Share Email Print

Proceedings Paper

Explanation mode for Bayesian automatic object recognition
Author(s): Thomas L. Hazlett; Rufus H. Cofer; Harold K. Brown
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

One of the more useful techniques to emerge from AI is the provision of an explanation modality used by the researcher to understand and subsequently tune the reasoning of an expert system. Such a capability, missing in the arena of statistical object recognition, is not that difficult to provide. Long standing results show that the paradigm of Bayesian object recognition is truly optimal in a minimum probability of error sense. To a large degree, the Bayesian paradigm achieves optimality through adroit fusion of a wide range of lower informational data sources to give a higher quality decision--a very 'expert system' like capability. When various sources of incoming data are represented by C++ classes, it becomes possible to automatically backtrack the Bayesian data fusion process, assigning relative weights to the more significant datums and their combinations. A C++ object oriented engine is then able to synthesize 'English' like textural description of the Bayesian reasoning suitable for generalized presentation. Key concepts and examples are provided based on an actual object recognition problem.

Paper Details

Date Published: 16 September 1992
PDF: 11 pages
Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); doi: 10.1117/12.138270
Show Author Affiliations
Thomas L. Hazlett, Florida Institute of Technology (United States)
Rufus H. Cofer, Florida Institute of Technology (United States)
Harold K. Brown, Florida Institute of Technology (United States)

Published in SPIE Proceedings Vol. 1700:
Automatic Object Recognition II
Firooz A. Sadjadi, Editor(s)

© SPIE. Terms of Use
Back to Top