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Currently, a major difficulty for the widespread use of robots in assembly and material handling comes from the necessity of feeding accurately positioned workpieces to robots. ``Bin picking'' techniques help reduce this constraint. This paper presents the application of matched filters for enabling robots with vision to acquire workpieces randomly stored in bins. This approach complements heuristic methods already reported. The concept of matched filter is an old one. Here, however, it is redefined to take into account robot end-effector features, in terms of geometry and mechanics. In particular, the proposed filters match local workpiece structures where the robot end-effector is likely to grasp successfully and hold workpieces. The local nature of the holdsites is very important as computation costs are shown to vary with the fifth power of structure size. In addition, the proposed filters tend to have a narrow angular bandwidth. An example, which features a parallel-jaw hand is developed in detail, using both statistical and Fourier models. Both approaches concur in requiring a very small number of filters (typically four), even if a good orientation accuracy is expected (two degrees). Success rates of about 90 percent in three or fewer attempts have been experimentally obtained on a system which includes a small minicomputer, a 128 Ã 128 pixel solidstate camera, a prototype Cartesian robot, and a ``universal'' parallel-jaw hand.