Two-tier self-organizing visual model for road sign recognition
Nguwi, Yok-Yen, and Cho, Siu-Yeung (2008) Two-tier self-organizing visual model for road sign recognition. In: Proceedings of the 2008 International Joint Conference of Neural Networks, pp. 794-799. From: 2008 International Joint Conference of Neural Networks (IJCNN 2008), 1-8 June 2008, Hong Kong.
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This paper attempts to model human brain's cognitive process at the primary visual cortex to comprehend road sign. The cortical maps in visual cortex have been widely focused in recent research. We propose a visual model that locates road sign in an image and identifies the localized road sign. Gabor wavelets are used to encode visual information and extract features. Self-organizing maps are used to cluster and classify the road sign images. We evaluate the system with various test sets. The experimental results show encouraging recognition hit rates. There are quite a number of literatures - introducing different approaches to classify road sign, but none has adopted unsupervised approach. This work makes use of two-tier topological maps to recognize road signs. First–tier map, called detecting map, filters out non-road sign images and regions. Second-tier map, called recognizing map, classifies a road sign into appropriate class.
|Item Type:||Conference Item (Refereed Research Paper - E1)|
|Keywords:||self-organizing map; Gabor feature; visual model; road sign recognition|
|Date Deposited:||20 Sep 2012 01:24|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080104 Computer Vision @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 100%|