Performance Comparison between PCA and ANN Techniques for Road Signs Recognition
This study reports about a comparison in recognizing road signs between Neural Network and Principal Component Analysis (PCA). The road sign with circular, triangular, octagonal and diamond shapes have been used in this study. Two recognition systems to determine the classes of the road signs class...
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| Format: | Article |
| Language: | English |
| Published: |
Trans Tech Publications, Switzerland
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/9021/1/1569722521_ICAME_PAPER_2.pdf http://eprints.utem.edu.my/id/eprint/9021/ |
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| Summary: | This study reports about a comparison in recognizing road signs between Neural Network and Principal Component Analysis (PCA). The road sign with circular, triangular, octagonal and diamond shapes have been used in this study. Two recognition systems to determine the classes of the road signs class were implemented which are based on Feed Forward Neural Network and Principal Component Analysis (PCA). The performance of the trained classifier using Scaled Conjugate Gradient (SCG) back propagation function in Neural Network and PCA technique were evaluated on our test datasets. The experiments show that the system using PCA has a higher accuracy as compared to Neural Network with a minimum of 94% classification rate of road signs. |
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