The previous chapters described an approach to object recognition based upon top-down predictions and iterative refinement. That discussion centered around the best system attributes for solving recognition problems in two example domains. Now a switch is made from constructing the algorithm to a more thorough evaluation of performance based on a wider range of scenes. Currently, the dataset contains 80 test problems (see Chapter 3). This chapter evaluates the entire Render-Match-Refine (RMR) system on those 60 images not used by the previous two chapters. Within these 60 images, there are 190 instances of 17 different objects.
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