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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44063

Title: Road Sign Detection Using Eigen Color
Authors: Luo-Wei Tsai
Jun-Wei Hsieh
Chi-Hung Chuang
Yun-Jung Tseng
Kuo-Chin Fan
C.-C. Lee
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: image segmentation
image classification
feature extraction
filtering theory
radial basis function networks
statistical analysis
eigenvalues and eigenfunctions
image colour analysis
video signal processing
driver information systems
Date: 2008-09
Issue Date: 2017-11-14T06:19:51Z
Publisher: IET Computer Vision(formerly a part of IEE Proceedings Vision, Image & Signal Processing)
Abstract: Abstract:A novel colour-based method to detect road signs directly from videos is presented. A road sign is usually painted with different colours to show its functionalities. To detect it, different detectors should be designed to deal with its colour changes. A statistic linear model of colour change space that makes road sign colours be more compact and thus sufficiently concentrated on a smaller area is presented. On this model, only one detector is needed to detect different road signs even though their colours are different. The model is global and can be used to detect any new road signs. The colour model is invariant to different perspective effects and occlusions. After that, a radial basis function (RBF) network is then used to train a classifier to find all possible road sign candidates from road scenes. Furthermore, a verification process is applied to verify each candidate using its contour feature. After verification, a rectification process is used for rectifying each skewed road sign so that its embedded texts can be well segmented and recognised. Due to the filtering effect of the proposed colour model, different road signs can be very efficiently and effectively detected from videos. Experimental results have proved that the proposed method is robust, accurate and powerful in road sign detection.
Relation: 2(3), pp.164-177
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44063
Appears in Collections:[資訊工程學系] 期刊論文

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