Hierarchical Structures, Parallelism And Planning In Analyzing Time-Varying Images

A paradigm to reduce computational costs in analyzing time-varying images is proposed in this paper. Our model is a hybrid of three recent advances in computer science, namely, hierarchical data structures, parallel processing, and heuristic planning. A pipelined pyramid image structure is constructed in the model by continually converging incoming images into successively lower resolutions. The model also contains a set of processors which work concurrently and asynchronously on subimages at different levels of this pyramid structure. These processors initially watch for interesting features in the lowest resolution rendition, of the scene. Processors working on promising areas individually but coopeiatively proceed to progressively higher resolution levels according to a planning scheme. This distributed planning mechanism is afforded through a blackboard control structure which also permits a unified scene interpretation. The model has been implemented in a simulated distributed system.