Thanks to Bayes' theorem, any image processing problem involves two parts: a model of the image, given the semantics one is seeking, and a prior model of this semantics. Many problems involve a semantics of the form 'the volume in the world that projected to the region R in the image had parameters P', and thus models of regions, and models of images within regions, acquire great importance. My current research focuses on the construction of such models.