Neural networks for classification of 2-D patterns
Stanisław Osowski , D.D. Nghia
AbstractThe paper presents the application of three different types of neural networks to the 2D pattern recognition on the basis of its shape. They include the multilayer perceptron (MLP), Kohonen self-organizing network and hybrid structure composed of the self-organizing layer and the MLP subnetwork connected in cascade. The recognition is based on the features extracted from the Fourier transform of the data describing the shape of the pattern. Application of different neural network structure results in different accuracy of recognition and classification. The numerical experiments performed for the recognition of the shapes of airplanes have shown the superiority of the hybrid structure
|Book||5th International Conference on Signal Processing Proceedings, 2000. WCCC-ICSP 2000, vol. 3, 2000|
|Keywords in English||2D pattern classification, 2D pattern recognition, airplanes, cascade-connected neural nets, feature extraction, Fourier transform, Fourier transforms, image classification, Kohonen self-organizing network, MLP, multilayer perceptron, multilayer perceptrons, neural networks, self-organising feature maps, shape-based recognition|
|Publication indicators||= 3; = 5.0|
|Citation count*||5 (2013-01-30)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.