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. 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.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. The effects of communication interference on the performance of algorithms are studied. It is observed that if communication speeds are matched with the computation speeds, almost linear speedups are possible when algorithms are mapped across clusters.Finally, several techniques to perform data decomposition, and static and dynamic load balancing for IVS algorithms are described. These techniques can be used to perform load balancing for intermediate and high level, data dependent vision algorithms. They are shown to perform well, using them on an implementation of a motion estimation system on a hypercube multiprocessor. (Abstract shortened with permission of author.)U of I OnlyETDs are only available to UIUC Users without author permissio