18 May 2012 Infrared image segmentation with Gaussian mixture modeling
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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.
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Dong-Su Lee, Seokwon Yeom, "Infrared image segmentation with Gaussian mixture modeling", Proc. SPIE 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII, 83551J (18 May 2012); doi: 10.1117/12.919615; https://doi.org/10.1117/12.919615
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