A bayesian approach to edge detection in images

New statistical techniques for the edge detection problem in images are developed. The image is modeled by signal and noise, which are independent, additive, Gaussian, and autoregressive in two dimensions. The optimal solution, in terms of statistical decision theory, leads to a test that decides among multiple, composite, overlapping hypotheses. A redefinition of the problem, involving nonoverlapping hypotheses, allows the formulation of a computationally attractive scheme. Results are presented with both simulated data and real satellite images. A comparison with standard gradient techniques is made.