Multi-Counterpropagation Network Model For Colour Recognition

Authors

  • Razali Yaakob Department of Computer Science Faculty of Comp. Sc. And Inform. Tech., Universiti Putra Malaysia
  • Md. Nasir Sulaiman Department of Computer Science Faculty of Comp. Sc. And Inform. Tech., Universiti Putra Malaysia
  • Ramlan Mahmod Department of Computer Science Faculty of Comp. Sc. And Inform. Tech., Universiti Putra Malaysia
  • Mahmud Tengku Muda Mohamed Department of Agronomy and Horticulture, Faculty of Agriculture, Universiti Putra Malaysia
  • Abd. Rahman Ramli Department of Computer and Communications, Faculty of Engineering, Universiti Putra Malaysia

Keywords:

CPN model, competitive layer, unsupervised learning, supervised learning, Minolta Chroma Meters

Abstract

Minolta Chroma Meters was used to convert colours into numbers. It offers five different colour systems for measuring absolute chromaticity, that is, CIE Yxy, L*a*b*, L*C*Ho, Hunter Lab and XYZ. In this study, only L*a*b* is used, and combinations of two counterpropagation network (CPN) are required to recognise 808 colours produced by The Royal Horticultural Society, based on RHS Colour Chart [1]. Our proposed neural network model is tested; the result shows that 99% of trained data are recognised, against 98% for untrained data.

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Published

1999-06-01

How to Cite

Yaakob, R., Sulaiman, M. N., Mahmod, R., Mohamed, M. T. M., & Ramli, A. R. (1999). Multi-Counterpropagation Network Model For Colour Recognition. Malaysian Journal of Computer Science, 12(1), 38–46. Retrieved from https://mjir.um.edu.my/index.php/MJCS/article/view/5746

Issue

Vol. 12 No. 1 (1999): Malaysian Journal of Computer Science

Section

Articles

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