Skip to main content
SpringerLink
Log in

Stochastic Image Processing

  • Book
  • © 2004

Overview

Authors:
  1. Chee Sun Won

    1. Dongguk University, Seoul, South Korea

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Robert M. Gray

    1. Stanford University, Stanford, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • Provides the first thorough treatment of Markov and hidden Markov random fields and their application to image processing

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (5 chapters)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. Introduction

    • Chee Sun Won, Robert M. Gray
    Pages 1-10

  3. Noncausal Markov Random Fields

    • Chee Sun Won, Robert M. Gray
    Pages 11-66

  4. Causal Markov Random Fields

    • Chee Sun Won, Robert M. Gray
    Pages 67-97

  5. Multiscale Markov Models

    • Chee Sun Won, Robert M. Gray
    Pages 99-124

  6. Block-Wise Markov Models

    • Chee Sun Won, Robert M. Gray
    Pages 125-148

  7. Back Matter

    Pages 149-166
    Download chapter PDF
Back to top

Keywords

About this book

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.

Authors and Affiliations

  • Dongguk University, Seoul, South Korea

    Chee Sun Won

  • Stanford University, Stanford, USA

    Robert M. Gray

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation