In computer vision it is common to define algorithms in terms of matching against exemplars. This paper describes a probabilistic framework for such algorithms. It allows you to assign a consistent likelihood for each exemplar, eliminating the normalization and bias problems which occur under the scheme of Toyama & Blake (2002).

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