GRF:高斯随机场
高斯随机场(Gaussian Random Field,常缩写为GRF)是一种在数学、物理学及工程学等综合领域广泛使用的随机过程模型。该术语之所以常被缩写,主要是为了方便在学术文献和工程应用中快捷书写与频繁引用。其核心特征在于,场中任意有限点集的联合分布均为高斯分布,这一性质使其在空间统计、图像分析和宇宙学建模等领域具有重要理论及应用价值。
Gaussian Random Field具体释义
Gaussian Random Field的英文发音
例句
- Suppose the image signal and the superposed noise are mutually independent homogeneous Gaussian random field with zero mean and both are ( twice ) differentiable, the analysis and computation problems of the local accuracy of mean absolute difference image matching systems are solved.
- 在假定图象信号和附加噪声是相互独立的零均值齐次高斯随机场(GRF)、且二次可微的条件下,解决了平均绝对差图象匹配系统的定位精度的分析和计算问题。
- In this paper, the image signal and noise are described by models of mean-square differentiable two-dimensional homogeneous Gaussian random field.
- 本文用均方可微的二维齐次高斯随机场(GRF)模型描述图象信号和噪声。
- A novel video motion object automatic segmentation algorithm based on Gaussian Markov random field is studied in this paper.
- 探讨了一种基于高斯马尔可夫随机场(GMRF)模型的运动目标自动分割算法。
- According to space-time characteristics of dynamic texture, we establish neighborhood system and energy function of MLL model in marking field, and describe observations with Gaussian Markov Random Field.
- 根据动态纹理的空时特性,确立标记场MLL模型中的邻域系统和能量函数,并采用高斯马尔可夫随机场描述观察场。
- Diffusion process is a Gaussian process, which makes it reasonable to model DTI image as Gaussian markov random field.
- 扩散过程是一个高斯马尔可夫过程,论文提出了用高斯马尔可夫随机场对DTI图像进行建模。
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