Efficient MRF deformation model for non-rigid image matching

Shekhovtsov, 2007, Efficient MRF deformation model for non-rigid image matching, 1 R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappen, C. Rother, A comparative study of energy minimization methods for Markov random fields, in: European Conference on Computer Vision, vol. 2, 2006, pp. II: 16–29. Jojic, 2005, A comparison of algorithms for inference and learning in probabilistic graphical models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 1392, 10.1109/TPAMI.2005.169 Konrad, 1992, Bayesian estimation of motion vector fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 215, 10.1109/34.161350 Heitz, 1993, Multimodal estimation of discontinuous optical flow using Markov random fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, 15, 1217, 10.1109/34.250841 J. Zhang, J. Hanauer, The mean field theory for image motion estimation, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, 1993, pp. 197–200. Roy, 2000, MRF solutions for probabilistic optical flow formulations, 7053 Boykov, 2001, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 1222, 10.1109/34.969114 Kumar, 2005, Learning layered motion segmentation of video, 33 Jiang, 2007, Matching by linear programming and successive convexification, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 959, 10.1109/TPAMI.2007.1048 B. Glocker, N. Komodakis, N. Paragios, G. Tziritas, N. Navab, Inter and intra-modal deformable registration: continuous deformations meet efficient optimal linear programming, in: Information Processing in Medical Imaging, 2007, pp. 408–420. B. Glocker, N. Komodakis, G. Tziritas, N. Navab, N. Paragios, Dense image registration through MRFs and efficient linear programming, Medical Image Analysis, in press. N. Komodakis, G. Tziritas, A new framework for approximate labeling via graph cuts, in: Proceedings of the International Conference on Computer Vision, 2005, pp. 1018–1025. Felzenszwalb, 2004, Efficient belief propagation for early vision, vol. 1, 261 I. Kovtun, Image segmentation based on sufficient conditions of optimality in NP-complete classes of structural labelling problem, Ph.D. thesis, IRTC ITS National Academy of Sciences, Ukraine, in Ukrainian, 2004. Roche, 2000, Unifying maximum likelihood approaches in medical image registration, International Journal of Imaging Systems and Technology, 11, 71, 10.1002/(SICI)1098-1098(2000)11:1<71::AID-IMA8>3.0.CO;2-5 Wainwright, 2005, MAP estimation via agreement on (hyper)trees: message-passing and linear-programming approaches, IEEE Transactions on Information Theory, 51, 3697, 10.1109/TIT.2005.856938 Kolmogorov, 2006, Convergent tree-reweighted message passing for energy minimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 1568, 10.1109/TPAMI.2006.200 Werner, 2007, A linear programming approach to max-sum problem: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 1165, 10.1109/TPAMI.2007.1036 Chekuri, 2005, A linear programming formulation and approximation algorithms for the metric labeling problem, SIAM Journal on Discrete Mathematics, 18, 608, 10.1137/S0895480101396937 Koval, 1976, Two-dimensional programming in image analysis problems, Automatics and Telemechanics, 2, 149 Schlesinger, 1976, Syntactic analysis of two-dimensional visual signals in noisy conditions, Kibernetika, 4, 113 Felzenszwalb, 2003, Fast algorithms for large-state-space HMMs with applications to web usage analysis A. Shekhovtsov, I. Kovtun, V. Hlaváč, Efficient MRF deformation model for image matching, Research Report CTU-CMP-2006-08, Center for Machine Perception, Czech Technical University, October 2006. Rueckert, 1999, Non-rigid registration using free-form deformations: application to breast MR images, IEEE Transactions on Medical Imaging, 18, 712, 10.1109/42.796284 J.A. Schnabel, D. Rueckert, M. Quist, J.M. Blackall, A.D. Castellano-Smith, T. Hartkens, G.P. Penney, W.A. Hall, H. Liu, C.L. Truwit, F.A. Gerritsen, D.L.G. Hill, D.J. Hawkes, A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, 2001, pp. 573–581. J. Winn, N. Jojic, LOCUS: Learning object classes with unsupervised segmentation, in: Proceedings of the International Conference on Computer Vision, vol. 1, 2005, pp. 756–763.