Polynomial-Time Object Recognition in the Presence of Clutter, Occlusion, and Uncertainty

We consider the problem of object recognition via local geometric feature matching in the presence of sensor uncertainty, occlusion, and clutter. We present a general formulation of the problem and a polynomial-time algorithm which guarantees finding all geometrically feasible interpretations of the data, modulo uncertainty, in terms of the model. This formulation applies naturally to problems involving both 2D and 3D objects.