Based on an extended Gauss-Markov model where the unknown parameters has the prior normal distribution, this paper derives the maximum posterior estimate formulas of the parameters which are proved to be unbiased,effi...Based on an extended Gauss-Markov model where the unknown parameters has the prior normal distribution, this paper derives the maximum posterior estimate formulas of the parameters which are proved to be unbiased,efficient, and of variance of unit weight which is biased. Finally, the marginal maximum posterior estimate formula of the variance with unbiased and efficient , properties is derived.展开更多
为了在模型参数先验分布知识未知情况下实现基于区域和统计的图像分割,并同时获取更加精确的模型参数,提出了一种结合Voronoi划分技术、最大期望值(Expectation Maximization,EM)和最大边缘概率(Maximizationof the Posterior Marginal,...为了在模型参数先验分布知识未知情况下实现基于区域和统计的图像分割,并同时获取更加精确的模型参数,提出了一种结合Voronoi划分技术、最大期望值(Expectation Maximization,EM)和最大边缘概率(Maximizationof the Posterior Marginal,MPM)算法的图像分割方法。该方法利用Voronoi划分技术将图像域划分为若干子区域,待分割图像中的同质区域可以由一组子区域拟合而成,并假定各同质区域内像素强度服从同一独立的正态分布,从而建立图像模型,然后结合EM/MPM算法进行图像分割和模型参数估计,其中,MPM算法用于实现面向同质区域的图像分割,EM算法用于估计图像模型参数。为了验证提出的图像分割方法,分别对合成图像和真实图像进行了分割实验,并和传统的基于像素的MRF分割结果进行对比,测试结果的定性和定量分析表明了该方法的有效性和准确性。展开更多
The noniterative algorithm of multiscale MRF has much lower computing complexity and better result thanits iterative counterpart of noncausal MRF model, since it has causality property between scales, and such causali...The noniterative algorithm of multiscale MRF has much lower computing complexity and better result thanits iterative counterpart of noncausal MRF model, since it has causality property between scales, and such causality isconsistent with the character of images. Maximizer of the posterior marginals(MPM)algorithm of multiscale MRFmodel is presented for only one image can be obtained in image segmentation. EM algorithm for parameter estimate isalso given. Experiments demonstrate that comparing with iterative ones, the proposed algorithms have the character-istics of greatly reduced computing time and better segmentation results. This is more notable for large images.展开更多
文摘Based on an extended Gauss-Markov model where the unknown parameters has the prior normal distribution, this paper derives the maximum posterior estimate formulas of the parameters which are proved to be unbiased,efficient, and of variance of unit weight which is biased. Finally, the marginal maximum posterior estimate formula of the variance with unbiased and efficient , properties is derived.
文摘为了在模型参数先验分布知识未知情况下实现基于区域和统计的图像分割,并同时获取更加精确的模型参数,提出了一种结合Voronoi划分技术、最大期望值(Expectation Maximization,EM)和最大边缘概率(Maximizationof the Posterior Marginal,MPM)算法的图像分割方法。该方法利用Voronoi划分技术将图像域划分为若干子区域,待分割图像中的同质区域可以由一组子区域拟合而成,并假定各同质区域内像素强度服从同一独立的正态分布,从而建立图像模型,然后结合EM/MPM算法进行图像分割和模型参数估计,其中,MPM算法用于实现面向同质区域的图像分割,EM算法用于估计图像模型参数。为了验证提出的图像分割方法,分别对合成图像和真实图像进行了分割实验,并和传统的基于像素的MRF分割结果进行对比,测试结果的定性和定量分析表明了该方法的有效性和准确性。
文摘The noniterative algorithm of multiscale MRF has much lower computing complexity and better result thanits iterative counterpart of noncausal MRF model, since it has causality property between scales, and such causality isconsistent with the character of images. Maximizer of the posterior marginals(MPM)algorithm of multiscale MRFmodel is presented for only one image can be obtained in image segmentation. EM algorithm for parameter estimate isalso given. Experiments demonstrate that comparing with iterative ones, the proposed algorithms have the character-istics of greatly reduced computing time and better segmentation results. This is more notable for large images.
