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<p>Computer vision is regarded as one of the most complex and computationally intensiveproblems. In general, a Computer Vision System (CVS) attempts to relate scene(s) interms of model(s). A typical CVS employs algorithms from a very broad spectrum such as numerical, image processing, graph algorithms, symbolic processing, and artificialintelligence. The authors present a multiprocessor architecture, called "NETRA," forcomputer vision systems. NETRA is a highly flexible architecture. The topology of NETRA is recursively defined, and hence, is easily scalable from small to large systems. It is a hierarchical architecture with a tree-type control hierarchy. Its leaf nodes consists of a cluster of processors connected with a programmable crossbar with selective broadcast capability to provide the desired flexibility. The processors in clusters can operate in SIMD-, MIMD- or Systolic-like modes. Other features of the architecture include integration of limited data-driven computation within a primarily control flow mechanism, block-level control and data flow, decentralization of memory management functions, and hierarchical load balancing and scheduling capabilities. The paper also presents a qualitative evaluation and preliminary performance results of a cluster of NETRA.</p>
Index TermsNETRA; partitionable architecture; computer vision; hierarchical architecture; CVS;multiprocessor architecture; flexible architecture; topology; tree-type control hierarchy;broadcast capability; SIMD; MIMD; Systolic; block-level control; data flow; memorymanagement; load balancing; scheduling; performance; computer vision; parallelarchitectures

A. Choudhary, J. Patel and N. Ahuja, "NETRA: A Hierarchical and Partitionable Architecture for Computer Vision Systems," in IEEE Transactions on Parallel & Distributed Systems, vol. 4, no. , pp. 1092-1104, 1993.
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