Feature Extraction Using Auto-Regression Spectral Analysis for Fabric Defect Detection

Abstract:

Article Preview

A new feature extraction method for fabric defect detection is proposed, which is based on one-dimensional projection series of fabric images. By using horizontal projection and vertical projection of the image, the characteristics of periodicity and orientation of fabric texture can be fully utilized. In terms of detection defects, it helps acquire information at most, and the computational complexity can also be greatly decreased with one-dimensional projection series. The proposed new method, named Auto-Regressive spectral analysis (AR), is a kind of modern spectral analysis method which is very suitable for analyzing short data with a high spectral resolution. The Burg algorithm is applied to estimate the AR spectrum. Finally, t-test is applied to verify the effectiveness of AR spectral features. This approach has been applied to various cases of defect detections with satisfactory results.

Info:

Periodical:

Advanced Materials Research (Volumes 175-176)

Main Theme:

Edited by:

Lun Bai and Guo-Qiang Chen

Pages:

366-370

DOI:

10.4028/www.scientific.net/AMR.175-176.366

Citation:

J. Zhou et al., "Feature Extraction Using Auto-Regression Spectral Analysis for Fabric Defect Detection", Advanced Materials Research, Vols. 175-176, pp. 366-370, 2011

Online since:

January 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.