Labor Cost ◽  
Fabric Defect Detection ◽  
Accuracy Comparison ◽  
Comparison Results ◽  
Fabric Defect ◽  
Texture Defect Detection

This article deals with fabric defect detection. The quality control in textile manufacturing industry becomes an important task, and the investment in this field is more than economical when reduction in labor cost and associated benefits are considered. This work is developed in collaboration with “PARTNER TEXTILE” company which expressed its need to install automated defect fabric detection system around its circular knitting machines. In this article, we present a new fabric defect detection method based on a polynomial interpolation of the fabric texture. The different image areas with and without defects are approximated by appropriate interpolating polynomials. Then, the coefficients of these polynomials are used to train a neural network to detect and locate regions of defects. The efficiency of the method is shown through simulations on different kinds of fabric defects provided by the company and the evaluation of the classification accuracy. Comparison results show that the proposed method outperforms several existing ones in terms of rapidity, localization, and precision.

Defect detection in magnetic tile images based on stationary wavelet transform

2016 ◽  
Vol 83 ◽  
pp. 78-87 ◽  
Author(s):  
Chengli Yang ◽  
Peiyong Liu ◽  
Guofu Yin ◽  
Honghai Jiang ◽  
Xueqin Li
Keyword(s):  
Wavelet Transform ◽  
Defect Detection ◽  
Stationary Wavelet Transform

705 Development of defect detection system by image processing using wavelet transform

2010 ◽  
Vol 2010.18 (0) ◽  
pp. 185-186
Author(s):  
Kaoru TAKAMORI ◽  
Masashi ONO ◽  
Kazutaka NONOMURA ◽  
Libo ZHOU ◽  
Hirotaka OJIMA
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Defect Detection ◽  
Detection System

scholarly journals Wavelet transform in the application of three-dimensional terahertz imaging for internal defect detection

2017 ◽  
Vol 66 (8) ◽  
pp. 088701
Author(s):  
Dai Bing ◽  
Wang Peng ◽  
Zhou Yu ◽  
You Cheng-Wu ◽  
Hu Jiang-Sheng ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Defect Detection ◽  
Three Dimensional ◽  
Terahertz Imaging ◽  
Internal Defect ◽  
Internal Defect Detection
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