Abstract-Computer vision is regarded as one of the most complex and computationally intensive problems. In general, a Computer Vision System (CVS) attempts to relate scene(s) in terms of model(s). A typical CVS employs algorithms from a very broad spectrum such as such as numerical, image processing, graph algorithms, symbolic processing, and artificial intelligence. This paper presents a multiprocessor architecture+ called "NE-TRA," for computer vision systems. NETFLA 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, blocklevel control and data flow, decentralization of memory management functions, and hierarchical load balancing and scheduling capabilities. This paper also presents a qualitative evaluation and preliminary performance results of a cluster of NETRA.
NETRA REF was a parallel architecture for computer vision.
NETRA: A hierarchical and partitionable architecture for computer vision systems
IEEE Trans. on Parallel and Distributed Systems
Computer Science