20 June 2013 Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine
Author Affiliations +
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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James Graham, James Graham, Igor V. Ternovskiy, Igor V. Ternovskiy, } "Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine", Proc. SPIE 8757, Cyber Sensing 2013, 87570J (20 June 2013); doi: 10.1117/12.2018833; https://doi.org/10.1117/12.2018833
PROCEEDINGS
10 PAGES


SHARE
RELATED CONTENT

Self-structuring data learning approach
Proceedings of SPIE (June 22 2016)
PerSEval phase I development of a 3D urban terrain...
Proceedings of SPIE (April 30 2010)
Digital watermarking of 3D meshes
Proceedings of SPIE (January 28 2004)
Model-Derived Multisensor Target Discrimination
Proceedings of SPIE (January 05 1989)

Back to Top