In the quest for computer vision, that is the automatic understanding of images, a powerful strategy has been to model the image parametrically. Two prominent kinds of approaches have been those based. on polynomial models and those based on random-field models. This thesis combines these two methodologies, deciding on the proper model by means of a general decision criterion. The unified approach also admits composite polynomial/random-field. models and is applicable to other statistical models as well. This new approach has advantages in many applications, such as image identification and image segmentation. In segmentation, we achieve speed by avoiding iterative pixel-by-pixel calculations. With the general decision criterion as a sophisticated tool, we can deal with images according to a variety of model hypotheses. Our experiments with synthesized images and real images, such as Brodatz textures, illustrate some identification and segmentation uses of the unified approach. / Master of Science / incomplete_metadata