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Addresses the problem of fast object recognition and pose determination of segmented objects. It combines the well-studied parametric eigenspace method with statistical moments of image signatures resulting in a computationally and memory efficient algorithm. The approach is suited for time or memory critical applications, e.g. in embedded systems. A variety of experiments on a set of 1620 images compare the recognition and pose estimation performance to the standard eigenspace technique. The results show that despite the reduced memory and speed requirements the recognition rate is identical to the standard method; only under heavy noise conditions is the pose estimation accuracy slightly lower.