@Inproceedings{popovici2002b,
author              = { V. Popovici and J. Ph. Thiran },
title               = { Pattern Recognition using Higher-Order Local Autocorrelation Coefficients },
pages               = { 229--238 },
year                = 2002,
publisher           = {IEEE},
booktitle           = { Proceedings of the 2002 IEEE Workshop on Neural Networks for Signal Processing XII (NNSP)}
abstract            = { 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.
			}
}