This paper describes a bio-inspired Visual Attention and Object Recognition System (VARS) that can (1) learn
representations of objects that are invariant to scale, position and orientation; and (2) recognize and locate these objects
in static and video imagery. The system uses modularized bio-inspired algorithms/techniques that can be applied
towards finding salient objects in a scene, recognizing those objects, and prompting the user for additional information to
facilitate interactive learning. These algorithms are based on models of human visual attention, search, recognition and
learning. The implementation is highly modular, and the modules can be used as a complete system or independently.
The underlying technologies were carefully researched in order to ensure they were robust, fast, and could be integrated
into an interactive system. We evaluated our system's capabilities on the Caltech-101 and COIL-100 datasets, which are
commonly used in machine vision, as well as on simulated scenes. Preliminary results are quite promising in that our
system is able to process these datasets with good accuracy and low computational times.