A Statistical Framework for Robust Fusion of Depth Information

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Abstract
We describe a simple statistical framework intended as a model of how depth estimates derived from consistent depth cues are combined in biological vision. We assume that the rule of combination is linear, and that the weights assigned to estimates in the linear combination are variable. These weight values corresponding to different depth cues are determined by ancillary measures, information concerning the likely validity of different depth cues in a particular scene. The parameters of the framework may be estimated psychophysically by procedures described. The conditions under which the framework may be regarded as normative are discussed.
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