Parallel architectures and parallel algorithms for integrated vision systems
Computer vision has been regarded as one of the most complex and computationally intensive problems. An integrated vision system (IVS) is a system that uses vision algorithms from all levels of processing to perform for a high level application (e.g, object recognition). This thesis addresses several issues in parallel architectures and parallel algorithms for integrated vision systems. First, a model of computation for IVSs is presented. The model captures computational requirements, defines spatial and temporal data dependencies between tasks, and shows what types of interactions may occur between tasks from different levels of processing. The model is used to develop features and capabilities of a parallel architecture suitable for IVSs. A multiprocessor architecture for IVSs (called NETRA) is presented. Performance of several vision algorithms when they are mapped on one cluster is presented. It is shown that SIMD, MIMD and systolic algorithms can be easily mapped onto processor clusters, and almost linear speedups are possible. An extensive analysis of inter-cluster communication strategies in NETRA is presented. A methodology to evaluate performance of algorithms on NETRA is described. Performance analysis of parallel algorithms when mapped across clusters is presented. The parameters are derived from the characteristics of the parallel algorithms, which are then, used to evaluate the alternative communication strategies in NETRA. Finally, several techniques to perform data decomposition, and static and dynamic load balancing for IVS algorithms are described.
- Research Organization:
- Illinois Univ., Urbana, IL (United States)
- OSTI ID:
- 5533973
- Country of Publication:
- United States
- Language:
- English
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