Unified approach for early-phase image understanding using a general decision criterion

Two types of approaches for computer vision are combined to model images or portions thereof, parametrically. These approaches, namely those based on polynomial models and those based on random-field models, are combined based on a general decision criterion for dealing with a variety of modeling strategies. Selection among alternative model structures is in accordance with the tradeoff between sample size and model complexity. Experiments with synthesized images and natural images such as Brodatz textures illustrate some identification and segmentation uses of this unified approach. The implemented segmentation algorithm achieves early-phase region extraction without relying on any contextual or high-level assumptions. A natural result of this is a list of regions, suitable as input for higher-level stages of image understanding in addition to a pixel-labeled image.<>