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    Intra color-shape classification for traffic sign recognition

    Access Status
    Fulltext not available
    Authors
    Lim, Hann
    Seng, K.
    Ang, L.
    Date
    2010
    Type
    Conference Paper
    
    Metadata
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    Citation
    Lim, H. and Seng, K. and Ang, L. 2010. Intra color-shape classification for traffic sign recognition, pp. 642-647.
    Source Title
    ICS 2010 - International Computer Symposium
    DOI
    10.1109/COMPSYM.2010.5685432
    ISBN
    9781424476404
    School
    Curtin Sarawak
    URI
    http://hdl.handle.net/20.500.11937/43459
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a novel traffic sign recognition system comprising of: (i) Color/shape classification, (ii) Pictogram extraction, (iii) Features selection and, (iv) Lyapunov Theory-based Radial Basis Function neural network (RBFNN). In the proposed system, traffic signs are first segmented and classified with regard to its unique color and shape in order to partition a large set of data into smaller subclasses. Within these subclasses, all redundant information except the pictogram is discarded for feature selection since the pictogram contains critical information for road users. Principle Component Analysis (PCA) is applied to extract salient points for traffic sign dimensionality reduction. This is followed by the Fisher's Linear Discriminant (FLD) to further obtain the most discriminant features. These features are fed into RBFNN for training with a proposed weight updating scheme based on Lyapunov stability theory. The performance of the proposed system is evaluated with Malaysian road signs with promising recognition rate. ©2010 IEEE.

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