This paper introduces a system that automatically classifies registration problems based on the type of registration required. Rather than rely on a single “best” algorithm, the proposed system is made up of a suite of image registration techniques. Image pairs are analyzed according to the types of variation that occur between them, and appropriate algorithms are selected to solve for the alignment. In the case where multiple forms of variation are detected all potentially appropriate algorithms are run, and a normalized cross correlation (NCC) of the results in their respective error spaces is performed to select which alignment is best. In 87% of the test cases the system selected the transform of the expected corresponding algorithm, either through elimination or through NCC, while in the final 13% a better transform (as calculated by NCC) was proposed by one of the other methods. By classifying the type of registration problem and choosing an appropriate method the system significantly improves the flexibility and accuracy of automatic registration techniques.

Presented in Liège, October 2009 at the International Conference on Computer Vision Systems.