Danny Gibbins

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In this paper we present a methodical evaluation of the performance of a new and two traditional approaches to automatic target recognition (ATR) based on silhouette representation of objects. Performance is evaluated under the simulated conditions of imperfect localization by a region of interest (ROI) algorithm (resulting in clipping and scale changes) as(More)
— Traditionally, Inverse Synthetic Aperture Radar (ISAR) image frames are classified individually in an automatic target recognition system. When information from different image frames is combined, it is usually in the context of time-averaging to remove statistically independent noise fluctuations between images. The sea state induced variability of the(More)
• Researching feature learning using deep networks for face verification, and have achieved a 2% improvement over the state-of-the-art verification rate on the unconstrained YouTube dataset • Proposed a deep feature learning algorithm based on sparse coding for objection recognition from different domains, and achieved more than 10% improvement over the(More)
In this paper, we present a method for extracting salient local features from 3D models using surface curvature which has application to 3D object recognition. In the developed technique, the amount of curvature at a point is specified by a positive number known as the curvedness. This value is invariant to rotation as well as translation. A local(More)