Road sign classification using Laplace kernel classifier
نویسندگان
چکیده
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...
متن کاملRoad sign classi ® cation using Laplace kernel
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. Smoothin...
متن کاملMulticlass Road Sign Detection using Multiplicative Kernel
We consider the problem of multiclass road sign detection using a classification function with multiplicative kernel comprised from two kernels. We show that problems of detection and within-foreground classification can be jointly solved by using one kernel to measure object-background differences and another one to account for within-class variations. The main idea behind this approach is tha...
متن کاملTraining a Support Vector Classifier using a Cauchy-Laplace Product Kernel
The importance of the support vector machine and its applicability to a wide range of problems is well known. The strength of the support vector machine lies in its kernel. In our recent paper, we have shown how the Laplacian kernel overcomes some of the drawbacks of the Gaussian kernel. However this was not a total remedy for the shortcomings of the Gaussian kernel. In this paper, we design a ...
متن کاملTraining a Support Vector Classifier using a Cauchy-Laplace Product Kernel
The importance of the support vector machine and its applicability to a wide range of problems is well known. The strength of the support vector machine lies in its kernel. In our recent paper, we have shown how the Laplacian kernel overcomes some of the drawbacks of the Gaussian kernel. However this was not a total remedy for the shortcomings of the Gaussian kernel. In this paper, we design a ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Pattern Recognition Letters
سال: 2000
ISSN: 0167-8655
DOI: 10.1016/s0167-8655(00)00078-7