Maximum likelihood estimation of object size and orientation from projection data

The problem of detecting, locating and characterizing objects in a 2D cross-section from noisy projection data has been considered recently [1-3], in which objects are characterized by a finite number of parameters, which are estimated directly from noisy projection measurements. In this paper, the problem of maximum likelihood (ML) estimation of those parameters characterizing the geometry of an object (e.g. size and orientation) is considered, and estimation performance is investigated.