Improved SFS 3D measurement based on neural network

被引：0
|
作者：
Song, LM
Wang, D
Su, H
Li, XX
Liu, XY
机构：
[1] Information Engineering College, South West University of Science and Technology, SiChuan MianYang, 621010, China
来源：
JOURNAL OF OPTOELECTRONICS AND ADVANCED MATERIALS | 2006年 / 8卷 / 01期
关键词：
SFS; neural network; genetic algorithm; synthetic vase;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Non-contact 3D surface measurement is an important problem for modern industry. Shape from shading (SFS) is a convenient method because it can recover the 3D shape only from one image. But the conventional SFS research has a lot of restriction, such as Lambertian illumination model. If the object isn't under this model, the precision will decrease quickly. We proposed an improved SFS based on neural network combining with genetic algorithm. This proposed SFS doesn't care much about the illumination and increased the precision. It has been used in the 3D reconstruction of synthetic vase, and also used in the online 3D measurement of work piece.
引用
收藏
页码：285 / 290
页数：6
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