A novel motion detection method based on robust statistics was proposed.
提出了一种基于鲁棒统计量的运动检测方法。
In this paper, we introduce the concepts of robust statistics and the robust method of M-estimation.
本文介绍了稳健统计的概念和M估计的稳健方法。
Through robust statistics, put forward a robust estimation algorithm to estimate measuring results and error.
通过鲁棒统计，提出了一个鲁棒估计算法处理测量结果和误差。
In this paper, the robust statistics base is introduced, and the properties of robust statistics are analysed.
介绍稳健统计基础，分析稳健统计性质，并将稳健统计应用于实验室不确定度计算和实验室能力验证。
This parameter estimation method was introduced by combination of distribution hypothesis with robust statistics.
方法：结合分布假设和稳健统计给出了参数估计方法。
Methods This parameter estimation method is introduced by being combined with distribution hypothesis and robust statistics.
方法结合分布假设和稳健统计给出了参数估计方法。
Based on robust statistics, robust approaches are represented for these fuzzy regression models, and are illustrated with several examples in order to show the robustness of the suggested approaches.
就这两类回归模型，从稳健统计的角度提出相应的稳健方法，并通过例子与现有的方法进行比较，说明所提方法的稳健性。
Robust statistics provides methods which give reliable results even when the data deviate to a certain extent from statistical assumptions.
稳键统计学提供了一种当数据一定程度上偏离统计假设时也能给出可靠结果的统计方法。
Robust statistics is introduced to enhance the robustness of Gaussian filtering. Several selected robust weight functions are adopted and compared.
引入稳健统计学的思想，对高斯滤波进行了稳健处理，并比较了几种稳健估计权函数对高斯滤波性能的改进。
Robust statistics is introduced to enhance the robustness of Gaussian filtering. Several selected robust weight functions are adopted and compared.
引入稳健统计学的思想，对高斯滤波进行了稳健处理，并比较了几种稳健估计权函数对高斯滤波性能的改进。
应用推荐