In the part of "3d face registration", feature points based pie-registration method is implemented with the help of feature points location method.
三维人脸配准方面，通过和标志点定位结合，实现了基于标志点的粗配准方法。
The known error bound of location deviation is used to determine the minimum matching merit for two point pairs so that the effect of spurious feature points can be reduced.
基本思想是利用已知的最大定位误差，确定两点对的最小匹配度量，从而减小虚假特征点对匹配结果的影响。
The initial shape selection, location and the local feature description around the feature points are discussed deeply.
深入研究了该算法初始形状选择、定位与特征点局部特征描述两个关键问题。
Experiment results indicate that this approach can automatically locate the feature points, has obvious practicability in the way of feature location.
实验结果表明，该方法能自动定位特征点，在特征定位方面有很强的实用性。
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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