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Journal of Intelligent Systems

Editor-in-Chief: Fleyeh, Hasan


CiteScore 2018: 1.03

SCImago Journal Rank (SJR) 2018: 0.188
Source Normalized Impact per Paper (SNIP) 2018: 0.533

Online
ISSN
2191-026X
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Volume 20, Issue 1

Issues

Adaptive Shadow and Highlight Invariant Colour Segmentation for Traffic Sign Recognition Based on Kohonen SOM

Hasan Fleyeh
  • Department of Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
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  • Other articles by this author:
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/ Al-Hasanat R. M. Bin Mumtaz
  • Department of Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2011-02-02 | DOI: https://doi.org/10.1515/jisys.2011.002

Abstract

This paper describes an intelligent algorithm for traffic sign recognition which converges quickly, is accurate in its segmentation and adaptive in its behaviour. The proposed approach can segment images of traffic signs in different lighting and environmental conditions and in different countries. It is based on using Kohonen's Self-Organizing Maps (SOM) as a clustering tool and it is developed for Intelligent Vehicle applications. The current approach does not need any prior training. Instead, a slight portion, which is about 1% of the image under investigation, is used for training. This is a key issue to ensure fast convergence and high adaptability. The current approach was tested by using 442 images which were collected under different environmental conditions and from different countries. The proposed approach shows promising results; good improvement of 73% is observed in faded traffic sign images compared with 53.3% using the traditional algorithm. The adaptability of the system is evident from the segmentation of the traffic sign images from various countries where the result is 96% for the nine countries included in the test.

Keywords.: Colour segmentation; neural networks; traffic signs; recognition; classification; SOM

About the article

Received: 2010-09-21

Published Online: 2011-02-02

Published in Print: 2011-04-01


Citation Information: Journal of Intelligent Systems, Volume 20, Issue 1, Pages 15–31, ISSN (Online) 2191-026X, ISSN (Print) 0334-1860, DOI: https://doi.org/10.1515/jisys.2011.002.

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