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This paper describes a method for determining an object's pose given its 3D model and a 2D view. This 2D-3D registration problem arises in a number of medical applications, e.g. image guided spine procedures. Previous approaches often rely on a good initial estimate of the pose parameters and an optimization procedure to refine this initial pose estimate, e.g. the iterative closest point (ICP). However, such algorithms can identify local minima as global minima, leading to registration errors, if the initial pose is not carefully chosen. The specification of the appropriate initial conditions however requires user interaction and is time consuming. We propose an approach where sample 2D views are generated from the 3D model and matched against the given view (2D-3D registration). Additional views are then generated near the best view and the procedure is repeated until convergence. Results of estimating the coordinates of a vertebrae spine bone from its 3D model, obtained from volumetric (CT or MR) data, and a 2D view, as might be obtained from fluoroscopic data, demonstrates that the pose can be reliably obtained without requiring extensive user interface.