Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for S...Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.展开更多
This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several...This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori （MAP） estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.展开更多
基于四叉树的分层马尔可夫随机场(Markov random field,MRF)模型在层间存在因果性,不需要像非因果马尔可夫随机场模型那样的迭代算法,但是传统的分层MRF模型常常导致分割结果具有块状现象和非连续边缘．本文提出一种新的基于区域确定的...基于四叉树的分层马尔可夫随机场(Markov random field,MRF)模型在层间存在因果性,不需要像非因果马尔可夫随机场模型那样的迭代算法,但是传统的分层MRF模型常常导致分割结果具有块状现象和非连续边缘．本文提出一种新的基于区域确定的半树分层MRF算法,并推导出它的最大后验边缘概率(Maximizer of the posteriori marginal,MPM)算法．在流域算法过分割结果的基础上,该模型将层间的点概率转换为区域概率,采用区域概率实现各层图像分割．从SAR图像的监督分割实验结果来看,本文提出的模型较好地克服了基于像素分层模型和单分辨率MRF模型带米的块现象和非连续边界,因而具有更好的分割结果．展开更多
基金supported by the Specialized Research Found for the Doctoral Program of Higher Education (20070699013)the Natural Science Foundation of Shaanxi Province (2006F05)the Aeronautical Science Foundation (05I53076)
文摘Traditional image segmentation methods based on MRF converge slowly and require pre-defined weight. These disadvantages are addressed, and a fast segmentation approach based on simple Markov random field （MRF） for SAR image is proposed. The approach is firstly used to perform coarse segmentation in blocks. Then the image is modeled with simple MRF and adaptive variable weighting forms are applied in homogeneous and heterogeneous regions. As a result, the convergent speed is accelerated while the segmentation results in homogeneous regions and boarders are improved. Simulations with synthetic and real SAR images demonstrate the effectiveness of the proposed approach.
基金This work was supported by the National Natural Science Foundation of China under Grant No. 600330107 Zhejiang Provincial Natural Science Foundation of China under Grant No, Y105324 and Planned Program of Science and Technology Department of Zhejiang Province, China （Grant No. 2006C31065）,
文摘This paper proposes a Maxkov Random Field （MRF） model-based approach to natural image matting with complex scenes. After the trimap for matting is given manually, the unknown region is roughly segmented into several joint sub-regions. In each sub-region, we partition the colors of neighboring background or foreground pixels into several clusters in RGB color space and assign matting label to each unknown pixel. All the labels are modelled as an MRF and the matting problem is then formulated as a maximum a posteriori （MAP） estimation problem. Simulated annealing is used to find the optimal MAP estimation. The better results can be obtained under the same user-interactions when images are complex. Results of natural image matting experiments performed on complex images using this approach are shown and compared in this paper.
文摘基于四叉树的分层马尔可夫随机场(Markov random field,MRF)模型在层间存在因果性,不需要像非因果马尔可夫随机场模型那样的迭代算法,但是传统的分层MRF模型常常导致分割结果具有块状现象和非连续边缘．本文提出一种新的基于区域确定的半树分层MRF算法,并推导出它的最大后验边缘概率(Maximizer of the posteriori marginal,MPM)算法．在流域算法过分割结果的基础上,该模型将层间的点概率转换为区域概率,采用区域概率实现各层图像分割．从SAR图像的监督分割实验结果来看,本文提出的模型较好地克服了基于像素分层模型和单分辨率MRF模型带米的块现象和非连续边界,因而具有更好的分割结果．
