Detecting dominant points is an important subject in pattern recognition and computer vision. Corner detection and polygonal approximation are two major approaches for dominant point detection.
景物的特征点抽取是模式识别及计算机视觉中的一个重要问题，已出现的多种检测特征点的方法中主要有角检测法和多边形逼近法。
We addressed the problems and solutions of converting a measured point cloud into a realistic 3D polygonal model that can satisfy high modelling and visualization demands.
将三维扫描仪测量得到的点云转换成一个实际的3D（三维）多边形模型，以满足高级建模和可视化的需要。 为此提到了转换过程中出现的所有问题和解决方法。
A general algorithm of triangulating arbitrary planar polygonal domain and scattered point set is presented.
提出了一个适用于任意平面多边形区域及散乱点集的通用三角化算法。
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