20 June 2013 Complex scenes and situations visualization in hierarchical learning algorithm with dynamic 3D NeoAxis engine
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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


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