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Abstract:
We describe a theoretical formulation for stereo in terms of the Bayesian approach to vision. This formulation enables us to integrate the depth information from
different types of matching primitives, or from different vision modules. We solve the correspondence problem using compatibility constraints between features
and prior assumptions on the interpolated surfaces that result from the matching. We use techniques from statistical physics to show how our theory relates to
previous work. Finally we show that, by a suitable choice of prior assumptions about surfaces, the theory is consistent with some psychophysical experiments
which investigate the relative importance of different matching primitives.
