Infrared image segmentation with Gaussian mixture modeling

Dong-Su Lee; Seokwon Yeom

doi:10.1117/12.919615

18 May 2012 Infrared image segmentation with Gaussian mixture modeling

Dong-Su Lee, Seokwon Yeom

Proceedings Volume 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII; 83551J (2012) https://doi.org/10.1117/12.919615
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

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.

Citation Download Citation

Dong-Su Lee and 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); https://doi.org/10.1117/12.919615

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