The Analysis of Shape-based, DWT and Zernike Moments Feature Extraction Techniques for Fasterner Recognition Using 10-Fold Cross Validation Multilayer Perceptrons

  • Nur Diyanah Mustaffa Kamal Universiti Teknologi MARA
  • Nor’aini Jalil
  • Hadzli Hashim

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.

Author Biography

Nur Diyanah Mustaffa Kamal, Universiti Teknologi MARA
She is a master degree student focusing on image processing and object recognition. She studied Mechatronic in Universiti Malaysia Perlis during her undergraduate years.
Published
2019-06-24
How to Cite
MUSTAFFA KAMAL, Nur Diyanah; JALIL, Nor’aini; HASHIM, Hadzli. The Analysis of Shape-based, DWT and Zernike Moments Feature Extraction Techniques for Fasterner Recognition Using 10-Fold Cross Validation Multilayer Perceptrons. International Journal of Electrical & Electronic Systems Research (IEESR), [S.l.], v. 9, n. 1, p. 41-49, june 2019. ISSN 1985-5389. Available at: <http://myjms.mohe.gov.my/index.php/IEESR/article/view/1425>. Date accessed: 18 aug. 2019. doi: https://doi.org/10.24191/ieesr.v9i1.1425.