A Robust Affine Matching Algorithm Using an Exponentially Decreasing Distance Function

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 a⬚ne parameters), so that 2-dimensional scenes as well as planar structures in 3D scenes can be handled. The a⬚ne transformation between two di erent 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 a⬚ne parameters. Affine Matching, Spatial Registration, Information Fusion, Image Understanding