In this thesis, the specific parameter estimating by Kalman filter method was investigated under actual construction.
本文着重研究了在实际施工中，利用卡尔曼滤波法进行参数估计的具体做法。
The application of an adaptive noise canceller to parameter estimating is restricted for its unsatisfactory performance in the condition of high SNR input.
自适应噪声抵消器在高信噪比条件下往往难以取得良好的处理效果，从而使其在参数估计方面的应用受到了很大的限制。
To solve this problem, this paper provides a approximate parameter estimating method by using of the Maximum Likelihood method on the probability paper, and a calculation example is offered.
为解决这一问题，本文将极大似然法引入其概率图，提出了一种近似的参数估计方法并给出了算例。
Aimed at their defects in parameter estimation, an enhanced auto adaptive edge detection method for parameter estimating is presented firstly.
针对传统参数估计方法的缺陷，提出了增强的自适应边缘检测参数估计方法。
We realize an algorithm based on minimum features for rapid face modeling from video, by tracking feature points, calibrating exterior parameter, estimating 3D location of feature points.
通过跟踪视频中的特征点，标定相机外参，进而估计特征点的3D位置，实现了基于一段视频中小特征点集的人脸建模算法。
We realize an algorithm based on minimum features for rapid face modeling from video, by tracking feature points, calibrating exterior parameter, estimating 3D location of feature points.
通过跟踪视频中的特征点，标定相机外参，进而估计特征点的3D位置，实现了基于一段视频中小特征点集的人脸建模算法。
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