Paper Title:
Feature Extraction Using Auto-Regression Spectral Analysis for Fabric Defect Detection
  Abstract

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, H. G. Bu, J. Wang, "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
$32.00
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