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Proceedings Paper

The comparative analysis of image restoration represented as a matrix and as a vector using feed forward neural networks
Author(s): Igor Mardare; Veacheslav Perju; David Casasent; Olga Ghincul
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Paper Abstract

This work contains the results of the experiments on the restoration of the defective images proceeded in a matrix and a vector form with the help of the feed forward neural network. Sometimes it is convenient to represent an image as a vector rather than as a matrix. So the target of this work is to show experimentally what kind of input provides a better restoration, judging from the Euclid's distance of the output of a trained network. This work also shows the differences between processing different types of image presentation of the neuron network. Making a comparative analysis of a matrix and a vector form of presenting the images which are proceeded to a feed forward network allows stating some specific characteristics of a network. These characteristics include the optimal architecture of a network, the number of layers, the number of neurons in each layer and the time of an image restoration. Taking into account the network's characteristics and the most important factor - the Euclid's distance, are drawn conclusions that concern what is the best way of representing images that we want to restore using a feed forward network.

Paper Details

Date Published: 13 April 2009
PDF: 9 pages
Proc. SPIE 7340, Optical Pattern Recognition XX, 73400U (13 April 2009); doi: 10.1117/12.819268
Show Author Affiliations
Igor Mardare, Technical Univ. of Moldova (Moldova)
Veacheslav Perju, Technical Univ. of Moldova (Moldova)
Free International Univ. of Moldova (Moldova)
David Casasent, Carnegie Mellon Univ. (United States)
Olga Ghincul, Technical Univ. of Moldova (Moldova)


Published in SPIE Proceedings Vol. 7340:
Optical Pattern Recognition XX
David P. Casasent; Tien-Hsin Chao, Editor(s)

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