An EM-MAP-HMRF Multi-sensor Image Fusion Algorithm Based on Non-homogeneous Class and Direction

HE Gui-qing; PENG Jin-ye; HAO Chong-yang

[1] WAI-KI C, MICHAEL K N. Markov chains: models, algorithms and applications［M］. Springer, 2006.

[2] DANG M, GOVAERT G. Spatial fuzzy clustering using EM and markov random fields［J］. System Research and Information Systems, 1998, (8): 183-202.

[3] GUO Xiao-wei, TIAN Zheng, LIU Bao-li. A fast algorithm for hidden markov tree training in image denoising［J］. Journal of Northwestern Polytechnical University, 2004, 22(4): 457-462.

[4] DERIN H, ELLIOTT H. Modeling and segmentation of noisy and textured images using gibbs random fields［C］. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1987, 9(1): 39-55.

[5] DENG H, CLAUSI D A. Unsupervised image segmentation using a simple MRF model with a new implementation scheme［J］. Pattern Recognition, 2004, 37(12): 2323-2335.

[6] XU X, LI D, SUN H. Multiscale SAR image segmentation using a double markov random field model［C］. Proceedings of IEEE 7th International Symposium on Signal Processing and its Applications, 2003, 1: 349-352.

[7] MANJUNATH B S, CHELLAPPA R. A markov random field model-based approach to image interpretation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(6): 606-615.

[8] YAMRON J P, CARP I, GILLICK L, et al. A hidden markov model approach to text segmentation and event tracking［C］. Proceedings of the IEEE International conference on Acoustics, Speech and Signal Processing, 1998, 1: 333-336.

[9] LI Xu-chao, ZHU Shan-an. A survey of the markov random field method for image segmentation［J］. Journal of Image and Graphics, 2007, 12(5): 789-798.

[10] ZHANG J, MODESTINO J W, LANGAN D A. Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation［C］. IEEE Transactions on Image Processing, 1994, 3(4): 404-420.

[11] ZHANG J. The mean field theory in EM procedures for markov random fields［J］. IEEE Transactions on Signal Processing, 1992, 40(10): 2570-2583.

[12] ZHANG Y, BRADY M, SMITH S. Segmentation of brain mr images through a hidden markov random field model and the expectation maximization algorithm［J］. IEEE Transactions on Medical Imaging, 2001, 20(1): 45-47.

[13] WANG J P. Stochastic relaxation on partitions with connected components and its application to image segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(6): 619-636.

[14] BIEMACKI C, CELEUX G, GOVAERT G. Assessing a mixture model for clustering with the integrated completed likelihood［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(7): 719-725.

[15] COMER M L, DELP E J. The EM/MPM algorithm for segmentation of textured images analysis and further experimental result［J］. IEEE Transactions on Image Processing, 2000, 9(10): 1731-1744.

[16] YU Peng, ZHANG Zhen-long, HOU Zhi-qun. Textured image segmentation based on gauss markov random field mixture model［J］. Acta Geodaetica Et Cartographica Sinica, 2006, 35(3): 224-228.

[17] LIU Xiao-yun, WANG Zhen-song, CHEN Wu-fan, et al. Remote sensing images hierarchical clustering using markov random field and generalized gaussian mixture models［J］. Journal of Remote Sensing, 2007, 11(6): 838-844.

[18] CHEN Yun-jie, WANG Shun-feng, WANG Li. Brian MR images segmentation based on anisotropic gibbs random field and gauss mixture model［J］. Journal of Computer-Aided Design and Computer Graphics, 2007, 19(12): 1550-1563.

[19] GEMAN S, GEMAN D. Stochastic relaxation, gibbs distribution and the bayesian restoration of images［J］. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1984, 6(6): 721-741.

[20] WON C S, DERIN H. Unsupervised segmentation of noisy and texture image using markov random fields［J］. Graphical Models and Image Processing, 1992, 54(4): 308-328.

[21] BLEKAS K, LIKASK A, GALATSANOS N P, et al. A spatially constrained mixture model for image segmentation［J］. IEEE Transactions on Neural Networks, 2005, 16(2): 494-498.

[22] ZHANG B B, ZHANG C S, YI X. Competitive EM algorithm for finite mixture models［J］. Pattern Recognition, 2004, 37(1): 131-144.

[23] ZHANG Y Y, BRADY M, SMITH S. Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm［J］. IEEE Transactions on Medical Image, 2001, 20(1): 45-57.

[24] NIKOU C, GALATSANOS N P, LIKAS A C. A class-adaptive spatially variant mixture model for image segmentation［J］. IEEE Transactions on Image Processing, 2007, 16(4): 1121-1130.

[25] ZHANG J, MODESTINO J W, LANGAN D A. Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation［J］. IEEE Transactions on Image Processing, 1994, 3(4): 404-420.

[26] LANKOACDE O, HAYAT M.M, SANTHANAM B. Speckle reduction of sar images using a physically based markov random field model and simulated annealing［C］. SPIE, 2005, 5808: 210-221.

[27] YAN He, PAN Ying-jun, WU Gang et al. Study on remote sensing image classification based on fractal theory and ridgelet nerual network［J］. Acta Photonica Sinica, 2007, 36(6): 342-345.

[28] ZHANG Cui, LI Su-dan, WANG Zheng-zhi. SAR image segmentation based on markov random field model［J］. Remote Sensing Technology and Application, 2001, 16(1): 66-6.

[29] BESAG J. On the statistical analysis of dirty pictures［J］. Journal of Royal Statistical Society(B), 1986, 48(3): 259-302.