Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Object-Based and Semantic Image Segmentation Using MRF | EURASIP Journal on Applied Signal Processing 2004 6 833-840 2004 Hindawi Publishing Corporation Object-Based and Semantic Image Segmentation Using MRF Feng Li Shanghai Zhongke Mobile Communication Research Center Shanghai Division Institute of Computing Technology Chinese Academy of Sciences Shanghai 201203 China Institute for Pattern Recognition Artificial Intelligence State Education Commission Laboratory for Image Processing Intelligence Control Huazhong University of Science and Technology Wuhan 430074 China Email life1972@ Jiaxiong Peng Institute for Pattern Recognition Artificial Intelligence State Education Commission Laboratory for Image Processing Intelligence Control Huazhong University of Science and Technology Wuhan 430074 China Email jiaxpeng@ Xiaojun Zheng Shanghai Zhongke Mobile Communication Research Center Shanghai Division Institute of Computing Technology Chinese Academy of Sciences Shanghai 201203 China Em ail fran k. zh eng@cm cr. cn Received 6 December 2002 Revised 3 September 2003 The problem that the Markov random field MRF model captures the structural as well as the stochastic textures for remote sensing image segmentation is considered. As the one-point clique namely the external field reflects the priori knowledge of the relative likelihood of the different region types which is often unknown one would like to consider only two-pairwise clique in the texture. To this end the MRF model cannot satisfactorily capture the structural component of the texture. In order to capture the structural texture in this paper a reference image is used as the external field. This reference image is obtained by Wold model decomposition which produces a purely random texture image and structural texture image from the original image. The structural component depicts the periodicity and directionality characteristics of the texture while the former describes the stochastic. Furthermore in order to achieve a good result of segmentation .
