Using Context to Control Computer Vision Algorithms

We are investigating the design of an architecture that can be used as the basis for controlling the invocation of image understanding algorithms for cartographic feature extraction. The key research question is whether sufficient contextual constraints are available to choose algorithms and their parameters for aerial photo analysis. Our approach has been to apply the context-based architecture incorporated in CONDOR, an SRI system for automatically constructing scene models of natural terrain from ground-level views. The semiautomated nature of cartographic feature extraction allows access to additional sources of contextual constraints that were not available to CONDOR.1