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Proceedings Paper

Image and particle track filtering using a "dynamic" cellular automaton coupled to a neural network
Author(s): Marco Casolino; M. P. Martegani; Piergiorgio Picozza
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Paper Abstract

In this paper the noise removal capabilities of a cellular automaton are applied in two different fields. The first application is performed on 4 Gev pion and electron experimental events taken at Cern PS with a silicon tungsten tracking calorimeter. Particle interaction with the material of the calorimeter can produce different interactions resulting in energy releases and topology patterns dependent on the primary particle nature. The evolution rules devised for the CA have therefore to reckon with these different topologies in order to remove noise and restore interrupted tracks. The distributions of some discriminating parameters are compared with Monte Carlo data before and after filtering by the automaton and the agreement is shown to improve if pions are considered. To successfully take into account electromagnetic showers, more than one different evolutionary rule has to be considered. A neural network accordingly trained selects each step of the evolutions closer to the training classes. Upon convergence of these two different `paths,' obtained with dynamic update rules, the image with the highest output results is filtered and classified. The second use of cellular automata is in DNA sequence autoradiograph films. These images may be filtered by a CA which improves nucleotide readability and speeds up sequencing process.

Paper Details

Date Published: 6 April 1995
PDF: 12 pages
Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205124
Show Author Affiliations
Marco Casolino, Univ. di Roma Tor Vergata (Italy)
M. P. Martegani, Univ. di Roma La Sapienza (Italy)
Piergiorgio Picozza, Univ. di Roma Tor Vergata (Italy)

Published in SPIE Proceedings Vol. 2492:
Applications and Science of Artificial Neural Networks
Steven K. Rogers; Dennis W. Ruck, Editor(s)

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