Bibliography

[1]  A. Blake and A. Zisserman, Visual Reconstruction. MIT Press, London, 1987.

[2]  A. Blake, C. Rother, M. Brown, P. Perez, and P. Torr, “Interactive image segmentation using an adaptive GMMRF model,” in Proc. European Conference on Computer Vision (ECCV2004), 2004, pp. 428–441.

[3]  Y. Boykov and G. Funka-Lea, “Graph cuts and efficient n-d image segmentation,” International Journal of Computer Vision, vol. 70, no. 2, pp. 109–131, 2006.

[4]  Y. Boykov and M.-P. Jolly, “Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images,” in Proc. IEEE International Conference on Computer Vision (ICCV2001), vol. 1, 2001, pp. 105–112.

[5]  M. Bray, P. Kohli, and P. H. A. Torr, “Posecut: Simultaneous segmentation and 3d pose estimation of humans using dynamic graph-cuts,” in Proc. European Conference on Computer Vision (ECCV2006), vol. 2, 2006, pp. 642–655.

[6]  H. Ishikawa and D. Geiger, “Segmentation by grouping junctions,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’98), 1998, pp. 125–131.

[7]  ——, “Higher-dimensional segmentation by minimum-cut algorithm,” in Ninth IAPR Conference on Machine Vision Applications (MVA 2005), 2005, pp. 488–491.

[8]  Y. Li, J. Sun, and H.-Y. Shum, “Video object cut and paste.” ACM Trans. Graphics (Proc. SIGGRAPH2005), vol. 24, no. 3, pp. 595–600, 2005.

[9]  Y. Li, J. Sun, C.-K. Tang, and H.-Y. Shum, “Lazy snapping,” ACM Trans. Graphicss (Proc. SIGGRAPH2004), vol. 23, no. 3, pp. 303–308, 2004.

[10]  K. Li, X. Wu, D. Z. Chen, and M. Sonka, “Optimal surface segmentation in volumetric images—a graph-theoretic approach,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 1, pp. 119–134, 2006.

[11]  C. Rother, V. Kolmogorov, and A. Blake, ““grabcut”: Interactive foreground extraction using iterated graph cuts,” ACM Trans. Graphics (Proc. SIGGRAPH2004), vol. 23, no. 3, pp. 309–314, 2004.

[12]  J. Wang, P. Bhat, R. A. Colburn, M. Agrawala, and M. F. Cohen, “Interactive video cutout,” ACM Trans. Graphics (Proc. SIGGRAPH2005), vol. 24, no. 3, pp. 585–594, 2005.

[13]  N. Xu, R. Bansal, and N. Ahuja, “Object segmentation using graph cuts based active contours,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2003), vol. 2, 2003, pp. 46–53.

[14]  M. P. Kumar, P. H. Torr, and A. Zisserman, “Learning layered motion segmentations of video,” International Journal of Computer Vision, vol. 76, no. 3, pp. 301–319, 2008.

[15]  S. Roy and V. Govindu, “MRF solutions for probabilistic optical flow formulations,” in Proc International Conference on Pattern Recognition (ICPR2000), vol. 3, 2000, pp. 7053–7059.

[16]  J. Wills, S. Agarwal, and S. Belongie, “What went where,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2003), vol. 1, 2003, pp. 37–44.

[17]  J. Xiao and M. Shah, “Motion layer extraction in the presence of occlusion using graph cuts,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1644–1659, 2005.

[18]  S. Birchfield and C. Tomasi, “Multiway cut for stereo and motion with slanted surfaces,” in Proc. IEEE International Conference on Computer Vision (ICCV’99), vol. 1, 1999, pp. 489–495.

[19]  Y. Boykov, O. Veksler, and R. Zabih, “Markov random fields with efficient approximations,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’98), 1998, pp. 648–655.

[20]  H. Ishikawa, “Multi-scale feature selection in stereo,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’99), vol. 1, 1999, pp. 1132–1137.

[21]  H. Ishikawa and D. Geiger, “Local feature selection and global energy optimization in stereo,” in Scene Reconstruction, Pose Estimation and Tracking, R. Stolkin, Ed. Vienna, Austria: I-Tech Education and Publishing, 2007, pp. 411–430.

[22]  V. Kolmogorov, A. Criminisi, A. Blake, G. Cross, and C. Rother, “Probabilistic fusion of stereo with color and contrast for bilayer segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1480–1492, 2006.

