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Proceedings Paper

JTC based concealed object detection in terahertz imaging
Author(s): M. U. Habib; M. S. Alam; W. K. Al-Assadi
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Paper Abstract

Detection of concealed objects under cloth or inside paper/lather/plastic box is a challenge for security applications. With terahertz (THz) imaging technology, it is possible to spot concealed objects inside plastic box, underneath cloths paper or similar scenarios. THz frequency domain (~100 GHz - 10 THz) shows a unique feature in the under-used domain of the electromagnetic spectrum which helps to acquire image of concealed objects. This property of THz wave makes it useful in a variety of applications. Previously millimeter wave imaging and infrared imaging were used for detection of concealed features in an image with limited success rate. THz imaging helps solving the problem to a great extent because it can transmit through substances like cloths, paper, plastic, dried food etc. THz images have poor quality and low signal-to-noise-ratio. Noises and related artifacts must be reduced for proper detection of concealed objects. In this paper, a new technique for artifact reduction and detection of concealed object is proposed by utilizing nonzero-order fringe adjusted joint transform correlation (NFJTC) technique. In the proposed NFJTC technique, the joint power spectrum (JPS) is modified to obtain the nonzero-order fringe-adjusted joint power spectrum. NFJTC is already been used for object detection but never been used to detect concealed objects in THz imagery. Test results using real life THz imagery confirm the effectiveness of the proposed technique.

Paper Details

Date Published: 29 April 2013
PDF: 9 pages
Proc. SPIE 8748, Optical Pattern Recognition XXIV, 87480R (29 April 2013); doi: 10.1117/12.2018253
Show Author Affiliations
M. U. Habib, Univ. of South Alabama (United States)
M. S. Alam, Univ. of South Alabama (United States)
W. K. Al-Assadi, Univ. of South Alabama (United States)

Published in SPIE Proceedings Vol. 8748:
Optical Pattern Recognition XXIV
David Casasent; Tien-Hsin Chao, Editor(s)

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