@article{ref1,
title="Road-Sign Detection and Recognition Based on Support Vector Machines",
journal="IEEE transactions on intelligent transportation systems",
year="2007",
author="Saturnino, MB and Lafuente-Arroyo, S and Gil-Jimenez, P and Gomez-Moreno, H and Lopez-Ferreras, F",
volume="8",
number="2",
pages="264-278",
abstract="In this paper the authors present an automated method for road-sign detection and recognition that relies on support vector machines (SVM) for applications in automatic traffic sign maintenance as well as in visual driver assistance systems. The presented system employs classification and detection in three steps. The first is segmentation from the color of the pixel to be analyzed using hue saturation intensity (HSI). The next step is the determination of traffic sign shape through linear SVM and, lastly, content recognition is established through Gaussian-kernel SVM. Of these two steps, the first is designed to classify the candidate blob as a specific shape among the types of traffic signs stored in the vehicle?s database. The second, which uses the Gaussian kernel, is used to determine what shapes and content is printed on the sign itself.<p />",
language="",
issn="1524-9050",
doi="",
url="http://dx.doi.org/"
}