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Moments are generic (and usually intuitive) descriptors that can be computed from several kinds of objects defined either from closed contours or from a set of points. In this paper, image moments are used in two new methods for the pose estimation of a planar object observed through full perspective model. The first method is based on an iterative optimization scheme formulated as virtual visual servoing, while the second is based on an exhaustive but efficient optimization scheme of the two most critical parameters. It allows to avoid local minima. We finally present some experimental results to validate the theoretical developments presented in this paper.