  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.
