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Proceedings Paper

Infrared image segmentation with Gaussian mixture modeling
Author(s): Dong-Su Lee; Seokwon Yeom
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Paper Abstract

Infrared imaging allows surveillance during the night, thus it has been widely used for military and security applications. However, infrared images are generally characterized by low resolution, low contrast, and an unclear texture with no color information. Moreover, various types of noises and background clutters can degrade the image quality. This paper discusses multi-level segmentation for infrared images. The expectation-maximization algorithm is adopted to cluster pixels on the basis of Gaussian mixture models. The use of the multi-level segmentation method enables the extraction of human target regions from the background of the image. Several infrared images are processed to demonstrate the effectiveness of the presented method.

Paper Details

Date Published: 18 May 2012
PDF: 5 pages
Proc. SPIE 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII, 83551J (18 May 2012); doi: 10.1117/12.919615
Show Author Affiliations
Dong-Su Lee, Daegu Univ. (Korea, Republic of)
Seokwon Yeom, Daegu Univ. (Korea, Republic of)


Published in SPIE Proceedings Vol. 8355:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII
Gerald C. Holst; Keith A. Krapels, Editor(s)

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