Paper Title:
Fabric Defect Detection Based on SRG-PCNN
  Abstract

Defect segmentation has been a focal point in fabric inspection research, and it remains challenging because it detects delicate features of defects complicated by variations in weave textures and changes in environmental factors. According to the different features between the normal fabric image and defect image, this paper presents an adaptive image segmentation method based on a simplified region growing pulse coupled neural network (SRG-PCNN) for detecting fabric defects. The validation tests on the developed algorithms were performed with fabric images, and results showed that SRG-PCNN is a feasible and efficient method for defect detection.

  Info
Periodical
Advanced Materials Research (Volumes 148-149)
Edited by
Xianghua Liu, Zhengyi Jiang and Jingtao Han
Pages
1319-1326
DOI
10.4028/www.scientific.net/AMR.148-149.1319
Citation
X. S. Si, H. Zheng, X. M. Hu, "Fabric Defect Detection Based on SRG-PCNN", Advanced Materials Research, Vols. 148-149, pp. 1319-1326, 2011
Online since
October 2010
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Price
$35.00
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