Concealed object recognition based on geometric feature descriptors

Seokwon Yeom; Dong-Su Lee; YuShin Chang; Mun-Kyo Lee; Sang-Won Jung

doi:10.1117/12.919021

8 May 2012 Concealed object recognition based on geometric feature descriptors

Seokwon Yeom, Dong-Su Lee, YuShin Chang, Mun-Kyo Lee, Sang-Won Jung

Proceedings Volume 8362, Passive and Active Millimeter-Wave Imaging XV; 83620H (2012) https://doi.org/10.1117/12.919021
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

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. This paper discusses real-time concealed object recognition based on geometric descriptors. The concealed object region is extracted by the multi-level segmentation method. A novel approach is proposed to measure similarity between a true object model and segmented binary objects. Principal component analysis (PCA) regularizes the shape in terms of translation and rotation. Size normalization provides scale-invariant property. A geometric feature vector is composed of several shape descriptors. The feature vector is invariant to scale, rotation, and translation, and tolerant to distortion. Classification is performed by means of measuring Euclidean distance between the mean feature vector of training models and the feature vector of the segmented object. Experiments confirm that the proposed method provides fast and reliable recognition of the concealed object carried by a moving human subject.

Citation Download Citation

Seokwon Yeom, Dong-Su Lee, YuShin Chang, Mun-Kyo Lee, and Sang-Won Jung "Concealed object recognition based on geometric feature descriptors", Proc. SPIE 8362, Passive and Active Millimeter-Wave Imaging XV, 83620H (8 May 2012); https://doi.org/10.1117/12.919021

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KEYWORDS

Image segmentation

Principal component analysis

Extremely high frequency

Imaging systems

Object recognition

Feature extraction

Metals

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