[23]  S. Roy and I. Cox, “Maximum-flow formulation of the n-camera stereo correspondence problem,” in Proc. IEEE International Conference on Computer Vision (ICCV’98), 1998, pp. 492–499.

[24]  S. Roy, “Stereo without epipolar lines : A maximum-flow formulation,” International Journal of Computer Vision, vol. 34, pp. 147–162, 1999.

[25]  V. Kwatra, A. Schödl, I. Essa, G. Turk, and A. Bobick, “Graphcut textures: Image and video synthesis using graph cuts,” ACM Trans. Graphics (Proc. SIGGRAPH2003), vol. 22, no. 3, pp. 277–286, 2003.

[26]  M. H. Nguyen, J.-F. Lalonde, A. A. Efros, and F. de la Torre, “Image based shaving,” Computer Graphics Forum Journal (Eurographics 2008), vol. 27, no. 2, pp. 627–635, 2008.

[27]  A. Agarwala, M. Dontcheva, M. Agrawala, S. Drucker, A. Colburn, B. Curless, D. Salesin, and M. Cohen, “Interactive digital photomontage,” ACM Trans. Graphics (Proc. SIGGRAPH2004), vol. 23, no. 3, pp. 294–302, 2004.

[28]  A. Agarwala, M. Agrawala, M. Cohen, D. Salesin, and R. Szeliski, “Photographing long scenes with multi-viewpoint panoramas,” ACM Trans. Graphics (Proc. SIGGRAPH2006), vol. 25, no. 3, pp. 853–861, 2006.

[29]  J. Hays and A. A. Efros, “Scene completion using millions of photographs,” ACM Trans. Graphics (Proc. SIGGRAPH2007), vol. 26, no. 3, 2007, Article# 4.

[30]  C. Rother, S. Kumar, V. Kolmogorov, and A. Blake, “Digital tapestry,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2005), vol. 1, 2005, pp. 589–596.

[31]  M. P. Kumar, P. H. S. Torr, and A. Zisserman, “Obj cut,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2005), vol. 1, 2005, pp. 18–25.

[32]  D. Hoiem, C. Rother, and J. Winn, “3D layout CRF for multi-view object class recognition and segmentation,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2007), 2007.

[33]  J. Winn and J. Shotton, “The layout consistent random field for recognizing and segmenting partially occluded objects,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2006), 2006, pp. 37–44.

[34]  Y. Boykov and V. Kolmogorov, “Computing geodesics and minimal surfaces via graph cuts,” in Proc. IEEE International Conference on Computer Vision (ICCV2003), vol. 1, 2003, pp. 26–33.

[35]  Y. Boykov, V. Kolmogorov, D. Cremers, and A. Delong, “An integral solution to surface evolution PDEs via geo-cuts,” in Proc. European Conference on Computer Vision (ECCV2006), vol. 3, 2006, pp. 409–422.

[36]  A. Levin, R. Fergus, F. Durand, and W. T. Freeman, “Image and depth from a conventional camera with a coded aperture,” ACM Trans. Graphics (Proc. SIGGRAPH2007), vol. 26, no. 3, 2007, Article# 70.

[37]  C. Liu, A. B. Torralba, W. T. Freeman, F. Durand, and E. H. Adelson, “Motion magnification,” ACM Trans. Graphics (Proc. SIGGRAPH2005), vol. 24, no. 3, pp. 519–526, 2005.

[38]  Y. Boykov, O. Veksler, and R. Zabih, “Fast approximate energy minimization via graph cuts,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 11, pp. 1222–1239, 2001.

[39]  J. Besag, “On the statistical analysis of dirty pictures,” J. Royal Stat. Soc., Series B, vol. 48, pp. 259–302, 1986.

[40]  S. Geman and D. Geman, “Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 6, pp. 721–741, 1984.

[41]  P. L. Hammer, “Some network flow problems solved with pseudo-boolean programming,” Operations Res., vol. 13, pp. 388–399, 1965.

[42]  D. M. Greig, B. T. Porteous, and A. H. Seheult, “Discussion of: On the statistical analysis of dirty pictures (by J. E. Besag),” J. Royal Stat. Soc., Series B, vol. 48, pp. 282–284, 1986.

[43]  ——, “Exact maximum a posteriori estimation for binary images,” J. Royal Stat. Soc., Series B, vol. 51, pp. 271–279, 1989.

