ROOM MATERIAL IDENTIFICATION SYSTEM FROM PHOTO IMAGES USING GLCM, MODIFIED ZERNIKE MOMENTS, AND PSO-BP APPLICATION

Authors

  • Fathin Liyana Zainudin Embedded Computing System (EmbCoS) Research Focus Group, Faculty of Electrical & Electronics, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia
  • Abd Kadir Mahamad Embedded Computing System (EmbCoS) Research Focus Group, Faculty of Electrical & Electronics, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia
  • Sharifah Saon Embedded Computing System (EmbCoS) Research Focus Group, Faculty of Electrical & Electronics, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia
  • Musli Nizam Yahya Faculty of Mechanical & Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v78.7414

Keywords:

mage processing, GLCM, Zernike moments, neural network, PSO-BP

Abstract

In acoustic engineering, the types of material used in a room are basically one of the fundamental features that are essential in some of room acoustic parameters computation. This paper proposed an improved system to identify room material type from its surface photographic image. Data images of several room surfaces were collected for the system input. This improved system implements Gray Level Co-occurrence Matrix (GLCM) and modified Zernike moments for image extraction and hybrid Particle Swarm Optimization and back-propagation (PSO-BP) algorithm for classification. For comparison purpose, experiments using variations combination of GLCM and modified Zernike moments extraction as well as Levenberg-Marquardt, back-propagation neural network (BPNN), and PSO-BP algorithm were executed. By applying the proposed methods, the system accuracy increased around 30% compared to previous research. Moreover, the convergence attained during training was three times faster compared to BP algorithm. Thus using the new methods in identifying material surface images had positively improved the system in becoming more efficient and reliable.

Author Biographies

  • Fathin Liyana Zainudin, Embedded Computing System (EmbCoS) Research Focus Group, Faculty of Electrical & Electronics, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia

    Department of Computer Engineering,

    Faculty of Electrical and Electronics Engineering
  • Abd Kadir Mahamad, Embedded Computing System (EmbCoS) Research Focus Group, Faculty of Electrical & Electronics, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia

    Associate Professor, Dr.

    Department of Computer Engineering,

    Faculty of Electrical and Electronics Engineering

  • Sharifah Saon, Embedded Computing System (EmbCoS) Research Focus Group, Faculty of Electrical & Electronics, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia

    Lecturer

    Department of Communication Engineering,

    Faculty of Electrical and Electronics Engineering

  • Musli Nizam Yahya, Faculty of Mechanical & Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia

    Senior Lecturer, Eng. Dr.

    Department of Mechanical Engineering,

    Faculty of Mechanical & Manufacturing Engineering

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Published

2016-09-29

Issue

Section

Science and Engineering

How to Cite

ROOM MATERIAL IDENTIFICATION SYSTEM FROM PHOTO IMAGES USING GLCM, MODIFIED ZERNIKE MOMENTS, AND PSO-BP APPLICATION. (2016). Jurnal Teknologi, 78(10). https://doi.org/10.11113/jt.v78.7414