Abstract
Millimeter wave (MMW) readily penetrates fabrics, thus it can be used to detect objects concealed under clothing. A passive MMW imaging system can operate as a stand-off type sensor that scans people both indoors and outdoors. However, because of the diffraction limit and low signal level, the imaging system often suffers from low image quality. Therefore, suitable computational processing would be required for automatic analysis of the images. The authors present statistical and computational algorithms and their implementations for real-time concealed object detection. The histogram of the image is modeled as a Gaussian mixture distribution, and hidden object areas are segmented by a multilevel scheme involving the expectation-maximization algorithm. The complete algorithm has been implemented in both MATLAB and C++. Experimental and simulation results confirm that the implemented system can achieve real-time detection of concealed objects.
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