Finding objects on aerial photographs: a rule-based low level system

Abstract
A rule-based system for image segmentation and understanding is proposed. Called ROF (rule-based object finder), it is the low-level state of an image understanding system for aerial photographs. The general design of ROF uses a novel paradigm for segmentation. Candidate pixels for regions (or borders of regions) are chosen by the raw data module (RDM) on the basis of the geometric shape of their neighborhoods. The RDM has full access to the digital picture and its pixels. Compatible sets of candidate pixels for segment formation are found by search and decision-making techniques by the aggregation module. The segmentation module, the only one that is visible to the user, attempts to construct abstract entities which can serve as global regions or parts of objects in the picture. A specialized module deals with the quantitative side of object features such as size, shade and contrast. This quantification module interfaces to all other modules of ROF.

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