Registration of Images with Outliers Using Joint Saliency Map
This addresses outlier sensitivity in image registration for medical or computer vision applications, but it is incremental as it builds on existing mutual information methods.
The paper tackled the problem of image registration with outliers by proposing a joint saliency map to enhance mutual information, resulting in an algorithm that is accurate and robust for challenging image pairs.
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.