JOURNAL OF TEXTILE RESEARCH ›› 2011, Vol. 32 ›› Issue (8): 142-146.

Previous Articles     Next Articles

Establishment of Enhanced Matrix Feature Model for Fabric Image

  

  • Received:2010-08-02 Revised:2011-04-13 Online:2011-08-15 Published:2011-08-15

Abstract: Aiming at the present situation of lacking of mature model for fabric feature and not better recognition effect, this paper proposes a new feature model of fabric image, called as “enhanced matrix feature model”. Firstly, this model introduces a new enhanced matrix based on image gray. Secondly, this matrix is made of matrix operators generated by gradient variation of fabric image, which can do transform calculation for pixel value to amply or reduce local characteristic of image to make image feature obvious. Finally, we compile some program to verify this model using MATLAB7.0, and we find this model is suitable for defect feature recognition of fabric and recognition effect is better. With this model, feature change in defect regions is enhanced obviously. Therefore, a new theoretical basis of defect recognition of fabric is provided in this paper.

CLC Number: 

  • TS 103.7
[1] . Design of automatic fundamental flat knit knitting control system of flat knitting machine [J]. JOURNAL OF TEXTILE RESEARCH, 2013, 34(3): 127-131.
[2] LIU Zhe. Comprehensive evaluation of appearance quality of fabric based on image analysis [J]. JOURNAL OF TEXTILE RESEARCH, 2012, 33(11): 61-65.
[3] WANG Xiu-Chen, LI Xiao-Jiu. Fuzzy recognition of fabric texture with double conditions [J]. JOURNAL OF TEXTILE RESEARCH, 2012, 33(8): 55-58.
Viewed
Full text
524
HTML PDF
Just accepted Online first Issue Just accepted Online first Issue
0 0 0 0 0 524

  From Others local
  Times 379 145
  Rate 72% 28%

Abstract
781
Just accepted Online first Issue
0 0 781
  From Others local
  Times 693 88
  Rate 89% 11%

Cited

Web of Science  Crossref   ScienceDirect  Search for Citations in Google Scholar >>
 
This page requires you have already subscribed to WoS.
  Shared   
  Discussed   
No Suggested Reading articles found!