Explanation mode for Bayesian automatic object recognition

Thomas L. Hazlett; Rufus H. Cofer; Harold K. Brown

doi:10.1117/12.138270

16 September 1992 Explanation mode for Bayesian automatic object recognition

Thomas L. Hazlett, Rufus H. Cofer, Harold K. Brown

Proceedings Volume 1700, Automatic Object Recognition II; (1992) https://doi.org/10.1117/12.138270
Event: Aerospace Sensing, 1992, Orlando, FL, United States

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.

Citation Download Citation

Thomas L. Hazlett, Rufus H. Cofer, and Harold K. Brown "Explanation mode for Bayesian automatic object recognition", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); https://doi.org/10.1117/12.138270

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