Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science LNAI Series Editors

Abstract

Parallelization of image analysis tasks forms a basic key for processing huge image data in realtime. At this, suitable subtasks for parallel processing have to be extracted and mapped to components of a distributed system. Basically, this task should be done by the processing system and not by the user, as automatical parallelization allows a exible resource management and reduces time for developing image analysis programs. This paper describes a multi-agent based system for planning and performing image analysis tasks within a distributed system. It illustrates a method for modeling image analysis tasks under the viewpoint of parallel processing and explains the special design requirements for parallelizing agents. Furthermore, we describe concepts for agent cooperation and for using the agent's ability of learning to allow long term improvement of its planning and scheduling strategies. The presented image analysis system allows an architecture-independent parallel processing of image analysis tasks with an optimized resource management.