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: | en |
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Trans Tech Publications, Switzerland
2013
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| 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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| _version_ | 1832716361225207808 |
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| author | Mohd Ali, Nursabillilah |
| author_facet | Mohd Ali, Nursabillilah |
| author_sort | Mohd Ali, Nursabillilah |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.utem.eprints-9021 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2013 |
| publisher | Trans Tech Publications, Switzerland |
| record_format | eprints |
| spelling | my.utem.eprints-90212015-05-28T04:00:50Z http://eprints.utem.edu.my/id/eprint/9021/ Performance Comparison between PCA and ANN Techniques for Road Signs Recognition Mohd Ali, Nursabillilah TK Electrical engineering. Electronics Nuclear engineering 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. Trans Tech Publications, Switzerland 2013-07-30 Article PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/9021/1/1569722521_ICAME_PAPER_2.pdf Mohd Ali, Nursabillilah (2013) Performance Comparison between PCA and ANN Techniques for Road Signs Recognition. Applied Mechanics and Materials. pp. 611-616. ISSN 1660-9366 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Mohd Ali, Nursabillilah Performance Comparison between PCA and ANN Techniques for Road Signs Recognition |
| title | Performance Comparison between PCA and ANN Techniques for Road Signs Recognition |
| title_full | Performance Comparison between PCA and ANN Techniques for Road Signs Recognition |
| title_fullStr | Performance Comparison between PCA and ANN Techniques for Road Signs Recognition |
| title_full_unstemmed | Performance Comparison between PCA and ANN Techniques for Road Signs Recognition |
| title_short | Performance Comparison between PCA and ANN Techniques for Road Signs Recognition |
| title_sort | performance comparison between pca and ann techniques for road signs recognition |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utem.edu.my/id/eprint/9021/1/1569722521_ICAME_PAPER_2.pdf http://eprints.utem.edu.my/id/eprint/9021/ |
| url_provider | http://eprints.utem.edu.my/ |