A probabilistic formulation for Hausdorff matching

Matching images based on a Hausdorff measure has become popular for computer vision applications. However, no probabilistic model has been used in these applications. This limits the formal treatment of several issues, such as feature uncertainties and prior knowledge. In this paper, we develop a probabilistic formulation of image matching in terms of maximum likelihood estimation that generalizes a version of Hausdorff matching. This formulation yields several benefits with respect to previous Hausdorff matching formulations. In addition, we show that the optimal model position in a discretized pose space can be located efficiently in this formation and we apply these techniques to a mobile robot self-localization problem