A Robust Affine Matching Algorithm Using an Exponentially Decreasing Distance Function
Axel Pinz (Institute for Computer Graphics, Technical University of Graz, Austria)
Manfred Prantl (Institute for Computer Graphics, Technical University of Graz, Austria)
Harald Ganster (Institute for Computer Graphics, Technical University of Graz, Austria)
Abstract: We describe a robust method for spatial registration, which relies on the coarse correspondence of structures extracted from images, avoiding the establishment of point correspondences. These structures (tokens) are points, chains, polygons and regions at the level of intermediate symbolic representation (ISR). The algorithm recovers conformal transformations (4 affine parameters), so that 2-dimensional scenes as well as planar structures in 3D scenes can be handled. The affine transformation between two different tokensets is found by minimization of an exponentially decreasing distance function. As long as the tokensets are kept sparse, the method is very robust against a broad variety of common disturbances (e.g. incomplete segmentations, missing tokens, partial overlap). The performance of the algorithm is demonstrated using simple 2D shapes, medical, and remote sensing satellite images. The complexity of the algorithm is quadratic on the number of affine parameters.
Keywords: Affine Matching, Image Understanding, Information Fusion, Spatial Registration
Categories: I.2.10, I.4, I.5