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Proceedings Paper

Using constraints to incorporate domain knowledge
Author(s): Peter Eggleston
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Paper Abstract

Constraints are mathematical mapping functions which transform from an attribute or feature space onto a score or measure of plausibility. The term plausible is used because this paper assumes one is looking to support a hypothesis rather than refute it. In this paper, a system is described which allows the algorithm developer to easily incorporate domain knowledge into an interpretation process through the graphical creation and editing of constraints. These constraints can be applied to multiple sets of data through the use of application programs. Groupings or spatial relationships such as collinearity or nearness are also attributes which may be constrained in an attempt to interpret image data. Model matches may likewise be written as constraint mappings. Primitive constraints may be combined to form compound constraints, and differing compounding weights may be assigned to primitive constraints. If these weights are written as functions dependent upon other information, the a system developed with this process can be made adaptive.

Paper Details

Date Published: 1 February 1992
PDF: 7 pages
Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57093
Show Author Affiliations
Peter Eggleston, Amerinex Artificial Intelligence, Inc. (United States)

Published in SPIE Proceedings Vol. 1607:
Intelligent Robots and Computer Vision X: Algorithms and Techniques
David P. Casasent, Editor(s)

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