Stochastic Image Processing provides the first thorough treatment of
Markov and hidden Markov random fields and their application to image
processing. Although promoted as a promising approach for over thirty
years, it has only been in the past few years that the theory and
algorithms have developed to the point of providing useful solutions to
old and new problems in image processing. Markov random fields are a
multidimensional extension of Markov chains, but the generalization is
complicated by the lack of a natural ordering of pixels in
multidimensional spaces. Hidden Markov fields are a natural
generalization of the hidden Markov models that have proved essential to
the development of modern speech recognition, but again the
multidimensional nature of the signals makes them inherently more
complicated to handle. This added complexity contributed to the long
time required for the development of successful methods and
applications. This book collects together a variety of successful
approaches to a complete and useful characterization of multidimensional
Markov and hidden Markov models along with applications to image
analysis. The book provides a survey and comparative development of an
exciting and rapidly evolving field of multidimensional Markov and
hidden Markov random fields with extensive references to the literature.