Performance comparison between ANN and PCA 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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Trans Tech Publications Ltd., Switzerland
2013
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Online Access: | http://irep.iium.edu.my/35990/ http://irep.iium.edu.my/35990/ http://irep.iium.edu.my/35990/1/2._Performance_Comparison_between_PCA_and_ANN_Techniques_for_Road_Signs_Recognition.pdf |
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iium-359902014-03-10T07:39:40Z http://irep.iium.edu.my/35990/ Performance comparison between ANN and PCA techniques for road signs recognition Mohd Ali, Nursabillilah Mohd Mustafah, Yasir Alang Md Rashid, Nahrul Khair T Technology (General) 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 Ltd., Switzerland 2013-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/35990/1/2._Performance_Comparison_between_PCA_and_ANN_Techniques_for_Road_Signs_Recognition.pdf Mohd Ali, Nursabillilah and Mohd Mustafah, Yasir and Alang Md Rashid, Nahrul Khair (2013) Performance comparison between ANN and PCA techniques for road signs recognition. Applied Mechanics and Materials, 393. pp. 611-616. ISSN 1660-9336 http://www.scientific.net/AMM.393.611 |
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T Technology (General) Mohd Ali, Nursabillilah Mohd Mustafah, Yasir Alang Md Rashid, Nahrul Khair Performance comparison between ANN and PCA techniques for road signs recognition |
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 |
author |
Mohd Ali, Nursabillilah Mohd Mustafah, Yasir Alang Md Rashid, Nahrul Khair |
author_facet |
Mohd Ali, Nursabillilah Mohd Mustafah, Yasir Alang Md Rashid, Nahrul Khair |
author_sort |
Mohd Ali, Nursabillilah |
title |
Performance comparison between ANN and PCA techniques for road signs recognition |
title_short |
Performance comparison between ANN and PCA techniques for road signs recognition |
title_full |
Performance comparison between ANN and PCA techniques for road signs recognition |
title_fullStr |
Performance comparison between ANN and PCA techniques for road signs recognition |
title_full_unstemmed |
Performance comparison between ANN and PCA techniques for road signs recognition |
title_sort |
performance comparison between ann and pca techniques for road signs recognition |
publisher |
Trans Tech Publications Ltd., Switzerland |
publishDate |
2013 |
url |
http://irep.iium.edu.my/35990/ http://irep.iium.edu.my/35990/ http://irep.iium.edu.my/35990/1/2._Performance_Comparison_between_PCA_and_ANN_Techniques_for_Road_Signs_Recognition.pdf |
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2023-09-18T20:51:31Z |
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2023-09-18T20:51:31Z |
_version_ |
1777410026140336128 |