[44]  A. Blake, “Comparison of the efficiency of deterministic and stochastic algorithms for visual reconstruction,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, no. 1, pp. 2–12, 1989.

[45]  H. Ishikawa and D. Geiger, “Occlusions, discontinuities, and epipolar lines in stereo,” in Proc. European Conference on Computer Vision (ECCV’98), 1998, pp. 232–248.

[46]  ——, “Mapping image restoration to a graph problem,” in IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP’99), 1999, pp. 189–193.

[47]  H. Ishikawa, “Exact optimization for Markov random fields with convex priors,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1333–1336, 2003.

[48]  V. Kolmogorov and R. Zabih, “What energy functions can be minimized via graph cuts?” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 147–159, 2004.

[49]  C.-H. Lee, D. Lee, and M. Kim, “Optimal task assignment in linear array networks,” IEEE Trans. Computers, vol. 41, no. 7, pp. 877–880, 1992.

[50]  E. Boros, P. L. Hammer, and X. Sun, “Network flows and minimization of quadratic pseudo-boolean functions, Tech. Rep. RUTCOR Research Report RRR 17-1991, May 1991.

[51]  E. Boros, P. L. Hammer, R. Sun, and G. Tavares, “A max-flow approach to improved lower bounds for quadratic unconstrained binary optimization (qubo),” Discrete Optimization, vol. 5, no. 2, pp. 501–529, 2008.

[52]  E. Boros, P. L. Hammer, and G. Tavares, “Preprocessing of unconstrained quadratic binary optimization, Tech. Rep. RUTCOR Research Report RRR 10-2006, April 2006.

[53]  P. L. Hammer, P. Hansen, and B. Simeone, “Roof duality, complementation and persistency in quadratic 0-1 optimization,” Math. Programming, vol. 28, pp. 121–155, 1984.

[54]  J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Francisco, 1998.

[55]  P. Felzenszwalb and D. Huttenlocher, “Efficient belief propagation for early vision,” Int. J. Comput. Vis., vol. 70, pp. 41–54, 2006.

[56]  T. Meltzer, C. Yanover, and Y. Weiss, “Globally optimal solutions for energy minimization in stereo vision using reweighted belief propagation,” in Proc. IEEE International Conference on Computer Vision (ICCV2005), 2005, pp. 428–435.

[57]  M. J. Wainwright, T. S. Jaakkola, and A. S. Willsky, “Tree-based reparameterization framework for analysis of sum-product and related algorithms,” IEEE Trans. Information Theory, vol. 49, no. 5, pp. 1120–1146., 2003.

[58]  V. Kolmogorov, “Convergent tree-reweighted message passing for energy minimization,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 10, pp. 1568–1583, 2006.

[59]  R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, and C. Rother, “A comparative study of energy minimization methods for Markov random fields with smoothness-based priors,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 7, pp. 1068–1080, 2008.

[60]  V. Kolmogorov and C. Rother, “Comparison of energy minimization algorithms for highly connected graphs,” in Proc. European Conference on Computer Vision (ECCV2006), vol. 2, 2006, pp. 1–15.

[61]  R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows: Theory, Algorithms and Applications. Prentice Hall, 1993.

[62]  W. J. Cook, W. H. Cunningham, W. R. Pulleyblank, and A. Schrijver, Combinatorial Optimization. John Wiley & Sons, New-York, 1998.

[63]  T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein, Introduction to Algorithms (Third Edition). MIT Press, 2009.

[64]  L. R. Ford and D. R. Fulkerson, “Maximal flow through a network,” Can. J. Math., vol. 8, pp. 399–404, 1956.

[65]  P. Elias, A. Feinstein, and C. E. Shannon, “A note on the maximum flow through a network,” IEEE Trans. Information Theory, vol. 2, no. 4, pp. 117–119, 1956.

[66]  L. Ford and D. Fulkerson, Flows in Networks. Princeton University Press, 1962.

[67]  F. Alizadeh and A. V. Goldberg, “Implementing the push-relabel method for the maximum flow problem on a connection machine,” DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol. 12, pp. 65–95, 1993.

[68]  B. V. Cherkassky and A. V. Goldberg, “On implementing push-relabel method for the maximum flow problem,” in Proc. 4th International Programming and Combinatorial Optimization Conference, 1995, pp. 157–171.

[69]  A. V. Goldberg and R. E. Tarjan, “A new approach to the maximum-flow problem,” J. ACM, vol. 35, pp. 921–940, 1988.

