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Optical Engineering

Chaotic neural networks clustering: an application to landmine detection by dynamic infrared imaging
Author(s): Leonardo Angelini; F. De Carlo; Carmela Marangi; M. Mannarelli; Giuseppe Nardulli; M. Pellicoro; Giuseppe Satalino; Sebastiano Stramaglia
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Paper Abstract

We describe a nonparametric approach of dynamic thermography to the detection of buried antipersonnel (AP) mines. Dynamic thermography consists of processing temporal sequences of IR images taken from the same scene, submitted to either artificial or natural temperature variations. The aim is to obtain an image segmentation where mine and soil can be discriminated due to the different time evolution of their thermal properties. The proposed approach is rooted in a clustering stage performed by a chaotic neural network and provides the correct classification by analyzing very short image sequences, thus enabling a fast acquisition time. The effectiveness of the method is demonstrated on image sequences of plastic AP mines taken from realistic mine fields.

Paper Details

Date Published: 1 December 2001
PDF: 7 pages
Opt. Eng. 40(12) doi: 10.1117/1.1412623
Published in: Optical Engineering Volume 40, Issue 12
Show Author Affiliations
Leonardo Angelini, University of Bari (Italy)
F. De Carlo, Univ. of Bari (Italy)
Carmela Marangi, Univ. of Bari (Italy)
M. Mannarelli, University of Bari (Italy)
Giuseppe Nardulli, Univ. of Bari (Italy)
M. Pellicoro, Univ. of Bari (Italy)
Giuseppe Satalino, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Sebastiano Stramaglia, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)

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