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Proceedings Paper

Modular Real�Time Image Processing Hardware As A Means To Offload Computationally Intensive Tasks In Artificial Vision
Author(s): Robert J. Berger; Barry Unger
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Paper Abstract

Transforming large quantities of information from the video signal to more symbolic representations is the first step in any pattern recognition or machine vision application. Traditionally this step is so computationally intensive, that most researchers have had to limit themselves to simple algorithms that lacked flexibility and generality or to non-real-time problems. The other choice has been to skip the higher level AI or judgemental techniques and use all the computational bandwidth for signal processing and very limited problem domains. This paper describes a family of real-time modular hardware, "MaxVideo" that allows the user to cost-efficiently configure an image-processing "front end" that can handle most of the tasks associated with the first steps in machine vision applications, especially those that involve parallel calculations such as convolutions, rotations, erosion / dilation, and feature list extractions. This front end image processor is viewed as transforming the raw video data into the more digestible symbolic "raw primal sketch" of coherent lines and geometric elements. This offloads the more general purpose host, allowing it to effectively handle the higher level knowledge representation / manipulation tasks. As higher level vision techniques are understood, the architecture described allows them to be integrated into hardware.

Paper Details

Date Published: 26 March 1986
PDF: 11 pages
Proc. SPIE 0635, Applications of Artificial Intelligence III, (26 March 1986); doi: 10.1117/12.964189
Show Author Affiliations
Robert J. Berger, Datacube Inc. (United States)
Barry Unger, Datacube Inc. (United States)

Published in SPIE Proceedings Vol. 0635:
Applications of Artificial Intelligence III
John F. Gilmore, Editor(s)

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