Use of optimal nonlinear estimation theory in statistical image processing

The paper is concerned with the problem of simulating and identifying the boundaries of a noisy image treated as a two-dimensional random field. An optimal nonlinear estimation algorithm is proposed, and its efficiency is demonstrated by using a computer-simulated 32 x 32 image. The parameter estimates are shown to be invariant under shift, rotation, and change-of-scale transformations.