
Proceedings Paper
Recognition of propagating vibrations and invariant features for classificationFormat | Member Price | Non-Member Price |
---|---|---|
$17.00 | $21.00 |
Paper Abstract
The vibrations produced by objects, for example by a plate or cylinder insonified by a sonar wave, exhibit characteristics unique to the particular structure, which
can be used to distinguish among different objects. The situation is complicated, however,
by many factors, a particularly important one being propagation through media. As a vibration
propagates, its characteristics can change simply due to the propagation channel;
for example, in a dispersive channel, the duration of the vibration will increase with propagation
distance. These channel effects are clearly detrimental to automatic recognition
because they do not represent the object of interest and they increase the variability of
the measured responses, especially if measurements are obtained from targets at different
locations. Our principal aim is to identify characteristics of propagating vibrations and
waves that may be used as features for classification. We discuss various moment-like
features of a propagating vibration. In the first set of moments, namely temporal moments
such as mean and duration at a given location, we give explicit formulations that
quantify the effects of dispersion. Accordingly, one can then compensate for the effects
of dispersion on these moments. We then consider another new class of moments, which
are invariant to dispersion and hence may be useful as features for dispersive propagation.
We present classification results comparing these invariant features to related non-invariant
features, for classification of simulated backscatter from different steel shells in a dispersive
environment.
Paper Details
Date Published: 18 May 2006
PDF: 14 pages
Proc. SPIE 6234, Automatic Target Recognition XVI, 623415 (18 May 2006); doi: 10.1117/12.668253
Published in SPIE Proceedings Vol. 6234:
Automatic Target Recognition XVI
Firooz A. Sadjadi, Editor(s)
PDF: 14 pages
Proc. SPIE 6234, Automatic Target Recognition XVI, 623415 (18 May 2006); doi: 10.1117/12.668253
Show Author Affiliations
Greg Okopal, Univ. of Pittsburgh (United States)
Patrick Loughlin, Univ. of Pittsburgh (United States)
Patrick Loughlin, Univ. of Pittsburgh (United States)
Leon Cohen, City Univ. of New York (United States)
Published in SPIE Proceedings Vol. 6234:
Automatic Target Recognition XVI
Firooz A. Sadjadi, Editor(s)
© SPIE. Terms of Use
