Performance evaluations of correlations of digital images using different separability measures

A comparison of four separability measures which are useful in the registration of two-dimensional images is presented. Bayes probability of error, Chernoff bound, Bhattacharyya bound, and Fisher's criteria are used in the selection of the appropriate reference images from a set of aerial pictures, each exhibiting a different view of a scene, i.e., down-looking and target-looking. The experiment is repeated for corresponding synthetic images of this scene. Both area and edge correlations are used. A comparison of the measures and the resulting probabilities of error is made. The results show that for real images the target-looking view performs better than the down-looking view for both area and edge correlations. For synthetic images, the down-looking view performs better than the target-looking view for both area and edge correlations. The variation in synthetic images between the target-looking and the down-looking views is larger than in the real images for both area and edge correlations.