Decomposition and hierarchy: efficient structural matching of large multi-scale representations

Using the registration of remote imagery as an example do- main, this work describes an efficient approach to the structural matching of multi-resolution representations where the scale difference, rotation and translation are unknown. The matching process is posed within an optimisation framework in which the parameter space is the probabil- ity hyperspace of all possible matches. In this application, searching for corresponding features at all scales generates a parameter space of enor- mous dimensions - typically 1-10 million. In this work we use feature’s hierarchical relationships to decompose the parameter space into a series of smaller subspaces over which optimisation is computationally feasible.