Details

Autor(en) / Beteiligte

• Shu, Yufeng
• Zhang, Liangchao
• Zuo, Dali
• Zhang, Junhua
• Li, Junlong
• Gan, Haoquan

Titel

Analysis of texture enhancement methods for the detection of eco-friendly textile fabric defects

Ist Teil von

• Journal of intelligent & fuzzy systems, 2021-01, Vol.41 (3), p.4439

Ort / Verlag

Amsterdam: IOS Press BV

Erscheinungsjahr

2021

Link zum Volltext

Quelle

EBSCOhost Business Source Ultimate

Beschreibungen/Notizen

• If the appearance of an eco-friendly textile fabric is problematic, the product quality will be substantially deteriorated. Defect measurement is one of the most important quality control measures for eco-friendly textile fabrics. Compared to previously employed manual measurements, the application of image processing technology for the detection of eco-friendly textile defects is characterized by high efficiency and high precision. In this study, the main objectives of textile reinforcement based on texture enhancement are as follows: (1) Summarize the description methods of texture maps in a certain space and a certain frequency and investigate the gray-scale co-occurrence matrix of textile fabrics, which aimed at the characteristics of a unique texture of textile fabrics, the texture of the background caused by noise, and the texture of the defect area. The error between them was analyzed; (2) Apply a scheme based on principal component analysis-non local means to improve the eco-friendly textile quality. The image information used in the calculation process of the neighborhood similarity in nonlocal average filtering algorithm (NLM) includes the problem of an excess amount due to noise, and the NLM method is employed to estimate the parameters. On the other hand, to remove the noise, it is also possible to display the texture image content of the textile fabric, which is more conducive to the defect detection; and (3) Apply a texture-based textile defect measurement method, that is, a class-separable characteristic between non-defective and defect textures, which increases the measurement of the gray matrix characteristics that distinguish the defect regions and improves the correctness of the detected texture.