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Proceedings Paper

Adaptive Nov Elty Filtering For Machine Vision
Author(s): Richard A. Messner; Joseph G. Bailey; Harold H. Szu
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Paper Abstract

The development of an autonomous mobile platform vision system that can adapt to a variety of surroundings by modifying its current memory is an ambitious goal. We believe that to achieve such an ambitious goal it is necessary to look at areas that may seem unconventional to some researchers. Such an area is associative memory. For an autonomous robotic vision system to function adaptively it must be able to respond to a wide variety of visual stimuli, sort out what is new or different from previously stored information, and update its memory taking this new information into account. To compound the problem, this procedure should be invariant to the scale of objects within the scene and to some degree rotations as well. With this in mind we can identify two main functions that are desirable in such a visual system: 1) the ability to identify novel items within a scene; and 2) the ability to adaptively update the system memory. The need for these functions has led to the investigation of a class of filters called Novelty Filters. By use of a coordinate transformation it is possible to specify novelty filters that are invariant to scale and rotational changes. Further, it is then possible to postulate an adaptive memory equation which reflects the adaptive novelty filter for a multiple-channel pattern recognition system. This paper, while not all inclusive, is meant to stimulate further interest as well as report preliminary simulation and mathematical results.

Paper Details

Date Published: 19 February 1988
PDF: 8 pages
Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988);
Show Author Affiliations
Richard A. Messner, University of New Hampshire (United States)
Joseph G. Bailey, University of New Hampshire (United States)
Harold H. Szu, Naval Research Laboratory (United States)

Published in SPIE Proceedings Vol. 0848:
Intelligent Robots and Computer Vision VI
David P. Casasent; Ernest L. Hall, Editor(s)

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