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Proceedings Paper

A Model Driven System for Contextual Scene Analysis
Author(s): John F. Gilmore; Andrew J. Spiessbach
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Paper Abstract

Existing strategies for the identification of objects in a scene are based upon classical pattern recognition approaches. The basic concept involved centers around the extraction of a set of statistical features for each object detected in a scene, followed by the application of a classifier which attempts to derive the decision boundaries that separate these objects into classes. As statistical features are quite sensitive to noise, this approach has led to problems due to the inability of classifiers to identify accurate feature set separation in less than ideal conditions. A global approach utilizing the contextual information in a scene currently discarded offers the most promise in overcoming the short-comings of current object classification methods.

Paper Details

Date Published: 9 January 1984
PDF: 7 pages
Proc. SPIE 0432, Applications of Digital Image Processing VI, (9 January 1984); doi: 10.1117/12.936676
Show Author Affiliations
John F. Gilmore, Georgia Tech Engineering Experiment Station (United States)
Andrew J. Spiessbach, Martin Marietta Orlando Aerospace (United States)

Published in SPIE Proceedings Vol. 0432:
Applications of Digital Image Processing VI
Andrew G. Tescher, Editor(s)

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