An intelligent knitted garment defect detection and classification model based on gabor filter and modified Elman neural network

Y. H. Zhang, C. W.M. Yuen, Wai Keung Wong, Chi Wai Kan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

In this paper a new knitted garment defect detection and classification model based on 2D Gabor wavelet transform and Elman neural network is introduced. A new modified Elman network is proposed to classify the type of fabric defects which have proportional (P), integral (I), derivative (D) properties. The proposed inspecting model in this study is more feasible and applicable in fabric defect detection and classification. Compared with the traditional back propagation BP network, the successful classification rate obtained by the PID Elman network is higher than the BP neural network with the same number of classification parameter, and training time and classification time used by PID Elman is less than BP neural network.
Original languageEnglish
Title of host publicationIASP 10 - 2010 International Conference on Image Analysis and Signal Processing
Pages69-74
Number of pages6
DOIs
Publication statusPublished - 12 Jul 2010
Event2nd International Conference on Image Analysis and Signal Processing, IASP'2010 - Xiamen, China
Duration: 12 Apr 201014 Apr 2010

Conference

Conference2nd International Conference on Image Analysis and Signal Processing, IASP'2010
CountryChina
CityXiamen
Period12/04/1014/04/10

Keywords

  • Classification
  • Elman neural network
  • Fabric defect detection
  • Gabor filter
  • PID

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Signal Processing

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