D. Munoz, J. A. Bagnell, N. Vandapel, and M. Hebert, Contextual classification with functional Max-Margin Markov Networks, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.975-982, 2009.
DOI : 10.1109/CVPR.2009.5206590

M. Weinmann, B. Jutzi, S. Hinz, and C. Mallet, Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers, ISPRS Journal of Photogrammetry and Remote Sensing, vol.105, pp.286-304, 2015.
DOI : 10.1016/j.isprsjprs.2015.01.016

T. Hackel, J. D. Wegner, and K. Schindler, Fast semantic segmentation of 3d point clouds with strongly varying density, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.177-184, 2016.

J. Niemeyer, F. Rottensteiner, and U. Soergel, Classification of urban LiDAR data using conditional random field and random forests, Joint Urban Remote Sensing Event 2013, pp.139-142, 2013.
DOI : 10.1109/JURSE.2013.6550685

R. Shapovalov, A. Velizhev, and O. Barinova, Nonassociative Markov networks for 3d point cloud classification, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVIII-3A, pp.103-108, 2010.

J. D. Lafferty, A. Mccallum, and F. C. Pereira, Conditional random fields: probabilistic models for segmenting and labeling sequence data, Proc. ICML, pp.282-289, 2001.

N. Chehata, L. Guo, and C. Mallet, Airborne lidar feature selection for urban classification using random forests, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.207-212, 2009.

J. S. Yedidia, W. T. Freeman, and Y. Weiss, Understanding belief propagation and its generalizations, Exploring Artificial Intelligence in the New Millennium, vol.8, pp.236-239, 2003.

M. F. Tappen and W. T. Freeman, Comparison of graph cuts with belief propagation for stereo, using identical MRF parameters, Proceedings Ninth IEEE International Conference on Computer Vision, pp.900-906, 2003.
DOI : 10.1109/ICCV.2003.1238444

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.
DOI : 10.1109/34.969114

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.112.6806

L. Breiman, Random forests, Machine Learning, pp.5-32, 2001.

G. D. Forney, The viterbi algorithm, Proceedings of the IEEE, vol.61, issue.3, pp.268-278, 1973.
DOI : 10.1109/PROC.1973.9030

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

M. Schmidt, A Matlab toolbox for probabilistic undirected graphical models, 2007.