Acquisition of Complex Model Knowledge by Domain Theory-Controlled Generalization

Nearly all three-dimensional reconstruction methods lack proper model knowledge that reflects the scene. Model knowledge is required in order to reduce ambiguities which occur during the reconstruction process. It must comprise the scene and is therefore complex, and additionally difficult to acquire. In this paper we present an approach for the learning of complex model knowledge. A (large) sample set of three-dimensionally acquired buildings represented 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 generalization result is a distribution of relevant patterns which reduces ambiguities occurring in 3D object reconstruction (here: buildings). Three different applications for the 3D reconstruction of buildings from aerial images are executed whereas binary relations of so-called building atoms, namely tertiary nodes and faces, and building models are learned. These applications are evaluated based on (a) the estimated empirical generalization error and (b) the use of information coding theory and statistics by comparing the learned knowledge with non-available a priori knowledge.