Markov Random Fields for Vision and Image Processing

by Andrew Blake, Pushmeet Kohli, Carsten Rother, Andrew Blake, Pushmeet Kohli, Yuri Boykov, Vladimir Kolmogorov, Olga Veksler, Ramin Zabih, Hiroshi Ishikawa, Alan L. Yuille, Yair Weiss, Chen Yanover, Talya Meltzer, Carsten Rother, Dheeraj Singaraju, Leo Grady, Ali Kemal Sinop, René Vidal, Antonio Criminisi, Geoffrey Cross, William Freeman, Ce Liu, Richard Szeliski, Daniel Scharstein, Aseem Agarwala, Marshall Tappen, Daniel Cremers, Thomas Pock, Kalin Kolev, Antonin Chambolle, Michael Isard, Martin Szummer, Derek Hoiem, M. Pawan Kumar, Philip Torr, Nikos Komodakis, Victor Lempitsky, Stefan Roth, Michael J. Black, Lubor Ladicky, Sara Vicente, Jian Sun, Yin Li, Sing Bing Kang, Heung-Yeung Shum, John Winn, Jamie Shotton, Jenny Yuen, Antonio Torralba, Alex Rav-Acha,

Series: The MIT Press

Subject categories
ISBNs
  • 0262297442
  • 9780262015776
  • 9780262297448

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for future study.

This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image reconstruction, image segmentation, 3D vision, and object labeling. It offers key findings and state-of-the-art research on both algorithms and applications.

After an introduction to the fundamental concepts used in MRFs, the book reviews some of the main algorithms for performing inference with MRFs; presents successful applications of MRFs, including segmentation, super-resolution, and image restoration, along with a comparison of various optimization methods; discusses advanced algorithmic topics; addresses limitations of the strong locality assumptions in the MRFs discussed in earlier chapters; and showcases applications that use MRFs in more complex ways, as components in bigger systems or with multiterm energy functions. The book will be an essential guide to current research on these powerful mathematical tools.

  • The MIT Press; July 2011
  • ISBN: 9780262297448
  • Read online, or download in secure PDF format
  • Title: Markov Random Fields for Vision and Image Processing
  • Series: The MIT Press
  • Author: Andrew Blake (ed.); Pushmeet Kohli (ed.); Carsten Rother (ed.); Andrew Blake (contrib.); Pushmeet Kohli (contrib.); Yuri Boykov (contrib.); Vladimir Kolmogorov (contrib.); Olga Veksler (contrib.); Ramin Zabih (contrib.); Hiroshi Ishikawa (contrib.); Alan L. Yuille (contrib.); Yair Weiss (contrib.); Chen Yanover (contrib.); Talya Meltzer (contrib.); Carsten Rother (contrib.); Dheeraj Singaraju (contrib.); Leo Grady (contrib.); Ali Kemal Sinop (contrib.); René Vidal (contrib.); Antonio Criminisi (contrib.); Geoffrey Cross (contrib.); William Freeman (contrib.); Ce Liu (contrib.); Richard Szeliski (contrib.); Daniel Scharstein (contrib.); Aseem Agarwala (contrib.); Marshall Tappen (contrib.); Daniel Cremers (contrib.); Thomas Pock (contrib.); Kalin Kolev (contrib.); Antonin Chambolle (contrib.); Michael Isard (contrib.); Martin Szummer (contrib.); Derek Hoiem (contrib.); M. Pawan Kumar (contrib.); Philip Torr (contrib.); Nikos Komodakis (contrib.); Victor Lempitsky (contrib.); Stefan Roth (contrib.); Michael J. Black (contrib.); Lubor Ladicky (contrib.); Sara Vicente (contrib.); Jian Sun (contrib.); Yin Li (contrib.); Sing Bing Kang (contrib.); Heung-Yeung Shum (contrib.); John Winn (contrib.); Jamie Shotton (contrib.); Jenny Yuen (contrib.); Antonio Torralba (contrib.); Alex Rav-Acha (contrib.); Andrew Fitzgibbon (contrib.)
  • Imprint: The MIT Press
Subject categories
ISBNs
  • 0262297442
  • 9780262015776
  • 9780262297448
Subject categories
ISBNs
  • 0262297442
  • 9780262015776
  • 9780262297448