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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 18, Iss. 10 — May. 10, 2010
  • pp: 10659–10667

Automatic image segmentation for concealed object detection using the expectation-maximization algorithm

Dong-Su Lee, Seokwon Yeom, Jung-Young Son, and Shin-Hwan Kim  »View Author Affiliations


Optics Express, Vol. 18, Issue 10, pp. 10659-10667 (2010)
http://dx.doi.org/10.1364/OE.18.010659


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Abstract

We address an image segmentation method to detect concealed objects captured by passive millimeter wave (MMW) imaging. Passive MMW imaging can create interpretable imagery on the objects concealed under clothing, which gives the great advantage to the security system. In this paper, we propose the multi-level expectation maximization (EM) method to separate the concealed objects from the other area in the image. We apply the EM method to obtain a Gaussian mixture model (GMM) of the acquired image. In the experiments, we evaluate the performance by the average probability of error. We will show that the consecutive EM processes separates the object area more accurately than the conventional EM method.

© 2010 OSA

OCIS Codes
(000.5490) General : Probability theory, stochastic processes, and statistics
(100.2000) Image processing : Digital image processing
(100.5010) Image processing : Pattern recognition
(110.2970) Imaging systems : Image detection systems
(100.3008) Image processing : Image recognition, algorithms and filters
(110.6795) Imaging systems : Terahertz imaging

ToC Category:
Image Processing

History
Original Manuscript: March 24, 2010
Revised Manuscript: April 27, 2010
Manuscript Accepted: April 29, 2010
Published: May 6, 2010

Citation
Dong-Su Lee, Seokwon Yeom, Jung-Young Son, and Shin-Hwan Kim, "Automatic image segmentation for concealed object detection using the expectation-maximization algorithm," Opt. Express 18, 10659-10667 (2010)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-18-10-10659


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