[70]  A. V. Goldberg, “Efficient graph algorithms for sequential and parallel computers,” Ph.D. dissertation, Massachussetts Institute of Technology, 1987.

[71]  Y. Boykov and V. Kolmogorov, “An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1124–1137, 2004.

[72]  E. A. Dinic, “Algorithm for solution of a problem of maximum flow in networks with power estimation,” Soviet Math. Dokl., vol. 11, pp. 1277–1280, 1970.

[73]  A. Delong and Y. Boykov, “A scalable graph-cut algorithm for N-D grids,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2008), 2008.

[74]  O. Juan and Y. Boykov, “Active graph cuts,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2006), vol. 1, 2006, pp. 1023–1029.

[75]  P. Kohli and P. H. S. Torr, “Effciently solving dynamic Markov random fields using graph cuts,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2005), vol. 2, 2005, pp. 922–929.

[76]  ——, “Measuring uncertainty in graph cut solutions–efficiently computing minmarginal energies using dynamic graph cuts,” in Proc. European Conference on Computer Vision (ECCV2006), vol. 2, 2006, pp. 20–43.

[77]  V. Kolmogorov and C. Rother, “Minimizing non-submodular functions with graph cuts — a review,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 9, no. 7, pp. 1274–1279, 2007.

[78]  C. Rother, V. Kolmogorov, V. Lempitsky, and M. Szummer, “Optimizing binary MRFs via extended roof duality,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2007), 2007.

[79]  D. Cremers and L. Grady, “Statistical priors for efficient combinatorial optimization via graph cuts,” in Proc. European Conference on Computer Vision (ECCV2006), vol. 3, 2006, pp. 263–274.

[80]  O. J. Woodford, P. H. S. Torr, I. D. Reid, and A. W. Fitzgibbon, “Global stereo reconstruction under second order smoothness priors,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2008), 2008.

[81]  S. Vicente, V. Kolmogorov, and C. Rother, “Graph cut based image segmentation with connectivity priors,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2008), 2008.

[82]  C. Rother, T. Minka, A. Blake, and V. Kolmogorov, “Cosegmentation of image pairs by histogram matching - incorporating a global constraint into MRFs,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2006), vol. 1, 2006, pp. 993–1000.

[83]  H. Ishikawa and D. Geiger, “Rethinking the prior model for stereo,” in Proc. European Conference on Computer Vision (ECCV2006), vol. 3, 2006, pp. 526–537.

[84]  G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher, “Texture synthesis via a noncausal nonparametric multiscale markov random field,” IEEE Trans. Image Processing, vol. 7, no. 6, pp. 925–931, 1998.

[85]  S. Roth and M. J. Black, “Fields of experts: A framework for learning image priors,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2005), vol. 2, 2005, pp. 860–867.

[86]  P. Kohli, M. P. Kumar, and P. H. S. Torr, “P3 & beyond: Move making algorithms for solving higher order functions,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 9, pp. 1645–1656, 2009.

[87]  P. Kohli, L. Ladicky, and P. H. S. Torr, “Robust higher order potentials for enforcing label consistency,” Int. J. Comput. Vis., vol. 82, no. 3, pp. 303–324, 2009.

[88]  X. Lan, S. Roth, D. P. Huttenlocher, and M. J. Black, “Efficient belief propagation with learned higher-order markov random fields,” in Proc. European Conference on Computer Vision (ECCV2006), vol. 2, 2006, pp. 269–282.

[89]  B. Potetz, “Efficient belief propagation for vision using linear constraint nodes,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2007), 2007.

[90]  C. Rother, P. Kohli, W. Feng, and J. Jia, “Minimizing sparse higher order energy functions of discrete variables,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2009), 2009, pp. 1382–1389.

[91]  N. Komodakis and N. Paragios, “Beyond pairwise energies: Efficient optimization for higher-order MRFs,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2009), 2009, pp. 2985–2992.

[92]  H. Ishikawa, “Higher-order clique reduction in binary graph cut,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2009), 2009, pp. 2993–3000.

[93]  ——, “Transformation of general binary MRF minimization to the first order case,” IEEE Trans. Pattern Analysis and Machine Intelligence, 2011.

[94]  D. Freedman and P. Drineas, “Energy minimization via graph cuts: Settling what is possible,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2005), vol. 2, 2005, pp. 939–946.

[95]  E. Boros and P. L. Hammer, “Pseudo-boolean optimization,” Discrete Appl. Math., vol. 123, pp. 155–225, November 2002.

