@article{ref1,
title="Concealed contraband recognition by integrating improved fuzzy clustering with moment invariants",
journal="Journal of information and computational science",
year="2012",
author="Hu, Taiyang and Xiao, Zelong and Xu, Jianzhong",
volume="9",
number="2",
pages="451-459",
abstract="Concealed contraband detection has been a growing concern due to the emerging terrorism and violence crimes throughout the world. Millimeter-wave (MMW) radiometric imaging plays an important role in concealed contraband detection, but the speed and accuracy of recognizing contraband from the acquired MMW radiometric images are usually restricted to the deficiencies in traditional method of artificial interpretation. For automatic recognition of concealed contraband, an algorithm integrating improved Fuzzy C-means (FCM) clustering with moment invariants is proposed in this paper. Experimental results demonstrate that the proposed algorithm can recognize concealed contraband rapidly and accurately, so it can be applied in the area of concealed contraband detection. Copyright  2012 Binary Information Press.<p />",
language="",
issn="1548-7741",
doi="",
url="http://dx.doi.org/"
}