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24 October 2016 Utilising E-on Vue and Unity 3D scenes to generate synthetic images and videos for visible signature analysis
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This paper investigates the ability to develop synthetic scenes in an image generation tool, E-on Vue, and a gaming engine, Unity 3D, which can be used to generate synthetic imagery of target objects across a variety of conditions in land environments. Developments within these tools and gaming engines have allowed the computer gaming industry to dramatically enhance the realism of the games they develop; however they utilise short cuts to ensure that the games run smoothly in real-time to create an immersive effect. Whilst these short cuts may have an impact upon the realism of the synthetic imagery, they do promise a much more time efficient method of developing imagery of different environmental conditions and to investigate the dynamic aspect of military operations that is currently not evaluated in signature analysis. The results presented investigate how some of the common image metrics used in target acquisition modelling, namely the Δμ1, Δμ2, Δμ3, RSS, and Doyle metrics, perform on the synthetic scenes generated by E-on Vue and Unity 3D compared to real imagery of similar scenes. An exploration of the time required to develop the various aspects of the scene to enhance its realism are included, along with an overview of the difficulties associated with trying to recreate specific locations as a virtual scene. This work is an important start towards utilising virtual worlds for visible signature evaluation, and evaluating how equivalent synthetic imagery is to real photographs.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Christopher S. Madden, Noel J. Richards, and Joanne B. Culpepper "Utilising E-on Vue and Unity 3D scenes to generate synthetic images and videos for visible signature analysis", Proc. SPIE 9997, Target and Background Signatures II, 99970P (24 October 2016);

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