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A common task in computer vision is to recognize the objects in an image. Most computer vision systems do this by matching models for each possible object type in turn, recognizing objects by the best matches. This is not ideal, as it does not take advantage of the similarities and differences between the possible object types. The computation time also increases linearly with the number of possible objects, which can become a problem if the number is large. A new recognition method is described: feature indexed hypotheses, which takes advantage of the similarities and differences between object types and is able to handle cases, where there are a large number of possible object types, in sublinear computation time. A two-dimensional occluded parts recognition system using this method is described.