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Color Segmentation using LVQ-Learning Vector Quantization
Dalarna University, School of Technology and Business Studies, Computer Engineering.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesis
Abstract [en]

This thesis aims to present a color segmentation approach for traffic sign recognition based on LVQ neural networks. The RGB images were converted into HSV color space, and segmented using LVQ depending on the hue and saturation values of each pixel in the HSV color space. LVQ neural network was used to segment red, blue and yellow colors on the road and traffic signs to detect and recognize them. LVQ was effectively applied to 536 sampled images taken from different countries in different conditions with 89% accuracy and the execution time of each image among 31 images was calculated in between 0.726sec to 0.844sec. The method was tested in different environmental conditions and LVQ showed its capacity to reasonably segment color despite remarkable illumination differences. The results showed high robustness.

Place, publisher, year, edition, pages
Borlänge, 2010. , 88 p.
Keyword [en]
Color, RGB, HSV, Hue, Saturation, Pixel, Matrix, MATLAB, LVQ.
Identifiers
URN: urn:nbn:se:du-5315OAI: oai:dalea.du.se:5315DiVA: diva2:519011
Uppsok
Technology
Supervisors
Available from: 2011-02-09 Created: 2011-02-09 Last updated: 2012-04-24Bibliographically approved

Open Access in DiVA

fulltext(11295 kB)1324 downloads
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf