An Iterative Method Combining Fuzzy Fusion and Fisher Vectors for Concealed Object Detection in Passive Millimeter-Wave Imaging

Abstract

Passive millimeter-wave (PMMW) imaging technology holds potential in security checks by revealing the brightness temperature (BT) difference between concealed objects and the human body. However, existing region-based methods for detecting objects in PMMW multipolarization imaging suffer from the poor performance of regional segmentation. To address the concerns mentioned, this article presents an iterative method combining fuzzy fusion and Fisher vectors, named ICFFFV. First, the multipolarization membership degree vectors from contrast images of low BT (CILBTs) are analyzed. This leads to a multipolarization fuzzy fusion that constructs potential target regions. Next, a new index searching-assisted re-segmentation strategy is introduced from the analyses of inaccurate segmentation and superpixels near the body edge. It mitigates the impact of inaccurate object segmentation and superpixel interference near the body edge. Finally, the subregional contrast of the Fisher vector (SCFV) image is combined with the fuzzy fusion image. Following a feedback-based iterative manner, it protects the edge pixels of objects while suppressing clutter pixels. The experiments validate the enhancement performance and the effectiveness of improving detection performance at pixel and region levels.