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Proceedings Paper

Artificial Intelligence In Image Processing
Author(s): John F. Gilmore
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Paper Abstract

Image processing technology concentrates on the development of data extraction techniques applied toward the statistical classification of visual imagery. In classical image processing systems, an image is [1] preprocessed to remove noise, [2] segmented to produce close object boundaries, [3] analyzed to extract a representative feature vector, and [4] compared to ideal object feature vectors by a classifier to determine the nearest object classification and its associated confidence level. This type of processing attempts to formulate a two-dimensional interpretation of three-dimensional scenes using local statistical analysis, an entirely numerical process. Symbolic information dealing with contextual relationships, object attributes, and physical constraints is ignored in such an approach. This paper describes a number of artificial intelligence techniques which allow symbolic information to be exploited in conjunction with numerical data to improve object classification performance.

Paper Details

Date Published: 22 July 1985
PDF: 10 pages
Proc. SPIE 0528, Digital Image Processing, (22 July 1985); doi: 10.1117/12.946419
Show Author Affiliations
John F. Gilmore, Georgia Tech Research Institute (United States)

Published in SPIE Proceedings Vol. 0528:
Digital Image Processing
Andrew G. Tescher, Editor(s)

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