@inproceedings{78f002842b3947ec85c0761348e0f5f5,
title = "Road speed sign recognition using edge-voting principle and learning vector quantization network",
abstract = "This paper presents an automatic speed sign detection and recognition for providing the visual driving-assistance of speed limits awareness. To reduce the influence of digital noise caused by lighting condition and pollution, a segmentation based on pan-red color information is applied to extract the shape of speed sign. Based on the edge-phase information of a circle shape, a novel edge-voting principle is proposed for fast detecting the speed sign candidate from road scenes. The recognition of the content of speed sign is achieved through a modified learning vector quantization (LVQ) network which also verifies each candidate to eliminate non-target blobs. Results show a high success rate and a low amount of false positives in both detection and recognition strategy under a wide variety of visual conditions.",
keywords = "Color segmentation, Detection, Edge-voting, Learning Vector Quantization (LVQ) network, Recognition, Speed sign",
author = "Chiang, \{Hsin Han\} and Chen, \{Yen Lin\} and Wang, \{Wen Qing\} and Lee, \{Tsu Tian\}",
year = "2010",
doi = "10.1109/COMPSYM.2010.5685511",
language = "English",
isbn = "9781424476404",
series = "ICS 2010 - International Computer Symposium",
pages = "246--251",
booktitle = "ICS 2010 - International Computer Symposium",
note = "2010 International Computer Symposium, ICS 2010 ; Conference date: 16-12-2010 Through 18-12-2010",
}