10 February 2009 Principle and design of a dynamic neural network for efficient and accurate recognition of a time-varying object based on its static patterns and its dynamic pattern variations
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Abstract
Based on our research in the last 17 years (with 68 papers published) on the subject of artificial neural network studied from the point of view of N-dimension geometry, a novel neural network system, the dynamic neural network, is proposed here for detecting an unknown moving (or time-varying) object such that the object will not only be detected by its static images, but also by the way it moves if this object follows a constant moving pattern. The system is designed to identify the unknown object by comparing a few time-separated snapshots of the object to a few standard moving objects learned or memorized in the system. The identification is determined by a user entered accuracy control. It could be very accurate, yet still be quite robust and quite fast in identification (e.g., identification in real-time) because of the simplicity of the algorithm. It is different from most other neural network systems because it employs the ND geometrical concept.
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Chialun John Hu, Chialun John Hu, } "Principle and design of a dynamic neural network for efficient and accurate recognition of a time-varying object based on its static patterns and its dynamic pattern variations", Proc. SPIE 7245, Image Processing: Algorithms and Systems VII, 724518 (10 February 2009); doi: 10.1117/12.805483; https://doi.org/10.1117/12.805483
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