Machine Perception using a Blackboard Architecture

Abstract

Here we present ongoing research in the application of symbolic reasoning to perception in general and vision in particular. Perception is treated as the combination of the possibly contradictory outputs of many specialized processes which communicate via a blackboard data structure. It is demonstrated that our design allows for bottom-up, horizontal and top-down information flow. Significant progress towards the analysis of unstructured scenes has been made. The principles involved have been explored experimentally and preliminary results are presented.