Real-time modeling of image sequences based on hidden Markov mesh random field models

The image modeling problem is discussed under the assumption that images can be represented by third-order. hidden Markov mesh random field models. The modeling applications comprise restoration of binary images, compression of image data, and segmentation of gray-level images and image sequences under the short-range motion hypothesis. Coherent approaches to the problems of image modeling and estimation of model parameters are outlined. A labeling algorithm based on a maximum marginal a posteriori probability criterion is proposed. Critical aspects of the computer simulation of a real-time implementation are discussed in detail. A learning technique by which the model parameters can be estimated without ground truth information is developed. Extensive experimentation with both static and dynamic images from a variety of sources is discussed