Vector Clustering of Passive Millimeter Wave Images with Linear Polarization for Concealed Object Detection

Passive millimeter (MMW) imaging can penetrate clothing to create interpretable imagery of concealed objects. However, the image quality is often restricted by low signal to noise ratio and temperature contrast as well as low spatial resolution. In this paper, we explore a four-channel passive MMW imaging system operating in the 8 and 3 mm wavelength regimes with linear vertical and horizontal polarization directions. Both registration between different channel images and segmentation of concealed objects are addressed. Multi-channel image registration is performed by geometric feature matching and affine transform, and then multi-level segmentation separates the human body region from the background, and concealed objects from the body region, sequentially. In the experiments, several metallic and non-metallic objects concealed under clothing are captured in indoors. It will be shown that our method can separate objects with higher accuracy than the conventional method.