Research of Fabric Weave Pattern’s Recognition Using Texture Orientation Features

Jian Qiang Shen; Xuan Zou

doi:10.4028/www.scientific.net/AMR.328-330.1763

Paper Titles

Digital Frequency Multiplying Method for the Power System Frequency Tracking Based on FPGA
p.1747

Study of Coplanor Capacitance Prong Teeth Sensors
p.1751

Multiple Vortex Body Vortex Numerical Simulation
p.1755

Study on Intelligent Control of Milling Process Based on MATLAB
p.1759

Research of Fabric Weave Pattern’s Recognition Using Texture Orientation Features
p.1763

A Novel Precision Ultrasonic Thermometer
p.1768

A Kind of Land Form Detector for Loose and Soft Sandy Soil Milling-Planing and Collecting Device Machinery
p.1772

Design of a Hand Exoskeleton Rehabilitation Device
p.1778

On-Line Test System of Air-Condition in Automobile
p.1784

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 328-330Research of Fabric Weave Pattern’s Recognition...

Research of Fabric Weave Pattern’s Recognition Using Texture Orientation Features

Abstract:

A novel approach is proposed for measuring fabric texture orientations and recognizing weave patterns. Wavelet transform is suited for fabric image decomposition and Radon Transform is fit for line detection in fabric texture. Since different weave patterns have their own regular orientations in original image and sub-band images decomposed by Wavelet transform, these orientations features are extracted and used as SOM and LVQ inputs to achieve automatic recognition of fabric weave. The experimental results show that the neural network of LVQ is more effective than SOM. The contribution of this study is that it not only can identify fundamental fabric weaves but also can classify double layer and some derivative twill weaves such as angular twill and pointed twill.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 328-330)

Pages:

1763-1767

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.328-330.1763

Citation:

Cite this paper

Online since:

September 2011

Authors:

Jian Qiang Shen, Xuan Zou

Keywords:

Fabric Weave, Neural Network (NN), Texture Orientation, Wavelet Transform (WT)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Huang, C., Liu, S., and Yu, W.: Woven Fabric Analysis by Image Processing, Part I: Identification of Weave Patterns, Textile Res. J. , Vol. 70 (2000) No. 6, p.481.

DOI: 10.1177/004051750007000603

Google Scholar

[2] Xu, B.: Identifying Fabric Structures with Fast Fourier Transform Techniques, Textile Res. J., Vol. 66 (1996) No. 8 , p.496.

DOI: 10.1177/004051759606600803

Google Scholar

[3] Tae Jin Kang. : Automatic Recognition of Fabric Weave Patterns by Digital Image Analysis. Textile-Res.J. , Vol. 71 (2001) No. 3, p.261.

DOI: 10.1177/004051759906900201

Google Scholar

[4] Hubbard, B. B. : The World According to Wavelets(A. K. Peters, Ltd., Wellesley, MA, 1996).

Google Scholar

[5] Fan, K., Wang, T., Chang, T., Jou, C., and Kao, T.: Fabric Classification Based on Recognition Using a Neural Network and Dimensionality Reduction, Textile Res. J. , Vol. 68 (1998) No. 3, p.179.

DOI: 10.1177/004051759806800305

Google Scholar

[6] Yassine Ben Salem , Salem Nasri: Automatic recognition of woven fabrics based on texture(Springer-Verlag London Limited 2009).

DOI: 10.1007/s11760-009-0132-5

Google Scholar

[7] Chung-Feng, J.K., Cheng-chih, T.: Automatic recognition of fabric nature by using the approach of texture analysis. Textile Res.J. , Vol. 76 (2006) No. 5, p.375.

Google Scholar

[8] Percival, D. B., and Walden, A. T. : Wavelet Methods for Time Series Analysis(Cambridge University Press, Cambridge, U.K., 2000).

Google Scholar