Image change detection using copulas

This paper explores a new class of measures for the detection of changes in images, specially for images acquired from different classes of sensors such as synthetic aperture radar (SAR) systems or computerized axial tomography (CAT) systems, monitoring patients. The problems become very challenging as the local statistics may be different even though the observations in the images may be similar. By exploiting this similarity new approaches are proposed for change detection. Based on the assumption that some form of dependence exists between the images, this dependence can be modeled by copulas. By using the conditional copula and the second image to simulate the distribution of first image, the dependence between the two images may be more closely modeled by the ensuing joint distribution. As a follow on, the symmetrical Kullback-Leibler distance can be used to obtain the change indicator between the distributions associated with the two images. In this paper the conditional copula is used as a change detector and applied to scenes from two distinct and different image families -SAR and CAT, and its performance compared with that of conventional change detection algorithms, based on a pixel based difference measure and on local pixel statistics.