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Proceedings Paper

Utilizing Context In Computer Vision By Confidence Modification
Author(s): Advait Mogre; Robert McLaren; James Keller
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Paper Abstract

The analysis of images derived from a "real" scene often involves applying a rule-based structure leading to object detection, recognition and object relationships. A rule consists of a set of conditions whose satisfaction triggers the rule. These conditions are based on domain knowledge of image characteristics, inferred knowledge, and scene context. Firing a rule leads to conclusions with assigned confidences. Frequently, additional scene context knowledge can have a significant effect on the final conclusion but is excluded because its form varies from scene to scene, and its unavailability in the main rule base would prevent rule application. This paper utilizes context by altering confidences associated with conclusions of specific rules. Uncertainty computations for conclusions of rules to which the contextual information applies would be effected. For each rule, Rj, of the main rule structure, a context factor Cj with values over [-1,1], is defined. If Cj = 0, no relevant context information is present; if C≥0, there is a degree of support; if C≤j0, a conflict with the conclusion is implied. If P(THEN,j) represents the certainty in the conclusion of rule Rj, then modify this as P'(THEN,j)'[P(THEN,j), Cj], where f' is selected to satisfy conditions imposed on the confidence and context information.

Paper Details

Date Published: 27 March 1989
PDF: 10 pages
Proc. SPIE 1002, Intelligent Robots and Computer Vision VII, (27 March 1989); doi: 10.1117/12.960299
Show Author Affiliations
Advait Mogre, University of Missouri-Columbia (United States)
Robert McLaren, University of Missouri-Columbia (United States)
James Keller, University of Missouri-Columbia (United States)

Published in SPIE Proceedings Vol. 1002:
Intelligent Robots and Computer Vision VII
David P. Casasent, Editor(s)

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