In this paper, a decision support system for ship identification is presented. The system receives as input a silhouette of the vessel to be identified, previously extracted from a side view of the object. This view could have been acquired with imaging sensors operating at different spectral ranges (CCD, FLIR, image intensifier). The input silhouette is preprocessed and compared to those stored in a database, retrieving a small number of potential matches ranked by their similarity to the target silhouette. This set of potential matches is presented to the system operator, who makes the final ship identification. This system makes use of an evolved version of the Curvature Scale Space (CSS) representation. In the proposed approach, it is curvature extrema, instead of zero crossings, that are tracked during silhouette evolution, hence improving robustness and enabling to cope successfully with cases where the standard CCS representation is found to be unstable. Also, the use of local curvature was replaced with the more robust concept of lobe concavity, with significant additional gains in performance. Experimental results on actual operational imagery prove the excellent performance and robustness of the developed method.
One of the main advantages of mm-wave radiation is its capability to "see through" clothing and other materials, therefore enabling the detection of metallic and non-metallic weapons and explosives. Mm-wave imagers are able to perform stand-off screening, providing a solution in checkpoints and compound gates where the chance of suicide bomber attacks is high. A study has been made simulating real case scenarios to test practical detection performance and stand-off distances at a range of frequencies in this band, the results of which are presented.
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