Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/35277
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Type: Conference paper
Title: Wavelet based segment detection and feature extraction for 3D T-ray CT pattern classification
Author: Yin, X.
Ng, B.
Ferguson, B.
Mickan, S.
Abbott, D.
Citation: 2006 IEEE 12th Digital Signal Processing Workshop and 4th IEEE Signal Processing Education Workshop : Teton National Park, WY : 24-27 September 2006 :pp.602-607
Publisher: IEEE
Publisher Place: USA
Issue Date: 2006
ISBN: 1424405343
Conference Name: IEEE Digital Signal Processing Workshop (12th : 2006 : Grand Teton National Park, Wyoming)
Statement of
Responsibility: 
X.X. Yin, B.W.-H. Ng, B. Ferguson, S.P. Mickan, D. Abbott
Abstract: This paper explores three dimensional (3D) Terahertz (T-rays) computed tomographic (CT) classification based on T-ray functional imaging techniques. The target objects are separated by their refractive indices, which are indicated by the intensity in the images. Segmentation techniques are employed to identify the position of each pixel belonging to the different classes. Wavelet methods are applied to the detected T-ray pulsed responses for feature extraction. A Mahalanobis distance classifier is selected for the final classification task. This paper presents T-ray CT classification techniques that allow analysis of measured T-ray transmission image statistics and that automatically identify materials within a heterogeneous structure.
Description: Copyright © 2006 IEEE
RMID: 0020062993
DOI: 10.1109/DSPWS.2006.265494
Appears in Collections:Electrical and Electronic Engineering publications

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