On Mitigating Hard Clusters for Face Clustering

Chen, Yingjie; Zhong, Huasong; Chen, Chong; Shen, Chen; Huang, Jianqiang; Wang, Tao; Liang, Yun; Sun, Qianru

doi:10.1007/978-3-031-19775-8_31

Cited by 6 publications

(1 citation statement)

References 23 publications

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“…The KD-tree-based Euclidean clustering method can quickly search for adjacent points, and it also has a good effect on high-dimensional data due to KD-tree; however, this method usually uses a fixed cluster threshold in clustering. For different scenes, a great deal of debugging is required to obtain better segmentation results, and the use of fixed thresholds in large-scale scene applications is subject to classification errors [17,18].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Clustering for Point Cloud

Lin,

Kang,

et al. 2024

Sensors

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The point cloud segmentation method plays an important role in practical applications, such as remote sensing, mobile robots, and 3D modeling. However, there are still some limitations to the current point cloud data segmentation method when applied to large-scale scenes. Therefore, this paper proposes an adaptive clustering segmentation method. In this method, the threshold for clustering points within the point cloud is calculated using the characteristic parameters of adjacent points. After completing the preliminary segmentation of the point cloud, the segmentation results are further refined according to the standard deviation of the cluster points. Then, the cluster points whose number does not meet the conditions are further segmented, and, finally, scene point cloud data segmentation is realized. To test the superiority of this method, this study was based on point cloud data from a park in Guilin, Guangxi, China. The experimental results showed that this method is more practical and efficient than other methods, and it can effectively segment all ground objects and ground point cloud data in a scene. Compared with other segmentation methods that are easily affected by parameters, this method has strong robustness. In order to verify the universality of the method proposed in this paper, we test a public data set provided by ISPRS. The method achieves good segmentation results for multiple sample data, and it can distinguish noise points in a scene.

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Section: Introductionmentioning

confidence: 99%