Affine Matching of Intermediate Symbolic Representations

Spatial registration of images, features and symbols is important for the comparison of these entities, as well as for the fusion of visual information gathered from diverse sources. We introduce a method for spatial registration, which relies on the coarse correspondence of structures extracted from the images, and which does not require the establishment of point correspondences. These structures (tokens) are points, chains, polygons and regions at the level of intermediate symbolic representation (ISR). The conformai (4 affine parameter) transformation is found by a combination of hierarchical hypothesize and test, stepwise refinement of parameters, and parameter variation avoiding local minima. Since belief values guide the probability that a certain token is selected for correspondence, and many-to-many correspondences are possible, the method is very robust against a broad variety of common disturbances like incomplete segmentations, missing tokens or partial overlap. This is demonstrated using synthetic test data as well as quite complicated multisource medical images. The establishment of this kind of spatial relationships between different ISR data sets is used as one module of a system for information fusion in image understanding.