Ensemble learning that combines the decisions of multiple weak classifiers to from an output, has recently emerged as an effective identification method. This paper presents a road-sign identification system based upon the ensemble learning approach. The system identifies the regions of interest that are extracted from the scene into the road-sign groups that they belong to. A large road-sign image dataset is formed and used to train and test the system. Fifteen groups of road signs are chosen for identification. Five experiments are performed and the results are presented and discussed.


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    Title :

    Road-Sign Identification Using Ensemble Learning


    Contributors:


    Publication date :

    2007-06-01


    Size :

    565758 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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