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Abstract : |
Address correspondence to A.L. Yuille. There is a growing interest in formulating vision problems in terms of Bayesian inference and, in particular, the maximimum a posteriori (MAP) estimator. This approach involves putting prior probability distributions, P (X), on the variables X to be inferred and a conditional distribution P (Y jX) for the measurements Y. For example, X could denote the position and configuration of a road in an aerial image and Y can be the aerial image itself (or a filtered version). We observe that these distributions define a probability distribution P (X; Y) on the ensemble of problem instances. In this paper we consider the special case of detecting 1, |
