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Proceedings Paper

Linear programming solutions to problems in logical inference and space-variant image restoration
Author(s): Ramji V. Digumarthi; Paul Max Payton; Eamon B. Barrett
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Paper Abstract

Image understanding is a cross-disciplinary field, drawing on concepts and algorithms from image processing, pattern recognition, and artificial intelligence. An integrated system for image understanding may require a variety of capabilities that appear quite disparate, such as image restoration to compensate for degradations detected in the data, followed by logical inference to interpret features extracted from the restored data. The authors establish that constrained optimization provides a uniform formulation for two such apparently disparate problems: restoration of blurred imagery, and logical deduction or mechanized inference. Formulation of these problems in each of these categories as linear programming (LP) problems is shown. The 'deblurred' image is regained by minimizing a linear objective function subject to the constraints imposed by the blur. The degree of truth or falsity of a consequent proposition is established by maximizing a linear objective function subject to the constraints imposed by the premises.

Paper Details

Date Published: 1 August 1991
PDF: 11 pages
Proc. SPIE 1472, Image Understanding and the Man-Machine Interface III, (1 August 1991); doi: 10.1117/12.46478
Show Author Affiliations
Ramji V. Digumarthi, Lockheed Palo Alto Research Lab. (United States)
Paul Max Payton, Lockheed Missiles & Space Co., Inc. (United States)
Eamon B. Barrett, Lockheed Missiles & Space Co., Inc. (United States)


Published in SPIE Proceedings Vol. 1472:
Image Understanding and the Man-Machine Interface III
Eamon B. Barrett; James J. Pearson, Editor(s)

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