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Proceedings Paper

Application of scene understanding to representative military imagery
Author(s): Natalie Dyer
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Paper Abstract

Scene understanding (SU) is a high priority in many areas. Currently many SU algorithms are developed using imagery which is often captured in constant, well lit environments with low clutter and not affected by noise, compression or bandwidth artefacts. The initial research addressed how SU can assist automatic identification, semantic tagging and tracking of an object in a scene. However, it became apparent that current algorithms and software are unable to successfully process typical military imagery. Consequently, research was undertaken to assess how well current SU algorithms process imagery captured by a variety of typical military imagers. The imagery was chosen such that it covered a variety of scenarios and applied to a range of algorithms. It became apparent that the many algorithms experienced difficulties in processing the typical military imagery or had other drawbacks such as computational cost, which impacts on military utility. The National Imagery Interpretability Rating Scale (NIIRS) was used in order to help explain the general quality of military imagery and the analysis tasks which are expected to be carried out on it.

Paper Details

Date Published: 12 October 2010
PDF: 11 pages
Proc. SPIE 7838, Optics and Photonics for Counterterrorism and Crime Fighting VI and Optical Materials in Defence Systems Technology VII, 78380L (12 October 2010); doi: 10.1117/12.864953
Show Author Affiliations
Natalie Dyer, Defence Science and Technology Lab. (United Kingdom)


Published in SPIE Proceedings Vol. 7838:
Optics and Photonics for Counterterrorism and Crime Fighting VI and Optical Materials in Defence Systems Technology VII
Colin Lewis; Roberto Zamboni; François Kajzar; Doug Burgess; Emily M. Heckman, Editor(s)

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