针对小波域多尺度马尔科夫随机场模型(Markov random field,MRF)对信息利用不充分的特点,在模型中引入模糊理论,提出了一种新的小波域多尺度MRF模型。新模型定义了相应的模糊概率场,通过模糊概率场描述每个小波域各尺度上像素的类别隶属...针对小波域多尺度马尔科夫随机场模型(Markov random field,MRF)对信息利用不充分的特点,在模型中引入模糊理论,提出了一种新的小波域多尺度MRF模型。新模型定义了相应的模糊概率场,通过模糊概率场描述每个小波域各尺度上像素的类别隶属度;根据模糊概率场估计了对应的特征场模型参数,参数的估计考虑了同尺度所有位置的特征信息;根据特征场模型导出了对应的示性场模型,用其反映每个像素的类别能量。利用贝叶斯准则给出了3步交互迭代算法,获得了分割结果。展开更多
The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta...The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta (I<sub>1</sub>, I<sub>2</sub>, I<sub>3</sub>) is taken as the color model and different variants of Markov Random Field (MRF) models are proposed. In this regard, a Compound Markov Random Field (COMRF) model is porposed to take care of inter-color-plane and intra-color-plane interactions as well. In continuation to this model, a Constrained Compound Markov Random Field Model (CCOMRF) has been proposed to model the color images. The color image segmentation problem has been formulated in an unsupervised framework. The performance of the above proposed models has been compared with the standard MRF model and some of the state-of-the-art methods, and found to exhibit improved performance.展开更多
文摘针对小波域多尺度马尔科夫随机场模型(Markov random field,MRF)对信息利用不充分的特点,在模型中引入模糊理论,提出了一种新的小波域多尺度MRF模型。新模型定义了相应的模糊概率场,通过模糊概率场描述每个小波域各尺度上像素的类别隶属度;根据模糊概率场估计了对应的特征场模型参数,参数的估计考虑了同尺度所有位置的特征信息;根据特征场模型导出了对应的示性场模型,用其反映每个像素的类别能量。利用贝叶斯准则给出了3步交互迭代算法,获得了分割结果。
文摘The color image segmentation problem has two main issues to be solved. The proper choice of a color model and the choice of an appropriate image model are the key issues in color image segmentation. In this work, Ohta (I<sub>1</sub>, I<sub>2</sub>, I<sub>3</sub>) is taken as the color model and different variants of Markov Random Field (MRF) models are proposed. In this regard, a Compound Markov Random Field (COMRF) model is porposed to take care of inter-color-plane and intra-color-plane interactions as well. In continuation to this model, a Constrained Compound Markov Random Field Model (CCOMRF) has been proposed to model the color images. The color image segmentation problem has been formulated in an unsupervised framework. The performance of the above proposed models has been compared with the standard MRF model and some of the state-of-the-art methods, and found to exhibit improved performance.
