Segmentation of Concealed Objects in Active Terahertz Images

Terahertz passive and active imaging technology make it possible to detect concealed objects under clothing at a standoff distance. Compared with passive THz imaging scheme, several factors in active THz imaging make concealed objects detection and segmentation more challenging. This paper proposes a two-stage segmentation scheme for active THz images. The first stage is rough segmentation, in which K- means clustering and region analysis are applied. After this, the segmentation result may still not be satisfactory due to the low quality of active THz image. Then, the active shape model (ASM) is introduced in the second fine segmentation stage. Considering the shape prior of the target object, ASM could further improve the segmentation result. Experimental results verified that the proposed scheme can segment the concealed objects in the active terahertz images accurately.