Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/35276
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Type: Conference paper
Title: Statistical model for the classification of the wavelet transforms of T-ray pulses
Author: Yin, X.
Ng, B.
Ferguson, B.
Mickan, S.
Abbott, D.
Citation: 18th International Conference on Pattern Recognition (ICPR'06) Volume 3, pp. 236-239
Publisher: IEEE
Publisher Place: USA
Issue Date: 2006
Series/Report no.: INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION
ISBN: 0769525210
9780769525211
ISSN: 1051-4651
Conference Name: International Conference on Pattern Recognition (18th : 2006 : Hong Kong)
Statement of
Responsibility: 
X.X. Yin, B.W.-H. Ng, B. Ferguson, S.P. Mickan, D. Abbott
Abstract: This study applies Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) modeling to wavelet decomposed terahertz pulsed signals to assist biomedical diagnosis and mail/packaging inspection. T-ray classification systems supply a wealth of information about test samples to make possible the discrimination of heterogeneous layers within an object. In this paper, the classification of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of seven different powder samples are demonstrated. A correlation method and an improved Prony’s method are investigated in the calculation of the AR and ARMA model parameters. These parameters are obtained for models from second to eighth orders and are subsequently used as feature vectors for classification. For pre-processing, wavelet de-noising methods including the SURE (Stein’s Unbiased Estimate of Risk) and heuristic SURE soft threshold shrinkage algorithms are employed to de-noise the normalised T-ray pulsed signals. A Mahalanobis distance classifier is used to perform the final classification. The error prediction covariance of AR/ARMA modeling and the classification accuracy are calculated and used as metrics for comparison.
Description: ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
RMID: 0020062992
DOI: 10.1109/ICPR.2006.1077
Appears in Collections:Electrical and Electronic Engineering publications

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