Modular Morphological Neural Network Training via Adaptive Genetic Algorithm for Designing Translation Invariant Operators

Author

Araujo, Rd.A. ; Madeiro, Francisco ; De Sousa, Robson P. ; Pessoa, Lucio F C

Author_Institution

Dept. of Stat. & Comput. Sci., Pemambuco Catholic Univ., Recife

Volume

2

fYear

2006

fDate

14-19 May 2006

Abstract

In the present paper, adaptive genetic algorithm (AGA) is used for training a modular morphological neural network (MMNN) for designing translation invariant operators via Matheron decomposition and via Banon and Barrera decomposition. The operators are applied to restoration of images corrupted by salt and pepper noise. The AGA is used to determine the weights, architecture and number of modules of the MMNN. Results in terms of noise to signal ratio show that the method proposed in the present work lead to a better operators performance when compared to other methods previously proposed in the literature

Keywords

genetic algorithms; image processing; mathematical morphology; neural nets; Banon decomposition; Barrera decomposition; Matheron decomposition; adaptive genetic algorithm; image processing; modular morphological neural network training; noise to signal ratio; translation invariant operators; Adaptive systems; Algorithm design and analysis; Equations; Filters; Genetic algorithms; Image restoration; Morphology; Multi-layer neural network; Neural networks; Semiconductor device noise;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1660482

Filename

1660482