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Multisensor Concealed Weapon Detecti...

University of Windsor (Canada).

Multisensor Concealed Weapon Detection Using the Image Fusion Approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Multisensor Concealed Weapon Detection Using the Image Fusion Approach.
作者:	Xu, Tuzhi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2016
面頁冊數:	84 p.
附註:	Source: Masters Abstracts International, Volume: 55-05.
附註:	Adviser: Jonathan Wu.
Contained By:	Masters Abstracts International55-05(E).
標題:	Electrical engineering.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10125917
ISBN:	9781339839752

Multisensor Concealed Weapon Detection Using the Image Fusion Approach.
Xu, Tuzhi.

Multisensor Concealed Weapon Detection Using the Image Fusion Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2016 - 84 p.

Source: Masters Abstracts International, Volume: 55-05.

Thesis (M.A.Sc.)--University of Windsor (Canada), 2016.

Detection of concealed weapons is an increasingly important problem for both military and police since global terrorism and crime have grown as threats over the years. This work presents two image fusion algorithms, one at pixel level and another at feature level, for efficient concealed weapon detection application. Both the algorithms presented in this work are based on the double-density dual-tree complex wavelet transform (DDDTCWT). In the pixel level fusion scheme, the fusion of low frequency band coefficients is determined by the local contrast, while the high frequency band fusion rule is developed with consideration of both texture feature of the human visual system (HVS) and local energy basis. In the feature level fusion algorithm, features are exacted using Gaussian Mixture model (GMM) based multiscale segmentation approach and the fusion rules are developed based on region activity measurement. Experiment results demonstrate the robustness and efficiency of the proposed algorithms.

ISBN: 9781339839752Subjects--Topical Terms:

454503
Electrical engineering.

Multisensor Concealed Weapon Detection Using the Image Fusion Approach.
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