Road sign classi ® cation using Laplace kernel
نویسنده
چکیده
Driver support systems (DSS) of intelligent vehicles will predict potentially dangerous situations in heavy trac, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for trac situation understanding is presented by road signs. A new kernel rule has been developed for road sign classi®cation using the Laplace probability density. Smoothing parameters of the Laplace kernel are optimized by the pseudolikelihood cross-validation method. To maximize the pseudo-likelihood function, an Expectation±Maximization algorithm is used. The algorithm has been tested on a dataset with more than 4900 noisy images. A comparison to other classi®cation methods is also given. Ó 2000 Elsevier Science B.V. All rights reserved.
منابع مشابه
Road sign classification using Laplace kernel classifier
Driver support systems of intelligent vehicles will predict potentially dangerous situations in heavy traffic, help with navigation and vehicle guidance and interact with a human driver. Important information necessary for traffic situation understanding is presented by road signs. A new kernel rule has been developed for road sign classification using the Laplace probability density. Smoothing...
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