Comparison of K-Means and Fuzzy C-Means Algorithms on Simplification of 3D Point Cloud Based on Entropy Estimation

Abdelaaziz Mahdaoui; Aziz Bouazi; Abdallah Marhraoui Hsaini; El Hassan Sbai

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Comparison of K-Means and Fuzzy C-Means Algorithms on Simplification of 3D Point Cloud Based on Entropy Estimation

机译：基于熵估计的简化3D点云的K均值和模糊C均值算法比较

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In this article we will present a method simplifying 3D point clouds. This method is based on the Shannon entropy. This technique of simplification is a hybrid technique where we use the notion of clustering and iterative computation. In this paper, our main objective is to apply our method on different clouds of 3D points. In the clustering phase we will use two different algorithms; K-means and Fuzzy C-means. Then we will make a comparison between the results obtained.

机译：在本文中，我们将介绍一种简化3D点云的方法。该方法基于香农熵。这种简化技术是一种混合技术，其中我们使用聚类和迭代计算的概念。在本文中，我们的主要目标是将我们的方法应用于不同的3D点云。在聚类阶段，我们将使用两种不同的算法; K均值和模糊C均值。然后，我们将比较所获得的结果。

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《Advances in Science, Technology and Engineering Systems》 |2017年第5期|共7页
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Abdelaaziz Mahdaoui; Aziz Bouazi; Abdallah Marhraoui Hsaini; El Hassan Sbai;
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中图分类文化、科学、教育、体育;
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Comparison of K-Means and Fuzzy C-Means Algorithms on Simplification of 3D Point Cloud Based on Entropy Estimation

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