Neural networks for classification of 2-D patterns

Stanisław Osowski , D.D. Nghia

Abstract

The 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
Author Stanisław Osowski (FoEE / ITEEMIS)
Stanisław Osowski,,
- The Institute of the Theory of Electrical Engineering, Measurement and Information Systems
, D.D. Nghia
D.D. Nghia,,
-
Pages1568-1571 vol.3
Book 5th International Conference on Signal Processing Proceedings, 2000. WCCC-ICSP 2000, vol. 3, 2000
Keywords in English2D 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
DOIDOI:10.1109/ICOSP.2000.893399
Score (nominal)3
Publication indicators WoS Citations = 3; GS Citations = 5.0
Citation count*5 (2013-01-30)
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