Challenges to accurate registration come from three factors -presence of background clutter, occlusion of the pattern being registered and changes in feature values across images. To address these concerns, we propose a robust probabilistic estimation approach predicated on representations of the object model and the target image using a kernel density estimate. These representations are then matched in the space of density functions using a correlation measure, termed the Kernel Density Correlation (KDC) measure. A popular metric which has been widely used by previous image registration approaches is the Mutual Information (MI) metric. We compare the proposed KDC metric with the MI metric to highlight its better robustness to occlusions and random background clutter-this is a consequence of the fact that the KDC measure forms a re-descending M-estimator. Another advantage of the proposed metric is that the registration problem can be efficiently solved using a variational optimization algorithm. We show that this algorithm is an iteratively reweighted least squares (IRLS) algorithm and prove its convergence properties. The efficacy of the proposed algorithm is demonstrated by its application on standard stereo registration data-sets and real tracking sequences.
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