Open AccessReal-time concealed-object detection and recognition with passive millimeter wave imaging
Author(s) -
Seokwon Yeom,
Dong Su Lee,
Yushin Jang,
Mun-Kyo Lee,
Sang-Won Jung
Publication year - 2012
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.20.009371
Subject(s) - artificial intelligence , computer vision , computer science , object detection , feature extraction , segmentation , cognitive neuroscience of visual object recognition , pattern recognition (psychology) , image processing , feature (linguistics) , image (mathematics) , linguistics , philosophy
Millimeter wave (MMW) imaging is finding rapid adoption in security applications such as concealed object detection under clothing. A passive MMW imaging system can operate as a stand-off type sensor that scans people in both indoors and outdoors. However, the imaging system often suffers from the diffraction limit and the low signal level. Therefore, suitable intelligent image processing algorithms would be required for automatic detection and recognition of the concealed objects. This paper proposes real-time outdoor concealed-object detection and recognition with a radiometric imaging system. The concealed object region is extracted by the multi-level segmentation. A novel approach is proposed to measure similarity between two binary images. Principal component analysis (PCA) regularizes the shape in terms of translation and rotation. A geometric-based feature vector is composed of shape descriptors, which can achieve scale and orientation-invariant and distortion-tolerant property. Class is decided by minimum Euclidean distance between normalized feature vectors. Experiments confirm that the proposed methods provide fast and reliable recognition of the concealed object carried by a moving human subject.