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Back Propagation Neural Network for Defect Detection of Woven Fabrics
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 86 - Number 1 |
| Year of Publication: 2014 |
| Authors: Gnanaprakash. V, Sathishkumar. N, Finney Daniel Shadrach S |
10.5120/14948-3061
|
Gnanaprakash. V, Sathishkumar. N, Finney Daniel Shadrach S . Back Propagation Neural Network for Defect Detection of Woven Fabrics. International Journal of Computer Applications. 86, 1 ( January 2014), 11-14. DOI=10.5120/14948-3061
Abstract
Fabric defect detection plays a very important role for the automatic detection in fabrics. In this paper, fabric texture feature is extracted using Grey Level Co-occurrence Matrix (GLCM). The Co-occurrence matrices functions characterize the texture of an image by calculating how often pairs of pixel with specific values and in a specified spatial relationship occur in an image, and then extracting statistical measures from this matrix. The extracted features from GLCM are used to classify the texture by Back Propagation Neural Network to compare their effectiveness.
References
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