Woven Fabric Defect Detection Based on the Cascade Classifier

2019 ◽  
Author(s):  
Yi Wang ◽  
Liqing Li ◽  
Xianfu Wan ◽  
Jun Wang
Keyword(s):  
Defect Detection ◽  
Woven Fabric ◽  
Cascade Classifier ◽  
Fabric Defect Detection ◽  
Fabric Defect

Applications of Fractal in Textile Engineering

2012 ◽  
Vol 627 ◽  
pp. 567-571
Author(s):  
Chang Qu ◽  
Meng Xu ◽  
Jun Ze Wang ◽  
Jie Deng
Keyword(s):  
Texture Analysis ◽  
Defect Detection ◽  
Fractal Theory ◽  
Woven Fabric ◽  
Pattern Design ◽  
Fabric Defect Detection ◽  
Complex Problems ◽  
Fabric Defect ◽  
Fractal Images ◽  
Fabric Surface

In view of the generation of fractal images, the applications of fractal in textile engineering are summarized into two parts. Firstly, fractal images are used in textile image design, textile pattern design and so on. Secondly, fabric properties, such as woven fabric permeability analysis, fabric defect detection, texture analysis of the fabric surface and so on, are analyzed based on fractal theory. The applications of fractal images provide some new creative ideas for textile pattern design. The fractal theory is a powerful tool to solve the complex problems of textile engineering.

Machine Vision Window Division on Self-Adaptation

2011 ◽  
Vol 460-461 ◽  
pp. 617-620
Author(s):  
Xiu Chen Wang
Keyword(s):  
Image Analysis ◽  
Machine Vision ◽  
Defect Detection ◽  
Actual Situation ◽  
Adaptive Window ◽  
Fabric Defect Detection ◽  
Fabric Defect ◽  
Self Adaptation ◽  
Recognition Efficiency ◽  
Division Model

Aiming at time-consuming and ineffective problem of image window division in fabric defect detection, this paper proposes a new adaptive division method after a large number of experiments. This method can quickly and exactly recognize defect feature. Firstly, a division model on adaptive window is established, secondly, the formula to anticipate generally situation of fabric image is given according to the peaks and valleys change in the model, and methods to calculate the division size and position of adaptive window are given. Finally, we conclude that the algorithm in this paper can quickly and simply select the size and position of window division according to actual situation of different fabric images, and the time of image analysis is shortened and the recognition efficiency is improved.

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