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9 June 2014 Gaussian mixture modeling and clustering of hidden objects with multichannel passive millimeter wave images
Seokwon Yeom, Dong-Su Lee, Hyoung Lee, Jung-Young Son, V. P. Guschin
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Proceedings Volume 9078, Passive and Active Millimeter-Wave Imaging XVII; 90780O (2014) https://doi.org/10.1117/12.2053695
Event: SPIE Defense + Security, 2014, Baltimore, MD, United States
Abstract
In this paper, we review automatic concealed object recognition with multi-channel passive millimeter wave images. A four-channel passive millimeter wave imaging system operates in the 8 and 3 mm wavelength regimes with linear vertical and horizontal polarization directions. Registration between multi-channel images and segmentation of concealed objects are addressed. Multi-channel image registration is performed by means of the affine transform derived by the geometric feature matching. Gaussian mixture models are adopted to cluster hidden object pixels in the images. Multi-level segmentation separates the human body region from the background, and concealed objects from the body region, sequentially. In the experiments, the metallic and non-metallic objects concealed under clothing are captured and processed.
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Seokwon Yeom, Dong-Su Lee, Hyoung Lee, Jung-Young Son, and V. P. Guschin "Gaussian mixture modeling and clustering of hidden objects with multichannel passive millimeter wave images", Proc. SPIE 9078, Passive and Active Millimeter-Wave Imaging XVII, 90780O (9 June 2014); https://doi.org/10.1117/12.2053695
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KEYWORDS
Image segmentation
Extremely high frequency
Image registration
Expectation maximization algorithms
Image processing
Imaging systems
Object recognition
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