@Article{SergioEscalera2010,
author="Sergio Escalera
and Oriol Pujol
and Petia Radeva",
title="Traffic sign recognition system with $\beta$ -correction",
journal="Machine Vision and Applications",
year="2010",
publisher="Springer-Verlag",
volume="21",
number="2",
pages="99--111",
abstract="Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel $\beta$-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.",
optnote="MILAB;HUPBA",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1276), last updated on Thu, 18 Jan 2018 12:02:06 +0100",
issn="0932-8092",
doi="10.1007/s00138-008-0145-z"
}