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Proceedings Paper

Automatic target detection and identification with a scene understanding system based on network-symbolic models
Author(s): Gary Kuvich
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Paper Abstract

A new generation of target recognition systems must be based on the principles of image understanding and active vision. The implementation of both principles is possible in the form of Network-Symbolic systems. Instead of precise computations of 3-dimensional models a network-symbolic system converts image information into an "understandable" Network-Symbolic format, which is similar to relational knowledge models. An Image/Video Analysis that is based on Network-Symbolic models differs from the traditional linear bottom-up "segmentation-grouping-learning-recognition" approach. It is a combination of recursive hierarchical bottom-up and top-down processes. Logic of visual scenes can be captured in the Network-Symbolic models and used for the disambiguation of visual information, including target detection and identification. View-based object recognition is a hard problem for traditional algorithms that directly match a primary view of an object to a model. In Network-Symbolic Models, the derived structure and not the primary view is a subject for recognition. Such recognition is not affected by local changes and appearances of the object from a set of similar views. Network-Symbolic systems can be treated as a new type of Multi-Agent systems that can better interpret visual information for automatic target detection and identification required by a new generation of smart defense systems.

Paper Details

Date Published: 19 May 2005
PDF: 14 pages
Proc. SPIE 5807, Automatic Target Recognition XV, (19 May 2005); doi: 10.1117/12.603026
Show Author Affiliations
Gary Kuvich, Smart Computer Vision Systems (United States)

Published in SPIE Proceedings Vol. 5807:
Automatic Target Recognition XV
Firooz A. Sadjadi, Editor(s)

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