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Room material identification system from photo images using glcm, modified zernike moments, and pso-bp application
Fathin Liyana Zainudin1, Abd Kadir Mahamad2, Sharifah Saon3, Musli Nizam Yahya4.
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 backpropagation
(PSO-BP) algorithm for classification. For comparison purpose, experiments using
variations combination of GLCM and modified Zernike moments extraction as well as LevenbergMarquardt,
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.
Affiliation:
- Universiti Tun Hussein Onn Malaysia, Malaysia
- Universiti Tun Hussein Onn Malaysia, Malaysia
- Universiti Tun Hussein Onn Malaysia, Malaysia
- Universiti Tun Hussein Onn Malaysia, Malaysia
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Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
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6 |
Immediacy Index
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0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
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CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
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