J. Besag, Statistical analysis of dirty pictures*, Journal of Applied Statistics, vol.6, issue.5-6, pp.259-302, 1986.
DOI : 10.1016/0031-3203(83)90012-2

Y. Boykov, O. Veksler, and R. Zabih, Markov random fields with efficient approximations, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), 1998.
DOI : 10.1109/CVPR.1998.698673
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.118.3857

Y. Boykov, O. Veksler, and R. Zabih, Fast Approximate Energy Minimization via Graph Cuts, IEEE International Conference on Computer Vision, 1999.
DOI : 10.1109/iccv.1999.791245
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806

A. Delong and Y. Boykov, A scalable graph-cut algorithm for nd grids, IEEE Conference on Computer Vision and Pattern Recognition, 2008.

S. Gould, F. Amat, and D. Koller, Alphabet SOUP: A framework for approximate energy minimization, 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
DOI : 10.1109/CVPR.2009.5206650

D. Greig, B. Porteous, and A. Seheult, Exact maximum a posteriori estimation for binary images, Journal of the Royal Statistical Society: Series B, vol.51, issue.2, pp.271-279, 1989.

P. Hammer, Some network flow problems solved with pseudo-boolean programming, Operations Research, vol.13, issue.3, pp.388-399, 1965.

H. Ishikawa, Exact optimization for markov random fields with convex priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.10, pp.1333-1336, 2003.
DOI : 10.1109/TPAMI.2003.1233908

P. Kohli and P. Torr, Dynamic Graph Cuts for Efficient Inference in Markov Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.12, pp.2079-2088, 2007.
DOI : 10.1109/TPAMI.2007.1128

D. Koller and N. Friedman, Probabilistic Graphical Models: Principles and Techniques, 2009.

V. Kolmogorov and C. Rother, Minimizing Nonsubmodular Functions with Graph Cuts-A Review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.7, pp.1274-1279, 2007.
DOI : 10.1109/TPAMI.2007.1031

V. Kolmogorov and R. Zabih, What energy functions can be minimized via graph cuts?, European Conference on Computer Vision, 2002.
DOI : 10.1109/tpami.2004.1262177
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.113.1823

M. Kumar, O. Veksler, and P. Torr, Improved moves for truncated convex models, The Journal of Machine Learning Research, vol.12, pp.31-67, 2011.

V. Lempitsky, C. Rother, S. Roth, and A. Blake, Fusion Moves for Markov Random Field Optimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.8, pp.1392-1405, 2010.
DOI : 10.1109/TPAMI.2009.143

N. Schraudolph and D. Kamenetsky, Efficient exact inference in planar Ising models, Advances in Neural Information Processing Systems, 2009.

R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.6, pp.1068-1080, 2008.
DOI : 10.1109/TPAMI.2007.70844

O. Veksler, Efficient Graph-Based Energy Minimization, 1999.

O. Veksler, Graph Cut Based Optimization for MRFs with Truncated Convex Priors, 2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007.
DOI : 10.1109/CVPR.2007.383249