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Faint Object Classification Using Artificial Neural Networks

Published online by Cambridge University Press:  26 July 2016

M. Serra-Ricart
M. Serra-Ricart*
Affiliation:
Instituto de Astrofísica de Canarias, E-38200 La Laguna, Tenerife, Spain

Abstract

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Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

Type
Part Five: Image Detection, Cataloguing and Classification
Information
Symposium - International Astronomical Union , Volume 161: Astronomy from Wide-Field Imaging , 1994 , pp. 249 - 252
Copyright
Copyright © Kluwer 1994 

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