摘要
从三维点云数据中提取实物的边界特征点,在以计算机视觉为基础的数字化曲面重建过程中有非常重要的意义。为提高精度,重建之前,必须对通过各种方法获得的大量原始散乱数据进行除噪及精简处理。基于此,提出了一种基于小波变换的激光测量扫描边界特征点提取算法,我们通过严格的理论推导,构造了一种类似mexh小波的小波基来对两种边界特征点进行检测。多次实验结果显示:该算法有效地避免了噪声和冗余数据的干扰,较精确地定位到了边界特征点,通过重建原始数据,准确地提取了三维实体的外型轮廓,同时也为实现冗余数据的精简提供了一种新的思想。
It is meaningful to abstract edge feature point from a large amount of 3D point cloud in the reconstruction of curved surface. But the 3D data we get must be proceeded firstly in order to avoid the distortion and deviation during the course of reconstruction. According to this, a new approach based on wavelet edge detection is presented. We select a kind of wavelet similar to mexh as a tool exclusively to detect these two kinds of edge feature points ,the following experiments show that：the edge feature points are located accurately ignoring the disturbance of noise and redundancy. Comparing with former data proceeding methods, this method is more accurate and overcomes the influence of noise simultaneously.
出处
《计算机应用与软件》
CSCD
北大核心
2006年第9期8-10,42,共4页
Computer Applications and Software
基金
国家863高技术研究发展计划基金资助(编号:2001AA421160)
