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Proceedings Paper

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
Author(s): Chialun John Hu
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Paper 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.

Paper Details

Date Published: 10 February 2009
PDF: 9 pages
Proc. SPIE 7245, Image Processing: Algorithms and Systems VII, 724518 (10 February 2009); doi: 10.1117/12.805483
Show Author Affiliations
Chialun John Hu, Univ. of Colorado at Boulder (United States)

Published in SPIE Proceedings Vol. 7245:
Image Processing: Algorithms and Systems VII
Nasser M. Nasrabadi; Jaakko T. Astola; Karen O. Egiazarian; Syed A. Rizvi, Editor(s)

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