This work presents the application of a basic unsupervised classification algorithm for the segmentation of indoor passive
Terahertz images. The 30,000 pixel broadband images of a person with concealed weapons under clothing are taken
at a range of 0.8-2m over a frequency range of 0.1-1.2THz using single-pixel row-based raster scanning. The spiral-antenna
coupled 36x1x0.02&mgr;m Nb bridge cryogenic micro-bolometers are developed at NIST-Optoelectronics Division.
The antenna is evaporated on a 250&mgr;m thick Si substrate with a 4mm diameter hyper-hemispherical Si lens. The NETD
of the microbolometer is 125mK at an integration time of 30 ms. The background temperature calibration is performed
with a known 25 pixel source above 330 K, and a measured background fluctuation of 200-500mK. Several weapons
were concealed under different fabrics: cotton, polyester, windblocker jacket and thermal sweater. Measured temperature
contrasts ranged from 0.5-1K for wrinkles in clothing to 5K for a zipper and 8K for the concealed weapon. In order to
automate feature detection in the images, some image processing and pattern recognition techniques have been applied
and 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 that
pre-processing can reveal low temperature contrast features, such as folds in clothing.