Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Automated defect detection system using wavelet packet frame and Gaussian mixture model

Not Accessible

Your library or personal account may give you access

Abstract

This paper proposes an approach for automated defect detection in homogeneous textiles using texture analysis. The texture features are extracted by the wavelet packet frame decomposition followed by the Karhunen–Loève transform. The texture feature vector for each pixel is used as an input to a Gaussian mixture model that determines whether or not each pixel is defective. The parameters of the Gaussian mixture model are estimated with nondefective textile images in supervised defect detection. An approach for unsupervised defect detection is also presented that can identify the heterogeneous subblocks on the basis of the Kullback–Leibler divergence between two Gaussian mixtures. The proposed method was evaluated on 25 different homogeneous textile image pairs, one of each pair with a defect and the other with no defect, and was compared with existing methods using texture analysis. The experimental results yielded visually good segmentation and an excellent detection rate with a low false alarm rate for both supervised and unsupervised defect detection. This confirms the validity of the proposed approach for automated defect detection and localization.

© 2006 Optical Society of America

Full Article  |  PDF Article
More Like This
Unsupervised novelty detection using Gabor filters for defect segmentation in textures

Miquel Ralló, María S. Millán, and Jaume Escofet
J. Opt. Soc. Am. A 26(9) 1967-1976 (2009)

Unsupervised defect detection in textiles based on Fourier analysis and wavelet shrinkage

Guang-Hua Hu, Qing-Hui Wang, and Guo-Hui Zhang
Appl. Opt. 54(10) 2963-2980 (2015)

Real-time defect detection of steel wire rods using wavelet filters optimized by univariate dynamic encoding algorithm for searches

Jong Pil Yun, Yong-Ju Jeon, Doo-chul Choi, and Sang Woo Kim
J. Opt. Soc. Am. A 29(5) 797-807 (2012)

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (12)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (1)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (21)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All Rights Reserved