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Proceedings Paper

Maximum-Likelihood Image Classification
Author(s): Miles N. Wernick; G. Michael Morris
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Paper Abstract

An essential feature of a practical automatic image recognition system is the ability to tolerate certain types of variations within images. The recognition of images subject to intrinsic variations can be treated as a sorting task in which an image is identified as a member of some class of images. Herein, the maximum-likelihood strategy, an important tool in the field of statistical decision theory, is applied to the image classification problem. We show that the strategy can be implemented in a standard image correlation system and that excellent classification results can be obtained.

Paper Details

Date Published: 22 August 1988
PDF: 5 pages
Proc. SPIE 0938, Digital and Optical Shape Representation and Pattern Recognition, (22 August 1988); doi: 10.1117/12.976607
Show Author Affiliations
Miles N. Wernick, University of Rochester (United States)
G. Michael Morris, University of Rochester (United States)

Published in SPIE Proceedings Vol. 0938:
Digital and Optical Shape Representation and Pattern Recognition
Richard D. Juday, Editor(s)

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