Combining different and complementary object models promises to increase the robustness and generality of today's computer vision algorithms. This paper introduces a new method for combining different object models by determining a configuration of the models which maximizes their mutual information. The combination scheme consequently creates a unified hypothesis from multiple object models ``on the fly'' without prior training. To validate the effectiveness of the proposed method, the approach is applied to the detection of faces combining the output of three different models.
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This document produced for BMVC 2001