Identification of material surfaces using grey level co-occurrence matrix and elman neural network
Material type absorption coefficient is one of the important parameter that used for acoustic room calculation. Currently, absorption coefficient is obtained by using impedance tube or resonance tube. Both techniques need long learning good skills, high cost equipment, and time consuming to conduct....
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| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6539/ http://eprints.uthm.edu.my/6539/1/206.pdf |
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| Summary: | Material type absorption coefficient is one of the important parameter that used for
acoustic room calculation. Currently, absorption coefficient is obtained by using impedance tube or
resonance tube. Both techniques need long learning good skills, high cost equipment, and time
consuming to conduct. This paper proposed a system distinguished absorption coefficient thru the
material surface identification from digital images. The system was built by applying Grey Level
Co-occurrence Matrices (GLCM) and Elman Neural Network (ENN). Result for the best mean
squared error (MSE) was 4.62e-9 for training phase and 0.5084 for testing phase. Overall, the
system is able to identify the material surfaces and thus directly obtain the absorption coefficient of
the material without using any physical equipment as oppose to the current techniques |
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