Unified approach for low level image analysis

Low level image analysis has been found to be difficult if the images are complicated. A popular strategy has been to model the image parametrically. Two prominent classes of approaches to the problem of parametrically modeling images are those based on assuming a stochastic relationship among the pixels, and those based on assuming a deterministic relationship. These two approaches have tend to have complementary areas of applicability. We develop a method, based on a general decision criterion for dealing with a variety of modeling starategies, and refer to the method as a unified approach. As a consequence, this approach leads to advantages in segmenting images. The algorithm´s output has the form that is convenient as input for higher-level processing such as AI approaches to computer vision.