  Mutual information (MI) is a popular similarity measure for image
registration, whereby good registration can be achieved by maximizing the
compactness of the clusters in the joint histogram. However, MI is sensitive to
the "outlier" objects that appear in one image but not the other, and also
suffers from local and biased maxima. We propose a novel joint saliency map
(JSM) to highlight the corresponding salient structures in the two images, and
emphatically group those salient structures into the smoothed compact clusters
in the weighted joint histogram. This strategy could solve both the outlier and
the local maxima problems. Experimental results show that the JSM-MI based
algorithm is not only accurate but also robust for registration of challenging
image pairs with outliers.
