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Simplification Method Using K-NN Estimation and Fuzzy C-Means Clustering Algorithm

  • Abdelaaziz MahdaouiEmail author
  • A. Bouazi
  • A. Hsaini Marhraoui
  • E. H. Sbai
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 858)

Abstract

Acquisition systems, such as 3D scanners, are developing in resolution and accuracy. They give set of points that contain huge number of 3D data set. These acquisition systems are incapable to give the optimum number of points. For this reason, it is preferable to go through the simplification phase to optimize the number of 3D points constituting a point cloud and to consider only the relevant points. In this work, we will present a hybrid simplification method using two concepts. This technique is based on Shannon entropy and on the fuzzy c-means clustering algorithm.

Keywords

Simplification Clustering Fuzzy C-Means Entropy 3D point cloud 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Abdelaaziz Mahdaoui
    • 1
    Email author
  • A. Bouazi
    • 2
  • A. Hsaini Marhraoui
    • 2
  • E. H. Sbai
    • 2
  1. 1.Department of Physics, Faculty of ScienceMoulay Ismail UniversityMeknèsMorocco
  2. 2.Technology High SchoolMoulay Ismail UniversityMeknèsMorocco

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