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- W2018265654 abstract "This work presents the application of a basic unsupervised classification algorithm for the segmentation of indoor passiveTerahertz images. The 30,000 pixel broadband images of a person with concealed weapons under clothing are takenat a range of 0.8-2m over a frequency range of 0.1-1.2THz using single-pixel row-based raster scanning. The spiral-antennacoupled 36x1x0.02mm Nb bridge cryogenic micro-bolometers are developed at NIST-Optoelectronics Division.The antenna is evaporated on a 250mm thick Si substrate with a 4mm diameter hyper-hemispherical Si lens. The NETDof the microbolometer is 125mK at an integration time of 30 ms. The background temperature calibration is performedwith a known 25 pixel source above 330 K, and a measured background fluctuation of 200-500mK. Several weaponswere concealed under different fabrics: cotton, polyester, windblocker jacket and thermal sweater. Measured temperaturecontrasts ranged from 0.5-1K for wrinkles in clothing to 5K for a zipper and 8K for the concealed weapon. In order toautomate feature detection in the images, some image processing and pattern recognition techniques have been appliedand the results are presented here. We show that even simple algorithms, that can potentially be performed in real time,are capable of differentiating between a metal and a dielectric object concealed under clothing. Additionally, we show thatpre-processing can reveal low temperature contrast features, such as folds in clothing." @default.
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- W2018265654 date "2007-04-27" @default.
- W2018265654 modified "2023-09-27" @default.
- W2018265654 title "Unsupervised image segmentation for passive THz broadband images for concealed weapon detection" @default.
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- W2018265654 doi "https://doi.org/10.1117/12.720004" @default.
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