Improved color barycenter model for road-sign detection

Qieshi Zhang, Sei Ichiro Kamata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013
PublisherMVA Organization
Pages93-96
Number of pages4
ISBN (Print)9784901122139
Publication statusPublished - 2013
Event13th IAPR International Conference on Machine Vision Applications, MVA 2013 - Kyoto, Japan
Duration: 2013 May 202013 May 23

Publication series

NameProceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013

Conference

Conference13th IAPR International Conference on Machine Vision Applications, MVA 2013
CountryJapan
CityKyoto
Period13/5/2013/5/23

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Improved color barycenter model for road-sign detection'. Together they form a unique fingerprint.

  • Cite this

    Zhang, Q., & Kamata, S. I. (2013). Improved color barycenter model for road-sign detection. In Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013 (pp. 93-96). (Proceedings of the 13th IAPR International Conference on Machine Vision Applications, MVA 2013). MVA Organization.