Earticle

초록

영어

This paper presents a self-organized fuzzy neural network (SOFNN) surface reconstruction algorithm suitable for point clouds without normal. It overcomes the defect of traditional Delaunay triangulation which is difficult to reconstruct point clouds with noises and implicit function which is limited to the number of point clouds and point clouds are required very strict. The SOFNN is based on the fuzzy clustering method optimizing training data before learning fuzzy rules, in order to remove noise data and resolve conflicts in data. The approach not only reduce computational burden of neural network, but also make it easy to fit the surface for point clouds without normal and suitable for mass point clouds. The feature of the SOFNN has dynamic self-organized structure, fast learning speed and flexibility in learning. The experiment results show that is very fine.

목차

Abstract
 1. Introduction
 2. Related Work
 3. SOFNN Structure and Learning Algorithm
  3.1. Off-line Learning
  3.2. On-line learning
 4. Implicit Surface Reconstruction
 5. Experimental Results
 6. Conclusions
 Acknowledgment
 References

키워드

저자

• Liu Yan-ju [ Computer Center, Qiqihar University, Qiqihar, China ]
• Liu Yan-zhong [ College of Computer and Control Engineering, Qiqihar University, Qiqihar, China ]
• Tao Bai-rui [ Computer Center, Qiqihar University, Qiqihar, China ]
• Jiang Jin-gang [ College of Mechanical & Power Engineering, Harbin University of Science and Technology, Harbin, China ]
• Zhang Hong-lie [ College of Computer and Control Engineering, Qiqihar University, Qiqihar, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605