Image Processing Based Method Evaluating Fabric Structure Characteristics
Research and development
Authors:
- Shady Ebraheem
Department of Textile Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt - Qashqary Khadijah
Department of Fashion Design, Faculty of Art & Design, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia - Hassan Mounir
Department of Textile Engineering, Faculty of Engineering, Mansoura University, Mansoura, Egypt - Hassan Mounir
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia - Militký Jiři
Faculty of Textile Engineering, Technical University of Liberec, Liberec, Czech Republic
Full text | Abstract: A digital image processing approach was developed to evaluate fabric structure characteristics and to recognise the weave pattern utilising a Wiener filter. Images of six different groups were obtained and used for analysis. The groups included three different fabric structures with two different constructions for each. The approach developed decomposed the fabric image into two images, each of which included either warp or weft yarns. Yarn boundaries were outlined to evaluate the fabric surface characteristics and further used to identify the areas of interlaces to detect the fabric structure. The results showed success in evaluating the surface fabric characteristics and detecting the fabric structure for types of fabrics having the same colors of warp and weft yarns. The approach was also able to obtain a more accurate evaluation for yarn spacing and the rational fabric cover factor compared to the analytical techniques used to estimate these characteristics. |
Tags:
fabric structure characteristics, pattern recognition, image processing, Wiener filter.
Citation:
Shady E, Qashqary K, Hassan M, Militky J. Image Processing Based Method Evaluating Fabric Structure Characteristics. FIBRES & TEXTILES in Eastern Europe 2012; 20, 6A(95):86-90.
Published in issue no 6A (95) / 2012, pages 86–90.