An improved supervoxel 3D region growing method based on PET/CT multimodal data for segmentation and reconstruction of GGNs

Dong, Yunyun; Yang, Wenkai; Wang, Jiawen; Zhao, Zijuan; Sanhu, Wang; Cui, Qiang; Qiang, Yong

doi:10.1007/s11042-019-08250-4

Cited by 5 publications

(1 citation statement)

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“…The superpixel method is widely used in 2D image processing to effectively reduce the computation consumption of the subsequent process [12]. In recent years, the efficient supervoxel method has been introduced to 3D semantic segmentation [13][14][15]. Supervoxels were applied in a convolution operation (SVConv) by Huang, Ma et al to effectively accomplish online 3D semantic segmentation [16].…”

Section: Introductionmentioning

confidence: 99%

An Improved Supervoxel Clustering Algorithm of 3D Point Clouds for the Localization of Industrial Robots

Xie

Liang²,

Tao

et al. 2022

Electronics

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Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time.

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