Event detection in multisource imaging using contextual estimation

The authors propose a novel approach to the problem of detecting events, i.e. significant differences between pictures of a given scene taken at different wavelengths or with different sensors. It is shown that event detection can be expressed, within a Bayesian decision framework, as a contextual estimation problem. The unknown process to be estimated corresponds to the significant interimage changes. A grey-level map assigned to the unknown event maximizes the a posteriori distribution of the event image, given the observed images. A Markov random field model is used to describe the spatial statistics of the unknown process. The authors present an application to the fine arts, that of finding an underpainting from a visible/X-ray pair of images of the same painting