Supervised image segmentation method based on tree-structured Markov random field in wavelet domain

LIU Guo-Ying; WANG Ai-Min; CHEN Rong-Yuan; QIN Qian-Qing

[1] Poggi G, Ragozini R P. Image segmentation by tree-structured Markov random fields［J］. Signal Processing Letters,1999,6(7): 155—157.

[2] D’Elia C, Poggi G, Scarpa G. A tree-structured Markov random field model for Bayesian image segmentation［J］. IEEE Transactions on Image Processing,2003, 12(10): 1259—1273.

[3] Poggi G, Scarpa G, Zerubia J B. Supervised segmentation of remote sensing images based on a tree-structured MRF model［J］. IEEE Transactions on Geoscience and Remote Sensing,2005, 43(8): 1901—1911.

[4] Poggi G, Scarpa G, Z. J. Segmentation of remote-sensing images by supervised TS-MRF: IEEE International Conference on Image Processing, 2004［C］. Singapore, 2000(3): 1867—1870.

[5] Krishnamachari S, Chellappa R. Multiresolution Gauss-Markov random field models for texture segmentation［J］. IEEE Transactions on Image Processing,1997, 6(2): 251—267.

[7] Myunghee Jung, Eui-Jung Yun, Kim C S. Multiresolution Approach for Texture Segmentation Using MRF Models: Geoscience and Remote Sensing Symposium, 2005［C］. Seoul, Korea, 2005: 3971-3974.

[8] Daubechies, I. Ten lectures on wavelets. 1992: SIAM.