Neural networks for classification of 2-D patterns
- 7 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 3, 1568-1571
- https://doi.org/10.1109/icosp.2000.893399
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.Keywords
This publication has 8 references indexed in Scilit:
- Fast Second Order Learning Algorithm for Feedforward Multilayer Neural Networks and its ApplicationsNeural Networks, 1996
- The Nature of Statistical Learning TheoryPublished by Springer Nature ,1995
- 'Neural-gas' network for vector quantization and its application to time-series predictionIEEE Transactions on Neural Networks, 1993
- Complex Vectors and Image IdentificationThe College Mathematics Journal, 1993
- Noise injection into inputs in back-propagation learningIEEE Transactions on Systems, Man, and Cybernetics, 1992
- The self-organizing mapProceedings of the IEEE, 1990
- Counterpropagation networksApplied Optics, 1987
- Elliptic Fourier features of a closed contourComputer Graphics and Image Processing, 1982