Surface Reconstruction Method Based on GRNN

摘要：

A surface reconstruction method based on generalized regression neural net (GRNN) is presented. Firstly in order to eliminate noise points, some sample points are chosen from the measured data to construct GRNN. Thus a neural net to approximate the measured points is obtained. And the distribution probability of the approximation error is figured out. In result, the noise points are eliminated when their error probability is less than the threshold value. Then the boundary points are extracted. Lastly the surface model is reconstructed by use of the measured points from which noise points have been eliminated. The reconstruction error is analyzed. The results indicate that the reconstruction precision can satisfy the demands of engineering application.

作者: Wu Fuzhong

作者单位: School of Engineering, Shaoxing College of Arts and Sciences, Shaoxing, China

会议类型: 国际会议

会议名称: International Conference on Intelligent Computation Technology and Automation(2008 智能计算技术与自动化国际会议 ICICTA 2008)

会议地点: 长沙

会议语种:英文

页码: 262-265

在线出版日期: 2008-10-20（万方平台首次上网日期，不代表论文的发表时间）