A model-based vision system attempts to find a correspondence between features of an object model and features detected in an image. Most feature-based matching schemes assume that all the features that are potentially visible in a view of all object will appear with equal probability. The resultant matching algorithms have to allow for 'errors' without really understanding what they mean. PREMIO is an object recognition/localization system under construction at the University of Washington that attempts to model some of the physical processes that can cause these 'errors'. PREMIO combines techniques of analytic graphics and computer vision to predict how features of the object will appear in images under various assumptions of lighting, viewpoint, sensor, and image processing operators. These analytic predictions are used in a probabilistic matching algorithm to guide the search and to greatly reduce the search space.
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