Reduction of Computational Complexity in the Image/Video Understanding Systems with Active Vision

摘要

The vision evolved not only as a recognition system, but also as a sensory system for reaching, grasping and other motion activities. In advanced creatures, it has become a component of prediction functions, allowing the creation of environmental models and activity planning. Fast information processing and decision making requires reduction of informational and computational complexities. The brain achieves this goal using symbolic coding, hierarchical compression, and selective processing of visual information. Network-Symbolic representation, where both systematic structural / logical methods and neural / statistical methods are the parts of a single mechanism, is the most feasible for such models. It converts visual information into the relational Network-Symbolic structures, instead of precise computations of 3-dimensional models. Narrow foveal vision provides separation of figure from ground, object identification, semantic analysis, and precise control of actions. Rough wide peripheral vision identifies and tracks salient motion, guiding foveal system to salient objects. It also provides scene context. Objects and other stable systems have coherent relational structures. Network-Symbolic transformations derive more abstract structures that allow invariably recognize a particular structure as an exemplar of class. Robotic systems, equipped with such smart vision, will be able to navigate in any environment, understand situation, and act accordingly.