Automatic Target Detection and Recognition in the Process of Interaction between Visual and Object Buffers of Scene Understanding System based on Network-Symbolic models

摘要

Modern computer vision systems lack human-like abilities to understand the visual scene, detect, and unambiguously identify and recognize objects. Bottom-up grouping can rarely be effective for real world images if applied to the whole image without having clear criteria of how to further combine obtained small distinctive neighbor regions into meaningful objects. ATR systems that are based on the similar principles become dysfunctional if a target doesn't demonstrate remarkably distinctive and contrasting features that allow for unambiguous separation from background and identification. However, human vision unambiguously separates any object from its background and recognizes it, using a rough but wide peripheral system that tracks motions and regions of interests, and narrow but precise foveal vision that analyzes and recognizes the object in the center of a selected region of interest, and visual intelligence that provides scene and object contexts and resolves ambiguity and uncertainty in the visual information. Biologically-inspired Network-Symbolic models convert image information into an "understandable" Network-Symbolic format, which is similar to relational knowledge models. The equivalent of interaction between peripheral and foveal systems in the network-symbolic system is achieved via interaction between Visual and Object Buffers and top-level knowledge system. This interaction provides recursive rough context identification of regions of interest in the visual scene and their analysis in the object buffer for precise and unambiguous separation of the target from clutter with following the recognition of the target.