25 May 2005 Target detection and identification with a scene understanding system based on network-symbolic models
Author Affiliations +
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.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gary Kuvich, "Target detection and identification with a scene understanding system based on network-symbolic models", Proc. SPIE 5811, Targets and Backgrounds XI: Characterization and Representation, (25 May 2005); doi: 10.1117/12.603024; https://doi.org/10.1117/12.603024
PROCEEDINGS
14 PAGES


SHARE
Back to Top