A Real Time Image Fusion based Framework for Concealed Weapon Detection

In this paper, a well-organized hidden weapon detection (CWD) algorithm based on image fusion is presented. First, the images obtained consumingdissimilar sensors are decomposed into low and high occurrence bands with the double-density dualtree compound wavelet transform (DDDTCWT). Then two novel decision methods are introduced referring to the appearances of the frequency bands, which meaningfully improves the image fusion performance. The fusion of low frequency bands coefficients is strong-minded by the local contrast, while the high occurrence band fusion rule is developed by considering both the texture feature of HVS and the local energy basis. Finally, the fused image is attained through the inverse DDDTCWT. Experiments and comparisons establish the robustness and efficiency of the proposed approach and indicate that the fusion rules can be applied to different multi-scale transforms. Also, our work shows that the mixture result using the proposed fusion rules on DDDTCWT is superior to other mixtures as well as previously proposed approaches

Multisensor Concealed Weapon Detection Using the Image Fusion Approach

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