21 September 2004 Automatic target recognition with image/video understanding systems based on network-symbolic models
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Abstract
In past decades, the solution to ATR problem has been thought of as a solution to the Pattern Recognition problem. The reasons that Pattern Recognition problem has never been solved successfully and reliably for real-world images are more serious than lack of appropriate ideas. Vision is a part of a larger system that converts visual information into knowledge structures. These structures drive the vision process, resolving ambiguity and uncertainty via feedback, and provide image understanding, which is an interpretation of visual information in terms of these knowledge models. Vision mechanisms cannot be completely understood apart from the informational processes related to knowledge and intelligence. A reliable solution to the ATR problem is possible only within the solution of a more generic Image Understanding Problem. Biologically inspired Network-Symbolic representation, where both systematic structural/logical methods and neural/statistical methods are parts of a single mechanism, converts visual information into relational Network-Symbolic structures, avoiding precise computations of 3-D models. Logic of visual scenes can be captured in Network-Symbolic models and used for disambiguation of visual information. Network-Symbolic Transformations make possible invariant recognition of a real-world object as exemplar of a class. This allows for creating ATR systems, reliable in field conditions.
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Gary Kuvich, Gary Kuvich, } "Automatic target recognition with image/video understanding systems based on network-symbolic models", Proc. SPIE 5426, Automatic Target Recognition XIV, (21 September 2004); doi: 10.1117/12.541267; https://doi.org/10.1117/12.541267
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