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Proceedings Paper

Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine
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Paper Abstract

We applied a two stage unsupervised hierarchical learning system to model complex dynamic surveillance and cyber space monitoring systems using a non-commercial version of the NeoAxis visualization software. The hierarchical scene learning and recognition approach is based on hierarchical expectation maximization, and was linked to a 3D graphics engine for validation of learning and classification results and understanding the human – autonomous system relationship. Scene recognition is performed by taking synthetically generated data and feeding it to a dynamic logic algorithm. The algorithm performs hierarchical recognition of the scene by first examining the features of the objects to determine which objects are present, and then determines the scene based on the objects present. This paper presents a framework within which low level data linked to higher-level visualization can provide support to a human operator and be evaluated in a detailed and systematic way.

Paper Details

Date Published: 20 June 2013
PDF: 10 pages
Proc. SPIE 8757, Cyber Sensing 2013, 87570J (20 June 2013); doi: 10.1117/12.2018833
Show Author Affiliations
James Graham, Air Force Research Lab. (United States)
Ohio Univ. (United States)
Igor V. Ternovskiy, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 8757:
Cyber Sensing 2013
Igor V. Ternovskiy; Peter Chin, Editor(s)

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