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Algorithms for Adaptation in Aerial Surveillance

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Final rept. Apr 1998-Apr 2001

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This report discusses a self-adaptive architecture for image understanding that addresses lack of robustness common in image understanding programs. The architecture provides support for making image understanding programs that can manipulate their own semantics and thereby adjust their structure in response to changes in the environment that might cause static image understanding systems to fail. The general approach taken has been to explore the ideas of self-adaptive software and implement an architectural framework that addresses a class of problems that we term interpretation problems common in image understanding. The idea is that to make programs robust to changing environmental conditions that they should be aware of their relationship with the environment and be able to restructure themselves at runtime in order to track changes in the environment. The implementation takes the form of a multi-layered reflective interpreter that manipulates and runs simple agents. The interpreter framework utilizes Monte-Carlo sampling as a mechanism for estimating most likely solutions, uses Minimum Description Length MDL as a central coordinating device, and includes a theorem prover based compiler to restructure the program when necessary. To test the architectural ideas developed in the report a test domain of interpreting aerial images was chosen. The task of the program is to segment, label, and parse aerial images so as to produce an image description similar to descriptions produced by a human expert. An image corpus is developed that is used as the source of domain knowledge.

Subject Categories:

  • Cartography and Aerial Photography
  • Computer Programming and Software

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