Abstract
Abstract Some general principles are formulated about geometric reasoning in the context of model-based computer vision. Such reasoning tries to draw inferences about the spatial relationships between objects in a scene based on the fragmentary and uncertain geometric evidence provided by an image. The paper discusses the tasks the reasoner is to perform for the vision program, the basic competences it requires and the various methods of implementation. In the section on basic competences, some specifications of the data types and operations needed in any geometric reasoner are given.
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