DocumentCode :
2071496
Title :
Digital image processing with dynamical neural networks for resource management: theories aspects
Author :
Morales-Mendoza, L.J. ; Ibarra-Manzano, O.G. ; Ibarra-Manzano, M.A. ; Shmaliy, Y.
Author_Institution :
Fac. de Ingenieria Mecanica Electrica Electronica, Salamanca
fYear :
2006
fDate :
7-10 Nov. 2006
Firstpage :
199
Lastpage :
203
Abstract :
In this paper, we present the theories aspects of the problem from the reconstruction and enhancing of radar imaging in the natural resource management with detection of specials characteristics. The problem is oriented to the data massive processing involving with recurrent neural networks. The Maximum Entropy Variational Analysis method is implemented into the recurrent neural networks modified Hopfield-type to the reconstruction and, detection and stopping edges (enhancing) in the radar imaging. Furthermore, here we present two forms of the computational implementation of the recurrent neural network.
Keywords :
image enhancement; image reconstruction; maximum entropy methods; natural resources; radar imaging; recurrent neural nets; data massive processing; digital image processing; dynamical neural network; image enhancement; image reconstruction; maximum entropy variational analysis method; natural resource management; radar imaging; recurrent neural network; Digital images; Entropy; Image analysis; Image edge detection; Image reconstruction; Neural networks; Radar detection; Radar imaging; Recurrent neural networks; Resource management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronics and Photonics, 2006. MEP 2006. Multiconference on
Conference_Location :
Guanajuato
Print_ISBN :
1-4244-0627-7
Electronic_ISBN :
1-4244-0628-5
Type :
conf
DOI :
10.1109/MEP.2006.335663
Filename :
4135747
Link To Document :
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