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  1. University of Computer Studies, Yangon
  2. Conferences

Road Sign Recognition System using Radial Basis Function Neural Network Architecture

http://hdl.handle.net/20.500.12678/0000003489
http://hdl.handle.net/20.500.12678/0000003489
675aaece-5362-4a54-aed5-6068c6af6f95
f8d64bde-6eca-47df-be96-05bffe6498ac
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psc2010paper psc2010paper (30).pdf (390 Kb)
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Article
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Publication
Title
Title Road Sign Recognition System using Radial Basis Function Neural Network Architecture
Language en
Publication date 2010-12-16
Authors
Moe, Thae Ei
Aye, Zin May
Description
In this paper, road sign recognition system (RSR) is implemented with radial basis function (RBF) neural network architecture. The system consists of image pre-processing and algorithm of RBF function to recognition that road sign data set.RSR can be performed with dynamic and static road sign images. Road sign consists of various shapes according to their command. In this paper, the mostly use road signs are applied for training and testing data in the implementation of the system.RBF neural network is used in the recognition system because it is a supervised learning neural network and can have better approximation in pattern recognition than other systems. Road signs for warning for danger, forbidden and restriction, and command signs with various geometrical shapes are trained and tested. The result of the system is described by applying the recognition rate described from training and testing phase in RBF neural network.
Keywords
Road Signs Recognition, Radial Basis Function (RBF), supervised learning, Image processing
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1234
Journal articles
Fifth Local Conference on Parallel and Soft Computing
Conference papers
Books/reports/chapters
Thesis/dissertations
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