We present an aspect-graph approach to 3 0 object recognition where the definition of an aspect is motivated by its role in the subsequent recognition step. Specijically, we measure the similarity between two views by a 2 0 shape metric of similarity measuring the distance between the projected, segmented shapes of the 3 0 object. This endows the viewing sphere with a metric which is used to group similar views into aspects, and to represent each aspect by a prototype. The same shape similarity metric is then used to rate the similarity between unknown views of unknown objects and stored prototypes to identify the object and its pose. The performance of this approach on a database of 18 objects each viewed in five degree increments along the ground viewing plane is demonstrated.

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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 t o r e ne 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 speci cation of the appropriate initial conditions, however requires user interaction and is time consuming. We pr opose an approach where sample 2D views are generated f r om the 3D model and matched against the given view (2D-3D registration). Additional views are then generated in the vicinity of the best view and the procedure i s r epeated until convergence. Results of estimating the coordinates of a vertebrae spine bone from its 3D model, obtained f r om volumetric (CT or MR) data, and a 2D view, as might be obtained f r om uoroscopic data, demonstrates that the pose can be reliably obtained without requiring extensive user interface.