Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields

TLDR

The paper introduces a novel Gibbs distribution–based framework for modeling and segmenting noisy and textured images. It develops hierarchical Gibbs random field models and dynamic‑programming MAP segmentation algorithms, employing simplified approximations for tractability and a new parameter‑estimation method. Experiments demonstrate that the Gibbsian model and the proposed segmentation and estimation procedures effectively handle noisy and textured images.

Abstract

This paper presents a new approach to the use of Gibbs distributions (GD) for modeling and segmentation of noisy and textured images. Specifically, the paper presents random field models for noisy and textured image data based upon a hierarchy of GD. It then presents dynamic programming based segmentation algorithms for noisy and textured images, considering a statistical maximum a posteriori (MAP) criterion. Due to computational concerns, however, sub-optimal versions of the algorithms are devised through simplifying approximations in the model. Since model parameters are needed for the segmentation algorithms, a new parameter estimation technique is developed for estimating the parameters in a GD. Finally, a number of examples are presented which show the usefulness of the Gibbsian model and the effectiveness of the segmentation algorithms and the parameter estimation procedures.