Classification of rice grains using fuzzy artmap neural network
Chong‐Yaw Wee, Raveendran Paramesran, Fumiaki Takeda, T. Tsuzuki, Hideki Kadota, S. Shimanouchi · Asia Pacific Conference on Circuits and Systems · 2003
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.