Computer vision has been regarded as one of the most complex and computationally intensive problems. An integrated vision system (lVS) is a system that uses vision algorithms from all levels of processing to perform for a high level application (e.g, object recognition). An IVS normally involves algorithms from low level. intermediate level and high level vision. Designing parallel architectures for vision systems has been of a tremendous interest to researchers. This thesis addresses several

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... sues 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. It is concluded that an architecture for IVS must be reconfigurable into different modes, be partitionable, allow dynamic resource allocation and task scheduling, provide flexible and fast communication between processing elements, provide efficient I/O and be fault-tolerant. A multiprocessor architecture for IVSs (called NETRA) is presented. NETRA is highly flexible without the use of complex interconnection schemes. NETRA is recursively defined hierarchical architecture whose leaf nodes consist of clusters processors connected with a programmable crossbar with a selective broadcast capability. Hence, it is easily scalable from small to large systems. Homogeneity of NETRA permits fault tolerance and graceful degradation under faults. Several refinements in the architecture over the original design are also proposed. ACKNOWLEDGMENTS