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Proceedings Paper

Pyramidal neurovision architecture for vision machines
Author(s): Madan M. Gupta; George K. Knopf
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Paper Abstract

The vision system employed by an intelligent robot must be active; active in the sense that it must be capable of selectively acquiring the minimal amount of relevant information for a given task. An efficient active vision system architecture that is based loosely upon the parallel-hierarchical (pyramidal) structure of the biological visual pathway is presented in this paper. Although the computational architecture of the proposed pyramidal neuro-vision system is far less sophisticated than the architecture of the biological visual pathway, it does retain some essential features such as the converging multilayered structure of its biological counterpart. In terms of visual information processing, the neuro-vision system is constructed from a hierarchy of several interactive computational levels, whereupon each level contains one or more nonlinear parallel processors. Computationally efficient vision machines can be developed by utilizing both the parallel and serial information processing techniques within the pyramidal computing architecture. A computer simulation of a pyramidal vision system for active scene surveillance is presented.

Paper Details

Date Published: 20 August 1993
PDF: 13 pages
Proc. SPIE 2055, Intelligent Robots and Computer Vision XII: Algorithms and Techniques, (20 August 1993); doi: 10.1117/12.150169
Show Author Affiliations
Madan M. Gupta, Univ. of Saskatchewan (Canada)
George K. Knopf, Univ. of Saskatchewan (Canada)

Published in SPIE Proceedings Vol. 2055:
Intelligent Robots and Computer Vision XII: Algorithms and Techniques
David P. Casasent, Editor(s)

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