Earticle

초록

영어

In order to remove redundant data and resolve conflicts in point clouds, we proposed fuzzy clustering reduction strategy in this paper. Original point clouds are decreased before computing other pretreatments. The proposed method involves three processes: reduction of the original data using fuzzy clustering while the point clouds are divided into sub-domains using octree structure, generation of the sub-surface that is fitted the sub-surface by implicit function in each sub-domain, the normal alignment that are computed normal of sub-surface and inference the global normal of surface using iteratively propagate algorithm. The method is suitable to reduce mass point clouds to reconstruct surface that can keep the property of surface. The experimental results show that the model with less sharp feature is more effective than complex model to reduce point clouds by fuzzy clustering.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Reduction Strategy of Point Clouds using Fuzzy c-mean Clustering
  3.1. Reduce Point Clouds Framework during Partition of Unity
  3.2. Parameters of the FCM Algorithm
 4. Implicit Function Locally Fitting Framework
  4.1. Implicit Model for Point Clouds
  4.2. Stitch the Local Surface Framework by Advancing Front Algorithm
 5. Results
 6. Conclusions
 Acknowledgment
 References

키워드

저자

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

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605