Mustafa Kamal, N. D. and Jalil, N. and Hashim, H.
(2016)
The analysis of shape-based, DWT and zernike moments feature extraction techniques for fasterner recognition using 10-fold cross validation multilayer perceptrons / N. D. Mustaffa Kamal, N. Jalil and H. Hashim.
Journal of Electrical and Electronic Systems Research (JEESR), 9: 8.
pp. 43-51.
ISSN 1985-5389
Official URL: https://jeesr.uitm.edu.my/v1/
Abstract
—This paper presents an analysis of three feature
extraction techniques which are the shape-based, Zernike
moments and Discrete Wavelet Transform for fastener
recognition. RGB colour features are also added to these major
feature extractors to enhance the classification result. The
classifier used in this experiment is back propagation neural
network and the result in general is strengthen using ten-fold
cross validation. The result is measured using percentage
accuracy and Kappa statistics. The overall results showed that
the best feature extraction techniques are Zernike moment
group 3 and DWT both with added colour features.
Metadata
| Item Type: | Article |
|---|---|
| Creators: | Creators Email / ID Num. Mustafa Kamal, N. D. diyanahmustaffa@yahoo.com Jalil, N. UNSPECIFIED Hashim, H. UNSPECIFIED |
| Subjects: | T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Electronics > Pattern recognition systems |
| Divisions: | Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering |
| Journal or Publication Title: | Journal of Electrical and Electronic Systems Research (JEESR) |
| UiTM Journal Collections: | UiTM Journal > Journal of Electrical and Electronic Systems Research (JEESR) |
| ISSN: | 1985-5389 |
| Volume: | 9 |
| Page Range: | pp. 43-51 |
| Keywords: | Back propagation neural network, discrete wavelet transform, fastener recognition, RGB colour features, shape-based features, ten-fold cross validation, Zernike moments |
| Date: | December 2016 |
| URI: | https://ir.uitm.edu.my/id/eprint/63005 |
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