UCSY's Research Repository

Road Sign Recognition System using Radial Basis Function Neural Network Architecture

Show simple item record

dc.contributor.author Moe, Thae Ei
dc.contributor.author Aye, Zin May
dc.date.accessioned 2019-07-24T12:57:40Z
dc.date.available 2019-07-24T12:57:40Z
dc.date.issued 2010-12-16
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1234
dc.description.abstract 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. en_US
dc.language.iso en en_US
dc.publisher Fifth Local Conference on Parallel and Soft Computing en_US
dc.subject Road Signs Recognition en_US
dc.subject Radial Basis Function (RBF) en_US
dc.subject supervised learning en_US
dc.subject Image processing en_US
dc.title Road Sign Recognition System using Radial Basis Function Neural Network Architecture en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search Repository



Browse

My Account