Scene understanding based on network-symbolic models
Gary Kuvich · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
New generations of smart weapons and unmanned vehicles must have reliable perceptual systems that are similar to human vision. 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. Logic of visual scenes can be captured in the Network-Symbolic models and used for the disambiguation of visual information. It is hard to use geometric operations for processing of natural images. Instead, the brain builds a relational network-symbolic structure of visual scene, using different clues to set up the relational order of surfaces and objects. Feature, symbol, and predicate are equivalent in the biologically inspired Network-Symbolic systems. A linking mechanism binds these features/symbols into coherent structures, and image converts from a “raster” into a “vector” representation that can be better interpreted by higher-level knowledge structures. 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, not the primary view, is a subject for recognition. Such recognition is not affected by local changes and appearances of the object as seen from a set of similar views.