Pattern recognition using higher-order local autocorrelation coefficients

POPOVICI, Vlad a JP THIRAN. Pattern recognition using higher-order local autocorrelation coefficients. NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS. NEW YORK: IEEE, 2002, s. 229-238.

Základní údaje

Originální název

Pattern recognition using higher-order local autocorrelation coefficients

Autoři

POPOVICI, Vlad a JP THIRAN

Vydání

NEURAL NETWORKS FOR SIGNAL PROCESSING XII, PROCEEDINGS, NEW YORK, IEEE, 2002

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Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Utajení

není předmětem státního či obchodního tajemství

Označené pro přenos do RIV

Ne

UT WoS

000178710800023

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Anotace

V originále

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.