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
—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 u...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
UiTM Press
2016
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| Subjects: | |
| Online Access: | https://ir.uitm.edu.my/id/eprint/63005/1/63005.pdf https://ir.uitm.edu.my/id/eprint/63005/ https://jeesr.uitm.edu.my/v1/ |
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| Summary: | —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. |
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