TY - GEN
T1 - Improved color barycenter model for road-sign detection
AU - Zhang, Qieshi
AU - Kamata, Sei Ichiro
N1 - Publisher Copyright:
© 2013; MVA Organization. All rights reserved.
PY - 2013
Y1 - 2013
N2 - This paper proposes an improved color barycenter model (CBM) for road sign detection. The previous version of CBM can find out the colors of road-sign (RS), but its accuracy is not high enough for magenta and blue region segmentation. The improved CBM extends the barycenter distribution to cylinder coordinate and takes the number of colors in every point into account. Then the K-meansclusteringisusedtoanalyze the distribution under cylinder coordinate. Using Geodesic distance instead of Euclidean distance for K-means clustering and some conditions provided by the initial color region of CBM is used to constrain K-means operation. The experimental results show that the improved method is able to detect RS with high robustness.
AB - This paper proposes an improved color barycenter model (CBM) for road sign detection. The previous version of CBM can find out the colors of road-sign (RS), but its accuracy is not high enough for magenta and blue region segmentation. The improved CBM extends the barycenter distribution to cylinder coordinate and takes the number of colors in every point into account. Then the K-meansclusteringisusedtoanalyze the distribution under cylinder coordinate. Using Geodesic distance instead of Euclidean distance for K-means clustering and some conditions provided by the initial color region of CBM is used to constrain K-means operation. The experimental results show that the improved method is able to detect RS with high robustness.
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M3 - Conference contribution
AN - SCOPUS:85083085224
SN - 9784901122139
T3 - Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
SP - 93
EP - 96
BT - Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
PB - MVA Organization
T2 - 13th IAPR International Conference on Machine Vision Applications, MVA 2013
Y2 - 20 May 2013 through 23 May 2013
ER -