Abstract
Several techniques to perform static and dynamic load balancing for vision systems are presented. These techniques capture the computational requirements of a task by examining the data when it is produced. They can be applied to many vision systems because many algorithms in different systems are either the same or have similar computational characteristics. These techniques are evaluated by applying them on a parallel implementation of the algorithms in a motion estimation system on a hypercube multiprocessor system. It is shown that the performance gains when these data decomposition and load balancing techniques are used are significant and that the overhead of using these techniques is minimal. >
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