[96]  P. L. Hammer and S. Rudeanu, Boolean Methods in Operations Research and Related Areas. Berlin, Heidelberg, New York: Springer-Verlag, 1968.

[97]  S. Ramalingam, P. Kohli, K. Alahari, and P. H. S. Torr, “Exact inference in multi-label CRFs with higher order cliques,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2008), 2008.

[98]  D. Schlesinger and B. Flach, “Transforming an arbitrary min-sum problem into a binary one,” Dresden University of Technology, Tech. Rep. TUD-FI06-01, 2006.

[99]  D. Schlesinger, “Exact solution of permuted submodular MinSum problems,” in Proc. Int. Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR2007), 2007, pp. 28–38.

[100]  J. Darbon, “Global optimization for first order Markov random fields with submodular priors,” in 12th Int. Workshop on Combinatorial Image Analysis, 2008.

[101]  T. Pock, T. Schoenemann, G. Graber, H. Bischof, and D. Cremers, “A convex formulation of continuous multi-label problems,” in Proc. European Conference on Computer Vision (ECCV2008), vol. 3, 2008, pp. 792–805.

[102]  C. Zach, M. Niethammer, and J.-M. Frahm, “Continuous maximal flows and Wulff shapes: Application to MRFs,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2009), 2009, pp. 1382–1389.

[103]  V. Kolmogorov and A. Shioura, “New algorithms for convex cost tension problem with application to computer vision,” Discrete Optim., vol. 6, no. 4, pp. 378–393, 2009.

[104]  A. Chambolle, “Total variation minimization and a class of binary MRF models,” in Proc. Int. Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR2005), 2005, pp. 136–152.

[105]  J. Darbon and M. Sigelle, “Image restoration with discrete constrained total variation part I: Fast and exact optimization,” J. Math. Imaging Vis., vol. 26, no. 3, 2006.

[106]  V. Kolmogorov and R. Zabih, “Computing visual correspondence with occlusions using graph cuts,” in Proc. IEEE International Conference on Computer Vision (ICCV2001), vol. 2, 2001, pp. 508–515.

[107]  P. Carr and R. Hartley, “Solving multilabel graph cut problems with multilabel swap,” in Digital Image Computing: Techniques and Applications, 2009.

[108]  M. Kumar and P. H. S. Torr, “Improved moves for truncated convex models,” in Proc. Neural Information Processing Systems (NIPS2008), 2008, pp. 889–896.

[109]  S. Gould, F. Amat, and D. Koller, “Alphabet soup: A framework for approximate energy minimization,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2009), 2009, pp. 903–910.

[110]  V. Lempitsky, C. Rother, and A. Blake, “Logcut — efficient graph cut optimization for markov random fields,” in Proc. IEEE International Conference on Computer Vision (ICCV2007), 2007.

[111]  V. Lempitsky, S. Roth, and C. Rother, “Fusionflow: Discrete-continuous optimization for optical flow estimation,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2008), 2008.

[112]  H. Ishikawa, “Higher-order gradient descent by fusion-move graph cut,” in Proc. IEEE International Conference on Computer Vision (ICCV2009), 2009, pp. 568–574.

[113]  O. Veksler, “Graph cut based optimization for MRFs with truncated convex priors,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2007), 2007.

[114]  ——, “Multi-label moves for MRFs with truncated convex priors,” in Proc. International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR2009), 2009, pp. 1–13.

[115]  N. Komodakis and G. Tziritas, “A new framework for approximate labeling via graph-cuts,” in Proc. IEEE International Conference on Computer Vision (ICCV2005), vol. 2, 2005, pp. 1018–1025.

[116]  ——, “Approximate labeling via graph-cuts based on linear programming,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 8, pp. 1436–1453, 2007.

[117]  N. Komodakis, G. Tziritas, and N. Paragios, “Fast, approximately optimal solutions for single and dynamic MRFs,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR2007), 2007.

[118]  Y. Boykov and O. Veksler, “Graph cuts in vision and graphics: Theories and applications,” in Handbook of Mathematical Models in Computer Vision, N. Paragios, Y. Chen, and O. Faugeras, Eds. Springer-Verlag, 2006, pp. 79–96.

[119]  A. Blake, P. Kohli, and C. Rother, Eds., Advances in Markov Random Fields for Vision and Image Processing. MIT Press, 2011.

..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.
Reset
18.223.172.132