Classification of rice grains using fuzzy artmap neural network

Chong Yaw Wee, R. Paramesran, F. Takeda, T. Tsuzuki, H. Kadota, S. Shimanouchi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)


In this paper, a scaled invariant Zernike moment based feature extractor has been used to extract the relevant information from rice grain images for the purpose of classification. An incremental supervised learning and multidimensional map neural network, called fuzzy artmap (FA), has been proposed to reduce the learning time while maintaining high accuracy. A fast computation technique that uses the higher order Zernike polynomials to derive the lower order Zernike polynomials has been proposed to improve the computation speed of Zernike moments in real time applications.

Original languageEnglish
Title of host publicationProceedings - APCCAS 2002
Subtitle of host publicationAsia-Pacific Conference on Circuits and Systems
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages4
ISBN (Electronic)0780376900
Publication statusPublished - 2002
Externally publishedYes
EventIEEE Asia Pacific Conference on Circuits and Systems (APCCAS) 2002 - Denpasar, Bali, Indonesia
Duration: 28 Oct 200231 Oct 2002 (Proceedings)

Publication series

NameIEEE Asia-Pacific Conference on Circuits and Systems, Proceedings, APCCAS


ConferenceIEEE Asia Pacific Conference on Circuits and Systems (APCCAS) 2002
Abbreviated titleAPCCAS 2002
CityDenpasar, Bali
Internet address


  • Backpropagation algorithms
  • Communication industry
  • Data mining
  • Feature extraction
  • Fuzzy neural networks
  • Multilayer perceptrons
  • Neural networks
  • Polynomials
  • Supervised learning
  • Systems engineering and theory

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