Smart sensing within a pyramid vision machine
Article Abstract:
A pyramid-architecture parallel processing vision system is developed that uses early selective analysis and fast algorithms for 'smart sensing' of data to reduce computational costs while offering high performance. System design elements include a hierarchical image representation data structure, two classes of fast analysis algorithms, fine-to-coarse ones for feature generation and coarse-to-fine ones for searching, and a high-level control system for data gathering. The Pyramid Vision Machine developed at the David Sarnoff Research Center implements the described specifications in a pipelined architecture of three 512 by 512 memory modules and two clustered processing modules. The system can construct full Gaussian and Laplacian pyramids for 256 by 240 images frames 30 times each second. Operation of pyramid vision systems and the PVM are described.
Publication Name: Proceedings of the IEEE
Subject: Electronics
ISSN: 0018-9219
Year: 1988
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Neural aspects of vision and related technological advances
Article Abstract:
The development of more efficient and natural artificial visual systems may be predicated on the functioning of animal and human systems, but the actual implementation of artificial systems will be quite different from organic ones. Several models of the physiological functioning of human and animal visual systems are described and compared. Such issues as the visual cortex, conversion of optical stimuli to signals resolvable by the visual cortex, and feature extraction are discussed. Experimental data from such models are useful in the modeling of digital neural networks, and a vertebrate retina simulation is used to develop such a neural network. A very-large-scale integration (VLSI) neutral network--neuromime--on a chip is being developed that incorporates mathematical neuronal models and all possible physiological and behavioral factors.
Publication Name: Proceedings of the IEEE
Subject: Electronics
ISSN: 0018-9219
Year: 1988
User Contributions:
Comment about this article or add new information about this topic:
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