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Proceedings Paper

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. The traditional linear bottom-up “segmentation-grouping-learning-recognition” approach cannot provide a reliable separation of a target from its background/clutter, while human vision unambiguously solves this problem. The nature of informational processes in the visual system does not allow separating from the informational processes in the top-level knowledge system. An Image/Video Analysis that is based on Network-Symbolic approach 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 reliable 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.

Paper Details

Date Published: 25 May 2005
PDF: 14 pages
Proc. SPIE 5811, Targets and Backgrounds XI: Characterization and Representation, (25 May 2005); doi: 10.1117/12.603024
Show Author Affiliations
Gary Kuvich, Smart Computer Vision Systems (United States)

Published in SPIE Proceedings Vol. 5811:
Targets and Backgrounds XI: Characterization and Representation
Wendell R. Watkins; Dieter Clement; William R. Reynolds, Editor(s)

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