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| Identification of Degraded Traffic Sign Symbols Using PNN |
| Li Lun-bo; Ma Guang-fu |
| Dept. of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China |
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Abstract A novel feature extraction algorithm is presented for the recognition of traffic sign symbols undergoing degradations in this paper. In order to cope with the degradations, the Combined Blur-Affine Invariants (CBAIs) are adopted to extract the features of traffic sign symbols without any restorations which usually need a great amount of computations. A new magnitude normalization method is proposed for the great differences of magnitude of combined blur-affine invariants. Under the deep discussion of PNN and K-means algorithm, a probabilistic neural network classifier is designed using global K-means algorithm and applied to the classification of degraded traffic signs. The simulation results indicate that CBAIs are efficient for the feature extraction of degraded images, and the designed network is not only parsimonious but also has better generalization performance.
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Received: 16 October 2007
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2008, Vol. 30 
