Neural networks learning methods for image processing applications

Authors:
Jiri Stastny;Vladislav Skorpil
Affiliations:
Department of Automation and Computer Science, Brno University of Technology, Brno, Czech Republic;Department of Telekommunications, Brno University of Technology, Brno, Czech Republic
Venue:
ESPOCO'05 Proceedings of the 4th WSEAS International Conference on Electronic, Signal Processing and Control
Year:
2007

Citing 5
Cited 0

Machine learning, neural and statistical classification

Machine learning, neural and statistical classification
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Algorithms for Graphics and Imag

Algorithms for Graphics and Imag
Pattern Recognition and Neural Networks

Pattern Recognition and Neural Networks
A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization

Proceedings of the 6th International Conference on Genetic Algorithms

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper deals of the problem of the image processing by using artificial neural networks. The MLP neural network was used. We compared results obtained by a using of different learning algorithms - the classical back-propagation algorithm (BP) and the genetic algorithm (GA). The real technological scene for image processing was simulated with digitization of two-dimensional pictures.