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- Markov random process Markov随机过程模型
- Switzer(1965) has demonstrated the existence of a random process in the plane with Markov property. 曾经论证了一个具有马尔柯夫性质的平面随机过程的存在。
- In order to determine the a priori distribution of these submodels, a half plane Markov random field model is utilized to describe the distribution of the line process. 半平面MRF(Markov random field)模型描述线过程的分布,以确定各个子模型的先验概率。
- Markov random field in image analysis, written by Li Qing, an absolute classic! 马尔克夫随机场在图像分析中的应用，李子青写的，绝对经典！
- Markov random processes 马尔柯夫随机过程
- The computations for Average Run Length of CUSUM control chart in Fuzzy enviornment are discussed by means of extending traditional Markov chain methods to Fuzzy data valued random process (production process) control chart. 探讨累计和控制图在模糊环境下平均游程长度的计算方法 ,将普通马尔科夫链方法推广到取值于Fuzzy数据的随机过程 (生产过程 )控制图
- Firstly, a rough motion template was obtained by motion detection based on Markov random field model and through post-processing. 该算法首先利用马尔可夫随机场模型的运动检测算法，得到运动目标的初始模板。
- In this paper, a novel method of moving object segmentation based on spatiotemporal Markov Random Field(MRE) is proposed. 该文提出一种新的基于时空马尔可夫随机场的运动目标分割技术。
- This paper uses an algorithm of motion segmentation combined motion estimation with Markov Random Field（MRF）. 采用一种将运动估计方法与马尔可夫随机场（MRF）模型相结合的运动分割方法。
- Firstly,the accurate contour of the target in SAR imagery is extracted after the image segmentation based on Markov Random Field(MRF)model. 利用基于马尔可夫随机场(MRF)的图像分割提取准确的目标成像轮廓。
- A method extracting moving object was proposed by utilizing the Markov random field (MRF) and active contour model in video sequences. 针对视频序列图像中的运动目标分割，提出了将马尔可夫随机场模型和活动轮廓模型相结合的运动目标分割算法。
- Firstly,a maximum a posteriori framework is created according to conditional random field model and Markov random field model. 首先根据条件随机场模型和马尔可夫随机场模型建立了一个最大后验概率框架。
- As the noise is random process, which can only be estimated based on statistical models based on. 由于噪声也是随机过程，因此这种估计只能建立在统计模型基础上。
- On the basis of Markov Random Field(MRF) theory, Markov Random Field texture model was presented to analyse workpiece surface texture images. 基于马尔可夫随机场理论,建立了工件表面纹理图像的马尔可夫随机场纹理模型,并对工件表面纹理图像的特点进行了分析。
- The Time Variation of Waveform, Spectrum, and Probability Distribution of Random Process of Noises and Signals. 随机过程杂讯及讯号之波形、频谱及机率分布之时变特性。
- The traditional Markov Random Field (MRF) used in image segmentation have several disadvantages, such as too much time and no convergence. 摘要传统的马尔可夫随机场(MRF)图像分割在优化求解的过程中存在运算量较大，运算时间过长，算法不收敛等问题。
- Based on the features of existing structure, the nonstationary random process model of resistance for existing structure is proposed. 根据服役结构的特点，提出了服役结构抗力的非平稳随机过程模型。
- Then the SAR image was devided by useing Markov Random Field(MRF) model.Experiment results indicate that the method can improve the quality of image segmentation. 实验结果表明,该方法改善了SAR图像分割的质量,有效地改善了MRF图像分割算法的方向灵敏性。
- The distribution of inhomogeneous thermo-emf along the copper-iron thermoelectrode is described as a stationary ergodic random process. 不均匀热电势沿铜铁合金热电极长度上的分布可描述为各态历经的平稳的随机过程。
- The application of a Gaussian Markov Random Fields (GMRF) based Maximum A Posteriori Probability (MAP) estimation for image Gaussian noise filter was presented. 摘要提出了基于高斯马尔可夫随机场(GMRF)的最大后验概率(MAP)估计在图像高斯噪声滤波中的应用方法。
