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@article{1090325,
author = {Popovici, Vlad and Thiran, JP},
article_location = {NEW YORK},
language = {eng},
journal = {NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS},
title = {Pattern recognition using higher-order local autocorrelation coefficients},
year = {2002}
}
TY - JOUR
ID - 1090325
AU - Popovici, Vlad - Thiran, JP
PY - 2002
TI - Pattern recognition using higher-order local autocorrelation coefficients
JF - NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS
SP - 229-238
EP - 229-238
PB - IEEE
N2 - The autocorrelations have been previously used as features for 1D or 2D signal classification in a wide range of applications, like texture classification, face detection and recognition, EEG signal classification, and so on. However, in almost all the cases, the high computational costs have hampered the extension to higher orders (more than the second order). In this paper we present a method which avoids the computation of the autocorrelation coefficients and which can be applied to a large set of tools commonly used in statistical pattern recognition. We will discuss different scenarios of using the autocorrelations and we will show that the order of autocorrelations is no longer an obstacle.
ER -
POPOVICI, Vlad a JP THIRAN. Pattern recognition using higher-order local autocorrelation coefficients. \textit{NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS}. NEW YORK: IEEE, 2002, s.~229-238.
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