Sections
1. Introduction
2. Shortcomings of previous attempts to deal
with the subject
3. Goals and IVES System Architecture
4. Interactive on-the-fly analysis and
recording
5. Multi-image knowledge extractor
6. Iconic Markup in IVES
7. Iconic ontological conflation
8. Conclusion
9. Acknowledgements
10. Exercises and problems
11. References
Abstract
The unique human expertise in imagery analysis should be preserved and shared with other imagery analysts to improve image analysis and decision-making. Such knowledge can serve as a corporate memory and be a base for an imagery virtual expert. The core problem in reaching this goal is constructing a methodology and tools that can assist in building the knowledge base of imagery analysis. This chapter provides a framework for an imagery virtual expert system that supports imagery registration and conflation tasks. The approach involves tree strategies: (1) recording expertise on-the-fly and (2) extracting information from the expert in an optimized way using the theory of monotone Boolean functions and (3) use of iconized ontologies to built a conflation method.