Acquisition of complex model knowledge by domain theory-controlled generalization

Nearly all three-dimensional reconstruction methods lack proper model knowl edge that reflects the scene. Model knowledge is required in order to reduc e ambiguities which occur during the reconstruction process. It must compri se the scene and is therefore complex, and additionally difficult to acquir e. In this paper we present an approach for the learning of complex model k nowledge. A (large) sample set of three-dimensionally acquired buildings re presented as graphs is generalized by the use of background knowledge. The background knowledge entails domain-specific knowledge and is utilized for the search guidance during the generalization process of EXRES. The general ization result is a distribution of relevant patterns which reduces ambigui ties occurring in 3D object reconstruction (here: buildings). Three differe nt applications for the 3D reconstruction of buildings from aerial images a re executed whereas binary relations of so-called building atoms, namely te rtiary nodes and faces, and building models are learned. These applications are evaluated based on (a) the estimated empirical generalization error an d (b) the use of information coding theory and statistics by comparing the learned knowledge with non-available a priori knowledge.