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dc.contributor.authorRuta, A-
dc.contributor.authorLi, Y-
dc.contributor.authorLiu, X-
dc.contributor.editorBerthold, MR-
dc.contributor.editorShawe-Taylor, J-
dc.contributor.editorLavrač, N-
dc.identifier.citationIn Berthold, M., Shawe-Taylor, J. and Lavrac, N (ed). Advances in Intelligent Data Analysis VII, Aug 2007en
dc.description.abstractReal-time road sign recognition has been of great interest for many years. This problem is often addressed in a two-stage procedure involving detection and classification. In this paper a novel approach to sign representation and classification is proposed. In many previous studies focus was put on deriving a set of discriminative features from a large amount of training data using global feature selection techniques e.g. Principal Component Analysis or AdaBoost. In our method we have chosen a simple yet robust image representation built on top of the Colour Distance Transform (CDT). Based on this representation, we introduce a feature selection algorithm which captures a variable-size set of local image regions ensuring maximum dissimilarity between each individual sign and all other signs. Experiments have shown that the discriminative local features extracted from the template sign images enable simple minimum-distance classification with error rate not exceeding 7%.en
dc.format.extent256 bytes-
dc.publisherHeidelberg: Springer-
dc.titleTraffic sign recognition using discriminative local featuresen
dc.typeBook Chapteren
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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